From 5cb32c89605489f1611bd76e248c046ebbcf258b Mon Sep 17 00:00:00 2001 From: Jouni Helske Date: Fri, 9 Jul 2021 16:00:54 +0300 Subject: [PATCH 001/180] add link to helske-universe --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bf3047d8..80c7810e 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,13 @@ and linear-Gaussian state dynamics, as well as general non-linear Gaussian model For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf). -You can install the latest development version by using the devtools package: +You can install the latest development version from R-universe with + +```R +install.packages("bssm", repos = "https://helske.r-universe.dev") +``` + +Or by using the devtools package: ```R install.packages("devtools") @@ -25,7 +31,13 @@ devtools::install_github("helske/bssm") Recent changes (For all changes, see NEWS file.) ========================================================================== -bssm 1.1.3-2 (Release date: 2021-02-23) +bssm 1.1.4 (Release date: 2021-04-13) +============== + * Better documentation for SV model, and changed ordering of arguments to emphasise the + recommended parameterization. + * Fixed predict method for SV model. + +bssm 1.1.3-2 (Release date: 2021-02-24) ============== * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. * Added pandoc version >= 1.12.3 to system requirements. 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z7jTPY)cMDG zEN!eVbmc(!qFwD?g`xuVphwTk15+d zr_L-5#>U~MYr#Gz|=O>KMqhaHN-@F+YmaK@+m{C$bY z@V&V9jl&IJrhOXj?~P3LyeZddy$8=iPp|A66FL?DTixEE8fQ;murIGsSi9>zy)jnb z+3NdrMy}54i(%qK2;&%P`7PVyElaQQ0rWBOi6I40IST9;lZ<*CQ{$CEZ0*>cL zOQ*a)EJU%t%q@^}iPNroVR$PyT6s#&kpFAvbC+|Wx33cAyD+wIEx+htXFtW{7l!G+XgMamAU$hl;Yx&};RPn0MMT3JgNx+< fB=x_$u7{Jv*>1=HSD)?tb!u#2rvFm+!p;8yVGj^F literal 0 HcmV?d00001 From 98c2a7cc48870d05f9450d48c832bdbabd2a24de Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 9 Jul 2021 23:20:20 +0300 Subject: [PATCH 003/180] actual Rmd... --- slides_UseR2021/JouniHelske_bssm.Rmd | 399 +++++++++++++++++++++++++++ 1 file changed, 399 insertions(+) create mode 100644 slides_UseR2021/JouniHelske_bssm.Rmd diff --git a/slides_UseR2021/JouniHelske_bssm.Rmd b/slides_UseR2021/JouniHelske_bssm.Rmd new file mode 100644 index 00000000..3b22bbcc --- /dev/null +++ b/slides_UseR2021/JouniHelske_bssm.Rmd @@ -0,0 +1,399 @@ +--- +title: "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R" +author: "Jouni Helske (joint work with Matti Vihola)" +institute: "University of Jyväskylä, Finland" +date: "9/7/2021" +output: + xaringan::moon_reader: + lib_dir: libs + css: ["useR", "useR-fonts"] + nature: + highlightStyle: github + highlightLines: true + countIncrementalSlides: false +--- + +```{r setup, include=FALSE} +options(htmltools.dir.version = FALSE) +``` + +```{r, echo = FALSE} +suppressPackageStartupMessages(library("bssm")) +suppressPackageStartupMessages(library("dplyr")) +suppressPackageStartupMessages(library("ggplot2")) +``` + + +## What are state space models? + +- The bssm package (Helske, Vihola, 2021) allows fully Bayesian inference of state space models (SSMs) + - E.g. structural time series, ARIMA models, generalized linear models with time-varying coefficients, cubic splines, SDEs, ... +- In general we have + - Observations $y=(y_1,\ldots,y_T)$ with conditional distribution $g_t(y_t | \alpha_t)$ + - Latent *states* $\alpha=(\alpha_1,\ldots,\alpha_T)$ with a transition distribution $p_t(\alpha_{t+1} | \alpha_t)$ + - Both observations $y_t$ and states $\alpha_t$ can be multivariate +- Both distributions can depend on some hyperparameters $\theta$ +- Our interest is in the posterior distribution $p(\theta, \alpha | y)$ + - Prediction problems $p(y_{T+k} | y)$ and interpolation $p(y_t | y)$ are also straightforward with SSM setting + +??? + +So what are state space models? State space models are large class of statistical models including, for example, structural time series models, ARIMA models, and generalized linear models with time-varying coefficients. + +In general we have some observations $y$, from $y_1$ to $y_T$, where the subscript denotes time or other ordering variable, and where $y$ at time $t$ follows a conditional distribution $g_t$ where we condition on $\alpha_t$. + +Here $\alpha_t$ are called latent states, which go similarly from $1$ to $T$, and they follow a transition distribution $p_t$, which gives us the distribution of next state given the current state. + +Both observations $y_t$ and states $\alpha_t$ can be multivariate, and both of these distributions, $p_t$ and $g_t$ can depend on some hyperparameters $\theta$, and our interest is in the joint posterior distribution states $\alpha$ and parameters $\theta$ given our data $y$. + +Also predicting future observations and states, as well interpolation of missing observations is straightforward within this SSM setting. + +--- + +## But wait, what about KFAS? + +- Compared to the KFAS package (Helske, 2017) for state space modelling: + - KFAS is mainly for maximum likelihood inference vs Bayesian approach of bssm + - bssm supports more model types (nonlinear models, stochastic volatility models, SDEs) + - KFAS uses importance sampling for non-Gaussian models, bssm uses particle filtering (scales better) + - bssm is easier to maintain and extend further (written in C++ instead of Fortran) + - Creating models is more user-friendly with KFAS (but see `as_bssm` function!) + +??? + +But wait, what about KFAS? Some of you might be familiar with this other state space modelling package in R, so what's the difference with the bssm? + +KFAS was designed from the perspective of maximum likelihood estimation whereas bssm leans to Bayesian inference, although maximum likelihood estimation is also possible. + +Second, bssm supports wider variety of models, and instead of simple importance sampling, bssm uses particle filtering which scales better with the number of time points. + +bssm is also easier to maintain and extend in future, but on the other hand creating models is currently bit easier using the formula syntax of KFAS. But actually, you can convert KFAS models to bssm format with a helper function `as_bssm` which can be useful when constructing complex models. +--- + +## Bayesian estimation of SSMs + +- Two important special cases: + - Both observation equation and state equation are linear-Gaussian + - States $\alpha_t$ are categorical (often called hidden Markov models, not covered by bssm) +- In these cases the marginal likelihood $p(y | \theta)$ can be computed easily + - Marginalization of latent states results in highly efficient Markov chain Monte Carlo (MCMC) algorithms + - Run MCMC targeting the marginal posterior of hyperparameters $\theta$. + - Given samples from $p(\theta|y)$, simulate latent states from the smoothing distribution $p(\alpha | y, \theta)$. + - $\theta$ is often low dimensional so simple adaptive random walk Metropolis works well. + +??? + +Bayesian estimation of state space models, so how do you estimate these kind of models? There are two special classes of SSMs, first a case where the distributions of observations and states are both linear-Gaussian. Another one is where states are categorical, but I will not discuss this here because that is not supported by bssm. + +What is so special about these models is that the marginal likelihood can be computed in analytically tractable way. For this linear-Gaussian case we can use Kalman filter algorithm gives us the marginal likelihood of $y$ given $\theta$. This marginalization of latent states alpha can be used to construct an efficient Markov chain Monte Carlo (MCMC) algorithm. + +So what we what to do is to run MCMC targeting only the marginal posterior of hyperparameters $\theta$, and given samples from this marginal posterior, we simulate the states from the so called smoothing distribution which is the conditional distribution of all the latent states given all the data and current $\theta$. + +And because $\theta$ is often low-dimensional, simple adaptive random walk Metropolis works typically well. + +--- + +## Bayesian inference for general SSMs + +- In general, marginal likelihood $p(y | \theta)$ is not analytically tractable. Three routes forward: + - Sample both $\theta$ and $\alpha$ directly using, e.g., BUGS (Lunn et al. 2000) or Stan (Stan Development Team 2021). Typically inefficient due to strong correlation structures and high dimensionality of $\alpha$. + - Use (deterministic) approximation of $p(y | \theta)$, e.g, INLA (Rue et al. 2009), extended Kalman filter (EKF). Fast(er) but biased. Bias is hard to quantify. + - Use particle MCMC (Andrieu et al. 2010) where $p(y|\theta)$ is replaced with its unbiased estimator from particle filter. Leads to asymptotically exact inference, but often computationally intensive. Tuning of MCMC nontrivial with respect to number of particles and acceptance rate. + +??? + +For the Bayesian inference of general SSMs, things are more complicated because the marginal likelihood is now longer analytically tractable. Instead, we could consider at least three different options: + +First would be that we forget the special nature of the states and treat the states similarly as $\theta$, and sample both using some general MCMC machinery provided for example by BUGS or Stan. Unfortunately, this is often inefficient, due to the strong correlation structures and high dimensionality of alpha. + +Second option is to leverage some approximate methods, such as Laplace approximations as in INLA. This is often fast, but by biased by construction. Although the bias can often be negligible in practice, but it is hard to quantify the amount of bias in specific application. + +Third option would be to use so-called pseudo-marginal MCMC or particle MCMC methods where the marginal likelihood is replaced by its unbiased estimator from particle filter. This is asymptotically exact like the first option, but often computationally heavy as we need to run particle filter at each iteration, possibly with large number of particles. It can also be bit tricky to tune the MCMC algorithm and define a good number of particles for efficient inference. + +--- + +## IS-MCMC for state space models + +- What if we could combine fast approximations and exact methods? +- Vihola, Helske and Franks (2020) suggest targeting an approximate marginal posterior of $\theta$, combined with importance sampling type post-correction (IS-MCMC): + - Given $\theta$, assume that we can compute approximation $\hat p(y | \theta) = p(y | \theta) / w(\theta)$. + - Run MCMC targeting $\hat p(\theta | y)$, where the marginal likelihood is replaced with the the approximation $\hat p(y | \theta)$. + - For each $\theta$ from approximate marginal, run particle filter to obtain samples of $\alpha$ and unbiased estimate of $p(y | \theta)$. + - We now have weighted samples of $(\theta, \alpha)$ from the correct posterior, with weights $w(\theta)= p(y | \theta) / \hat p(y | \theta)$. + +??? + +IS-MCMC for state space models. So, what bssm does is it combines the fast approximations and exact methods by first finding an approximate marginal posterior of $\theta$s, and then correcting this with importance sampling type of weighting. We call this IS-MCMC method. + +First, assume that we can compute approximation $\hat p(y | \theta)$ of the marginal likelihood for a given $\theta$. +Then we run MCMC targeting approximate marginal posterior of $\theta$ where true likelihood is replaced by its approximation. +Then, for each theta from this approximate posterior, we run particle filter which gives us samples of $\alpha$ and unbiased estimate of likelihood. +So in the end we have a weighted samples from the joint posterior where weights correspond to the ratio of true and approximate likelihood terms. + +This works really well for the models supported for bssm, but of course in general the approximation should be "good enough", similarly as in typical importance sampling. + +--- + + +## Post-correction + +- For post-correction we recommend particle filter called $\psi$-APF (Vihola, Helske, Franks, 2020), which uses the dynamics of the approximating model with look-ahead strategy. +- Based on the approximating densities $\hat g_t(y_t | \alpha_t)$, and $\hat p_t(\alpha_{t+1} | \alpha_t)$ +- Produces correct smoothing distribution and unbiased estimate of the marginal likelihood +- For state space models supported by `bssm`, often only a small number (e.g. 10) particles is enough for accurate likelihood estimate. + +- Post-correction is easy to parallelize and the needs to be done only for accepted $\theta$. + +??? + +For the post correction, bssm uses by default a particle filter called $\psi$-APF, which again leverages the approximate model computed earlier, leading to a particle filter which in many cases needs only few particles, making it computationally efficient. Other particle filters could also be used, for example the basic bootstrap particle filter is also implemented in bssm. + +Note that the post correction needs only be done for each accepted $\theta$ independently, so it is trivial to parallelize efficiently. + + +--- + +## Linear-Gaussian state space models (LGSSM) + +$$ +\begin{aligned} +y_t &= d_t + Z_t \alpha_t + H_t\epsilon_t, \quad \epsilon_t \sim N(0, I)\\ +\alpha_{t+1} &= c_t + T_t\alpha_t + R_t \eta_t, \quad \eta_t \sim N(0, I)\\ +\alpha_1 &\sim N(a_1, P_1) +\end{aligned} +$$ + +- $d_t$, $Z_t$, $H_t$, $c_t$, $T_t$, $R_t$, $a_1$, $P_1$ can depend on $\theta$. +- Kalman filter gives us marginal likelihood $p(y|\theta)$. +- Smoothing algorithms give $p(\alpha|y,\theta)$. +- Building general LGSSM and some special cases in bssm: + +```{r, eval = FALSE} +# univariate LGSSM, ssm_mlg for multivariate version +ssm_ulg(y, Z, H, T, R, a1, P1, D, C, + init_theta, prior_fn, update_fn) + +# Basic structural time series model +bsm_lg(y, sd_level = gamma(1, 2, 10), sd_y = 1, + xreg = X, beta = normal(0, 0, 10)) +``` + +??? + +Ok, so what kind of models bssm supports? + +First we have linear-Gaussian models, where observations y are a linear combination of states plus some Gaussian error term and optional intercept term, and similarly states depend on states of the previous time points. + +Different models can be defined by defining different model components d, Z, H, c, T, R, a1 and P1. These are vectors, matrices or arrays, depending on whether we have univariate or multivariate model, and whether these model components depend on time. + +Often we know the structure of some of the model components whereas some of these depend on the parameter vector $\theta$. + +We can build these models with the bssm using several functions, for example ssm_ulg defines general univariate model, and bsm_lg can be used to define structural time series model, where unknown parameters correspond standard deviations of the noise terms as well as possible regression coefficients of the exogenous variables X. +--- + +## Non-Gaussian observations + +- State equation has the same form as in LGSSMs, but observations are non-Gaussian +- For example, $g_t(y_t | \alpha_t) = \textrm{Poisson}(u_t \exp(d_t + Z_t \alpha_t))$, where $u_t$ is the known exposure at time $t$. +- Filtering, smoothing and likelihood available via sequential Monte Carlo (SMC) i.e. particle filtering. +- Approximate inference possible via Laplace approximation + - Find LGSSM with same mode of $p(\alpha | y, \theta)$ (iteratively) +```{r, eval = FALSE} +ssm_ung(y, Z, T, R, distribution = "poisson") +ssm_mng(...) +bsm_ng(...) +svm(...) +ar1_ng(...) +``` + +??? + +Non-Gaussian observations. This is another class of models supported by the bssm. Here the observations are non-Gaussian, while states are still linear-Gaussian. Model building is similar as in the previous case, we just now have to define the distribution of observations as well. + +Bssm currently supports, Gaussian, Poisson, Binomial, negative binomial, and gamma models, and you can have multivariate models with mixed distributions as well. For these, we can use Laplace approximation for efficient approximate inference, or exact inference based on the IS-MCMC approach. Essentially we iteratively find a linear-Gaussian model which has the same conditional mode of the states as the original model. + +--- + +## Bivariate Poisson model with bssm + +```{r mng, eval = FALSE} + +# latent random walk +alpha <- cumsum(rnorm(100, sd = 0.1)) +# observations +y <- cbind(rpois(100, exp(alpha)), rpois(100, exp(alpha))) + +# function which defines the log-prior density +prior_fun <- function(theta) { + dgamma(theta, 2, 0.01, log = TRUE) +} +# function which returns updated model components +update_fun <- function(theta) { + list(R = array(theta, c(1, 1, 1))) +} + +model <- ssm_mng(y = y, Z = matrix(1, 2, 1), T = 1, + R = 0.1, P1 = 1, distribution = "poisson", + init_theta = 0.1, + prior_fn = prior_fun, update_fn = update_fun) +``` + +??? + +Here is an example of simple bivariate Poisson model where we assume that both time series are generated by the same latent state process $\alpha$. + +So I'm first simulating some data, and then I define two R functions which are used within ssm_mng function which defines the whole model. + +So these two R functions, prior_fun and update_fun which define the log-prior density and how the model components depend on the parameters $\theta$. Even though these are R functions which are used within the compiled C++ code, we only need to call these functions once per each MCMC iteration, so the overhead is small compared actual likelihood computations and so on. These functions, together with definition of model components such as Z and T are then given to the ssm_mng function, which returns a model object usable as an input for other functions of the package. + +--- + +## Other models supported by bssm + +- Non-linear Gaussian models: +$$ +\begin{aligned} +y_t &= Z_t(\alpha_t) + H_t(\alpha_t)\epsilon_t,\\ +\alpha_{t+1} &= T_t(\alpha_t) + R_t(\alpha_t)\eta_t,\\ +\alpha_1 &\sim N(a_1, P_1), +\end{aligned} +$$ + + - Unbiased estimation via particle filtering. + - Approximations with mode matching based on extended Kalman filter and smoother. + +- Models where the state equation is defined as a continuous-time diffusion: +$$ +\textrm{d} \alpha_t = +\mu(\alpha_t) \textrm{d} t + +\sigma(\alpha_t) \textrm{d} B_t, \quad t\geq0, +$$ + + - $B_t$ is a Brownian motion, $\mu$ and $\sigma$ are real-valued functions + - Observation density $p_k(y_k | \alpha_k)$ defined at integer times $k=1\ldots,n$. + +- These use user-defined C++ -snippets for model components based on a template provided + +??? + +In addition, bssm also supports two model types, non-linear models and models where the state equation is defined as a continuous-time diffusion. + +For the nonlinear model, the approximation is based on mode matching similarly as in the non-Gaussian case, but this time using extended Kalman filter and smoother. And unbiased estimation is possibly using particle filter. + +For models where the state equation is defined as a continuous-time diffusion, we assume that we have observations at integer times. Here the approximation is related to the coarseness of the time-discretization mesh. So finer the time-discretization, more computationally demanding the particle filter is, so we can use coarser approximation in the first phase and then do the post-correction using the finer mesh. + +These models are quite general in a way that these nonlinear functions are defined, so we can't use R functions for defining those, as we would need to go go back-and-forth from R and C++ within particle filtering. So instead users should define the models using small C++-snippets using templates provided in the vignettes. + +--- + + +## Illustration: Modelling deaths by drowning in Finland 1969-2019 + +- Yearly drownings $y_t$ assumed to follow Poisson distribution +- Predictor $x_t$ is (centered) average summer temperature (June to August) +- Exposure $u_t$ is the yearly population in hundreds of thousands +$$ +\begin{aligned} +y_t &\sim Poisson(u_t\exp(\beta x_t + \mu_t)) & t=1,\ldots, T\\ +\mu_{t+1} &= \mu_t + \nu_t + \eta_t, & \eta_t \sim N(0, \sigma_\eta^2)\\ +\nu_{t+1} &= \nu_t + \xi_t, & \xi_t \sim N(0, \sigma_\xi^2) +\end{aligned} +$$ +- Hyperparameters $\theta = (\beta, \sigma_\eta, \sigma_\xi)$ +- Latent states $\alpha_t = (\mu_t, \nu_t)$ + +??? + +Now as an example, I show how to analyse yearly drownings in Finland from 1969 to 2019. + +We have data on yearly drownings, which we assume follows Poisson distribution, conditional on the latent level $\mu$, average summer temperature, and yearly population. + +The latent process $\mu$ is assumed to follow a random walk with random slope, and the random slope is again defined as a random walk. + +So we have three hyperparameters, regression coefficient $\beta$, and two standard deviation parameters, and the latent state vector $\alpha_t$ contains the level and the slope terms. +--- + +## Estimating the model with bssm + +```{r, echo = TRUE} +data("drownings") + +model <- bsm_ng( + y = drownings[, "deaths"], + u = drownings[, "population"], + xreg = drownings[, "summer_temp"], + distribution = "poisson", + beta = normal(init = 0, mean = 0, sd = 1), + sd_level = gamma(init = 0.1, shape = 2, rate = 10), + sd_slope = gamma(0, 2, 10)) + +fit <- run_mcmc(model, iter = 20000, particles = 10) +summary(fit, TRUE)[,1:4] +``` + + +??? + +Estimating this model with the bssm: We can build this model using bsm_ng function, where we define our data, the population size (exposure), the distribution (Poisson), and predictor variable (summer temperature), and finally some priors. For priors of basic structural model we can use helper functions normal for regression coefficient and gamma for standard deviations, where we first define the initial value and the parameters of the prior. + +Then we just call the function run_mcmc, with certain number of iterations and number of particles used in the post-correction phase. By default, this uses the IS-MCMC, but fully approximate inference is also possible, as well as normal particle MCMC methods and it's delayed acceptance variant. + +We then see from the summary of theta that the temperature has a small effect, one degree rise in average summer temperature, in Celsius, leads to about 10% more deaths. + + +--- + +## Decrease in drownings after adjusting temperature and population growth + +```{r, echo = FALSE, fig.height = 5, fig.width = 10, fig.align = "center", alt = "Figure showing how the number of drownings per 100,00 has decreased from over 5 to under 2 per year in last 50 years."} + +d_states <- as.data.frame(fit, variable = "states", + time = 1:length(model$y)) + +# weighted summary statistics with Hmisc +intensity <- d_states %>% + filter(variable == "level") %>% + group_by(time) %>% + summarise(mean = Hmisc::wtd.mean(exp(value), weight, normwt = TRUE), + lwr = Hmisc::wtd.quantile(exp(value), weight, + 0.025, normwt = TRUE), + upr = Hmisc::wtd.quantile(exp(value), weight, + 0.975, normwt = TRUE)) +intensity$observations <- model$y / model$u + +intensity %>% + ggplot(aes(x = time, y = mean)) + + geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "#0038A8", alpha = 0.3) + + geom_line(aes(colour = "estimated intensity")) + + geom_point(aes(y = observations, + colour = "observations"),show.legend=FALSE) + + theme_bw() + + theme(legend.title = element_blank()) + + scale_x_continuous("Time") + + ylab("Deaths per 100,000") + + ggtitle("Temperature-adjusted drownings per 100,000 in Finland") + + scale_colour_manual(values = c("#0038A8", "#C85300")) + +``` + +??? + +And finally we have a figure of intensity $\exp(\mu)$, number of deaths per 100,000, which shows that the drownings have drastically decreased in recent decades. In the seventies we had around six drownings per year, per 100,000, and now only have around two. +--- + +### Thank you! + +Some references: + +* Helske, J. (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), + 1-39. https://www.jstatsoft.org/article/view/v078i10 +* Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. ArXiv preprint 2101.08492, https://arxiv.org/abs/2101.08492 +* Vihola M, Helske J, Franks J (2020). Importance Sampling Type Estimators Based on Approximate Marginal MCMC. +Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12492 +* Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS — a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325–337. +* Stan Development Team (2021). Stan Modeling Language Users Guide and Reference Manual, 2.27. https://mc-stan.org +* Rue, H., Martino, S. and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71: 319-392. https://doi.org/10.1111/j.1467-9868.2008.00700.x +* Andrieu, C., Doucet, A. and Holenstein, R. (2010), Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B, 72: 269-342. https://doi.org/10.1111/j.1467-9868.2009.00736.x + +??? +Thank you for listening! Here are some references, please check out the package on CRAN if you're interested in state space modellling. From 73e3e716bc4a536fdbfe8ea73fc0a45190743478 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 9 Jul 2021 23:27:12 +0300 Subject: [PATCH 004/180] forgot to commit stuff (updated drownings data) --- DESCRIPTION | 4 ++-- NEWS | 10 +++++++++- R/bssm-package.R | 14 ++++++++------ R/check_arguments.R | 44 +++++++++++++++++++++++++++----------------- R/predict.R | 7 +++++-- data/drownings.rda | Bin 1080 -> 1462 bytes 6 files changed, 51 insertions(+), 28 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index cc266d6d..b065737e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,8 +2,8 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.4 -Date: 2021-04-13 +Version: 1.1.5 +Date: 2021-06-14 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NEWS b/NEWS index 1f4be274..4cc40784 100644 --- a/NEWS +++ b/NEWS @@ -1,4 +1,12 @@ -bssm 1.1.4 (Release date: 2021-03-) +bssm 1.1.5 (Release date: 2021-06-14) +============== + * Added explicit check for nsim > 0 in predict method as sample function + works with missing argument causing crypting warnings later. + * Updated drownings data until 2019 and changed the temperature variable + to an average over three stations. + * Improved checks for observations and distributions in model building. + +bssm 1.1.4 (Release date: 2021-04-13) ============== * Better documentation for SV model, and changed ordering of arguments to emphasise the recommended parameterization. diff --git a/R/bssm-package.R b/R/bssm-package.R index 2fd5ec7f..54f0ec91 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -17,18 +17,20 @@ #' @aliases bssm #' @importFrom Rcpp evalCpp #' @importFrom coda mcmc -#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start time ts ts.union tsp tsp<- sd +#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm NULL -#' Deaths by drowning in Finland in 1969-2014 +#' Deaths by drowning in Finland in 1969-2019 #' -#' Dataset containing number of deaths by drowning in Finland in 1969-2014, -#' yearly average summer temperatures (June to August) and -#' corresponding population sizes (in hundreds of thousands). +#' Dataset containing number of deaths by drowning in Finland in 1969-2019, +#' corresponding population sizes (in hundreds of thousands), and +#' yearly average summer temperatures (June to August), based on simple +#' unweighted average of three weather stations: Helsinki (Southern Finland), +#' Jyväskylä (Central Finland), and Sodankylä (Northern Finland). #' #' @name drownings #' @docType data -#' @format A time series object containing 46 observations. +#' @format A time series object containing 51 observations. #' @source Statistics Finland \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. #' @keywords datasets #' @examples diff --git a/R/check_arguments.R b/R/check_arguments.R index 9c3971f3..ed035253 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -1,32 +1,42 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { - - if(multivariate) { - if (!is.matrix(x)) { - stop("Argument y must be a numeric matrix or multivariate ts object.") + if (any(!is.na(x))) { + if(multivariate) { + if (!is.matrix(x)) { + stop("Argument y must be a numeric matrix or multivariate ts object.") + } + } else { + if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { + stop("Argument y must be a numeric vector or ts object.") + } + if(distribution != "gaussian" && any(na.omit(x) < 0)) { + stop(paste0("Negative values not allowed for ", distribution, " distribution. ")) + } else { + if(distribution %in% + c("negative binomial", "binomial", "poisson") && any(na.omit(x != as.integer(x)))) { + stop(paste0("Non-integer values not allowed for ", distribution, " distribution. ")) + } + } } - } else { - if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { - stop("Argument y must be a numeric vector or ts object.") + if (any(is.infinite(x))) { + stop("Argument y must contain only finite or NA values.") } - if(distribution != "gaussian" && any(x < 0)) { - stop(paste0("Negative values not allowed for ", distribution, " distribution. ")) + if (length(x) < 2) { + stop("Length of argument y must be at least two.") } } - if (any(is.infinite(x))) { - stop("Argument y must contain only finite or NA values.") - } - if (length(x) < 2) { - stop("Length of argument y must be at least two.") - } - } check_distribution <- function(x, distribution) { for(i in 1:ncol(x)) { - if(distribution[i] != "gaussian" && any(x[,i] < 0)) { + if(distribution[i] != "gaussian" && any(na.omit(x[,i]) < 0)) { stop(paste0("Negative values not allowed for ", distribution[i], " distribution. ")) + } else { + if(distribution[i] %in% + c("negative binomial", "binomial", "poisson") && any(na.omit(x[,i] != as.integer(x[,i])))) { + stop(paste0("Non-integer values not allowed for ", distribution[i], " distribution. ")) + } } } } diff --git a/R/predict.R b/R/predict.R index 595ad1b3..6b29870b 100644 --- a/R/predict.R +++ b/R/predict.R @@ -15,13 +15,15 @@ #' It is also possible to input the original model, which can be useful for example for #' posterior predictive checks. In this case, set argument \code{future} to \code{FALSE}. #' @param nsim Number of samples to draw. -#' @param future Default is \code{TRUE}, in which case predictions are future. +#' @param future Default is \code{TRUE}, in which case predictions are for the future, +#' using posterior samples of (theta, alpha_T+1) i.e. the +#' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. #' @param seed Seed for RNG. #' @param ... Ignored. #' @return Data frame of predicted samples. #' @method predict mcmc_output -#' @rdname predict +#' @aliases predict predict.mcmc_output #' @export #' @examples #' require("graphics") @@ -123,6 +125,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = if(!identical(ncol(object$theta), length(model$theta))) { stop("Number of unknown parameters 'theta' does not correspond to the MCMC output. ") } + if(nsim < 1) stop("Number of samples 'nsim' should be at least one.") if(future) { diff --git a/data/drownings.rda b/data/drownings.rda index 1d4786a78828f61792b515c1b8eccc993dddc90d..a1ddde839742a19ea1ccee1e765eba3fc4e2f38d 100644 GIT binary patch literal 1462 zcmV;n1xflsT4*^jL0KkKSz(XFYXAZ;|NsAg|Nr*?-uv(T|NsC0{^$IESO5L{^S|%^ zeP93O|9{W}z2{z!dbgX+>f7Gvr6}3}XcG+$Owpi527n2Z8UO%H000^q02%{RGynhq z13&-(00STzX^;a%lT^u*(hQ9cNMzF{o>NDn8a+=W(@g^=O{S(G#Cb=gJw`yt^&3!V zG|7+*5Hx6H4Ky?Xri0QLLrpzTQwRWRCYmXKbThXlMq5)X)K-4FgY6pwIvTpa5y1pfnFq4^RO08)p4-oDtro8jW0XNdRW0 zEg>p3gso~LuL`zfxY@LmiAgl3O<9FGbVXp0L)Y&Z0KAZGM)1S7R!(m{Jrx%m-iEg&rBq*x%57R+h|u%Mav*v5zs04NA>U(L5zpak6mCxalmA!5WX%w<-_=JQ$U zCQpPwfw5&VGl^=_dBb+;P*CzWVupZi=zyJNi6PM=pV>6S!A^YU9iV+}BsME3AY>R1 zCN=9y+SQ3zk<4^gtUTh0l^{U0xAtwv(jep^1DQ!V3P%RLhbDx=@&`Jq?npt{PbyHp zRH2FJVmUBe2B$TQimTEFheeU1)~TpcpzueDa>ko8E?eAkTgolEL=ff;Jjw1WRP0El zcB1S+*FJ+Q38xb?{MoY;O_D#G6z3r~$Oh7ZM0Jp9(7{3oq)8+cs0*|qFEVoE2XAfO zJD$S5 z6K$cOqUHLu)o>X>HNVbw_}u7laC1-2T?qkb5Kz=GDNu?u4iE$%g#v7~o1Ds_e1Ty3 zZ2r{59_`M}qI8h}IhP0ka2rnt@chjaOFL@+(Kre7m`zmOa(Q^v5D?QS9f2stCe|_9 zezk{_a+V4MZ-ENAjafqOOZi<%_8 z+rx!OR%nMcDpXsoDx)uG02XneXKh3nAmwqV-A?e7_DnXY-KaarC51IAnFvBGd7db zE*U!hM=2OC6f}f@u6|b%6acq6-qlE0tsC>2=pgM^DDmbLdVz~NB;t47bi2QBT#H?X z1E8p^%Sb-He?29_1AmW7nX)H05C1>y#8Za@j2pc-lxYL-}%ltH|c!^dBso16~_@mBt$Ba5s6fIl}Uu37$PIf1zc{Zv+c8* ztW^#|qzMAi5B6aaT42$OXbXtw42t+Vh**ngLo}kU(jy{YkFih03gpW%=Fy)=z6|wc zBr?AoV-ungQIC0h(Z>jp`IU$z$nQbFs;GL^5ZMQha~ROKf<*E()(0Ya>QDQv&`0)D zg!_8rU-K;?Gt6#I{<4WMH-j}d(i4ZhldgXczgXA`F(F#!WMcjao4`Z6=lMLMAgcg|j#Ghk(I^Lr^_4j_$88QU#l%JpN{F?Y;Lh1Z`xGv5^ z=U^7Pt3B-IBwCM|X_7fEb=&DY%;bd)XXeK}%>8`H#=x)r%toO)6|U37Qqn+b5nkfF{?NIXb|D=GEH7%OjJZ*>}Hq)6TCz z(|m?c-zWo(XEe5>*AALqQ?hI5LC}2nL8*Czpr6S7g}b=}G>h^16#)-$X({@Lj}8K- z*4I0B{{dX(xo5Xttp)DwsxSV!_&0Dz8V@N?{sdfCS^FdMHsH>mC?NZ<0ykJZ^!VA2 zfE&wuLEUEtZc6B86##eljr72eH$W>-xH!=Xpk2G=hg35UTBW~NUDgO%cD-|F%N%Gc z>Q8a6G=R3g{g{93InaLi>HFK)?Fa3p(c`vHazGmp&r26`IN!WFlrEpN+a4x{rRD zScm4*yzHpF3|e=sm`C}rKdpy;8Po@LbRIf~>T@xs^>UD>^C%HjG^YDe=c2;9!l9$M0hnplI8 zcUC(vdcfhbSd4Y$PNT(+RGi&rcbR#o$!0}n&HqH@E-P=SaXFkuLv(*mhh2csr$-9y y--@H1c8gbIkl>0)SypN09S&?zN_{L&+MOi)7Hi?1btX5?v-lq)F_ps)2LJ$ Date: Sat, 10 Jul 2021 00:08:40 +0300 Subject: [PATCH 005/180] update docs --- .Rbuildignore | 1 + DESCRIPTION | 2 +- NAMESPACE | 1 + man/drownings.Rd | 12 +++++++----- man/{predict.Rd => predict.mcmc_output.Rd} | 5 ++++- 5 files changed, 14 insertions(+), 7 deletions(-) rename man/{predict.Rd => predict.mcmc_output.Rd} (96%) diff --git a/.Rbuildignore b/.Rbuildignore index 20d1730c..fb6972db 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -13,3 +13,4 @@ ^vignettes/psi_pf_experiments/.*\.sh$ ^vignettes/psi_pf_experiments/.*\.R$ ^\.github$ +slides_UseR2021 diff --git a/DESCRIPTION b/DESCRIPTION index b065737e..2b7317ef 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,7 +3,7 @@ Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models Version: 1.1.5 -Date: 2021-06-14 +Date: 2021-07-09 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NAMESPACE b/NAMESPACE index b516bf6d..c710e852 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -81,6 +81,7 @@ importFrom(stats,end) importFrom(stats,frequency) importFrom(stats,is.ts) importFrom(stats,logLik) +importFrom(stats,na.omit) importFrom(stats,qlogis) importFrom(stats,quantile) importFrom(stats,sd) diff --git a/man/drownings.Rd b/man/drownings.Rd index 89fd2650..9618e1d6 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -3,17 +3,19 @@ \docType{data} \name{drownings} \alias{drownings} -\title{Deaths by drowning in Finland in 1969-2014} +\title{Deaths by drowning in Finland in 1969-2019} \format{ -A time series object containing 46 observations. +A time series object containing 51 observations. } \source{ Statistics Finland \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. } \description{ -Dataset containing number of deaths by drowning in Finland in 1969-2014, -yearly average summer temperatures (June to August) and -corresponding population sizes (in hundreds of thousands). +Dataset containing number of deaths by drowning in Finland in 1969-2019, +corresponding population sizes (in hundreds of thousands), and +yearly average summer temperatures (June to August), based on simple +unweighted average of three weather stations: Helsinki (Southern Finland), +Jyväskylä (Central Finland), and Sodankylä (Northern Finland). } \examples{ data("drownings") diff --git a/man/predict.Rd b/man/predict.mcmc_output.Rd similarity index 96% rename from man/predict.Rd rename to man/predict.mcmc_output.Rd index 53ea1a85..7aeb9fa7 100644 --- a/man/predict.Rd +++ b/man/predict.mcmc_output.Rd @@ -2,6 +2,7 @@ % Please edit documentation in R/predict.R \name{predict.mcmc_output} \alias{predict.mcmc_output} +\alias{predict} \title{Predictions for State Space Models} \usage{ \method{predict}{mcmc_output}( @@ -29,7 +30,9 @@ posterior predictive checks. In this case, set argument \code{future} to \code{F \item{nsim}{Number of samples to draw.} -\item{future}{Default is \code{TRUE}, in which case predictions are future. +\item{future}{Default is \code{TRUE}, in which case predictions are for the future, +using posterior samples of (theta, alpha_T+1) i.e. the +posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} \item{seed}{Seed for RNG.} From 15cc33e844ebfabcd1f4c885cb322d2eceacf1c1 Mon Sep 17 00:00:00 2001 From: Jouni Helske Date: Sat, 10 Jul 2021 00:17:12 +0300 Subject: [PATCH 006/180] add link to slides --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 80c7810e..a8e7a0b6 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ chain Monte Carlo and importance sampling type weighted Markov chain Monte Carlo Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported. -For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf). +For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf), and an [Rmd file for the slides of my UseR!2021 talk](https://github.com/helske/bssm/tree/master/slides_UseR2021). You can install the latest development version from R-universe with From aef4f5630f5a6f2f920ac0a57fc7cac31922c6c6 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 23 Aug 2021 08:49:36 +0300 Subject: [PATCH 007/180] link to slides --- README.md | 2 +- slides_UseR2021/JouniHelske_bssm.Rmd | 399 -------------- slides_UseR2021/JouniHelske_bssm.html | 504 ------------------ .../figure-html/unnamed-chunk-5-1.png | Bin 7537 -> 0 bytes 4 files changed, 1 insertion(+), 904 deletions(-) delete mode 100644 slides_UseR2021/JouniHelske_bssm.Rmd delete mode 100644 slides_UseR2021/JouniHelske_bssm.html delete mode 100644 slides_UseR2021/JouniHelske_bssm_files/figure-html/unnamed-chunk-5-1.png diff --git a/README.md b/README.md index 80c7810e..abf55354 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ chain Monte Carlo and importance sampling type weighted Markov chain Monte Carlo Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported. -For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf). +For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) and [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) You can install the latest development version from R-universe with diff --git a/slides_UseR2021/JouniHelske_bssm.Rmd b/slides_UseR2021/JouniHelske_bssm.Rmd deleted file mode 100644 index 3b22bbcc..00000000 --- a/slides_UseR2021/JouniHelske_bssm.Rmd +++ /dev/null @@ -1,399 +0,0 @@ ---- -title: "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R" -author: "Jouni Helske (joint work with Matti Vihola)" -institute: "University of Jyväskylä, Finland" -date: "9/7/2021" -output: - xaringan::moon_reader: - lib_dir: libs - css: ["useR", "useR-fonts"] - nature: - highlightStyle: github - highlightLines: true - countIncrementalSlides: false ---- - -```{r setup, include=FALSE} -options(htmltools.dir.version = FALSE) -``` - -```{r, echo = FALSE} -suppressPackageStartupMessages(library("bssm")) -suppressPackageStartupMessages(library("dplyr")) -suppressPackageStartupMessages(library("ggplot2")) -``` - - -## What are state space models? - -- The bssm package (Helske, Vihola, 2021) allows fully Bayesian inference of state space models (SSMs) - - E.g. structural time series, ARIMA models, generalized linear models with time-varying coefficients, cubic splines, SDEs, ... -- In general we have - - Observations $y=(y_1,\ldots,y_T)$ with conditional distribution $g_t(y_t | \alpha_t)$ - - Latent *states* $\alpha=(\alpha_1,\ldots,\alpha_T)$ with a transition distribution $p_t(\alpha_{t+1} | \alpha_t)$ - - Both observations $y_t$ and states $\alpha_t$ can be multivariate -- Both distributions can depend on some hyperparameters $\theta$ -- Our interest is in the posterior distribution $p(\theta, \alpha | y)$ - - Prediction problems $p(y_{T+k} | y)$ and interpolation $p(y_t | y)$ are also straightforward with SSM setting - -??? - -So what are state space models? State space models are large class of statistical models including, for example, structural time series models, ARIMA models, and generalized linear models with time-varying coefficients. - -In general we have some observations $y$, from $y_1$ to $y_T$, where the subscript denotes time or other ordering variable, and where $y$ at time $t$ follows a conditional distribution $g_t$ where we condition on $\alpha_t$. - -Here $\alpha_t$ are called latent states, which go similarly from $1$ to $T$, and they follow a transition distribution $p_t$, which gives us the distribution of next state given the current state. - -Both observations $y_t$ and states $\alpha_t$ can be multivariate, and both of these distributions, $p_t$ and $g_t$ can depend on some hyperparameters $\theta$, and our interest is in the joint posterior distribution states $\alpha$ and parameters $\theta$ given our data $y$. - -Also predicting future observations and states, as well interpolation of missing observations is straightforward within this SSM setting. - ---- - -## But wait, what about KFAS? - -- Compared to the KFAS package (Helske, 2017) for state space modelling: - - KFAS is mainly for maximum likelihood inference vs Bayesian approach of bssm - - bssm supports more model types (nonlinear models, stochastic volatility models, SDEs) - - KFAS uses importance sampling for non-Gaussian models, bssm uses particle filtering (scales better) - - bssm is easier to maintain and extend further (written in C++ instead of Fortran) - - Creating models is more user-friendly with KFAS (but see `as_bssm` function!) - -??? - -But wait, what about KFAS? Some of you might be familiar with this other state space modelling package in R, so what's the difference with the bssm? - -KFAS was designed from the perspective of maximum likelihood estimation whereas bssm leans to Bayesian inference, although maximum likelihood estimation is also possible. - -Second, bssm supports wider variety of models, and instead of simple importance sampling, bssm uses particle filtering which scales better with the number of time points. - -bssm is also easier to maintain and extend in future, but on the other hand creating models is currently bit easier using the formula syntax of KFAS. But actually, you can convert KFAS models to bssm format with a helper function `as_bssm` which can be useful when constructing complex models. ---- - -## Bayesian estimation of SSMs - -- Two important special cases: - - Both observation equation and state equation are linear-Gaussian - - States $\alpha_t$ are categorical (often called hidden Markov models, not covered by bssm) -- In these cases the marginal likelihood $p(y | \theta)$ can be computed easily - - Marginalization of latent states results in highly efficient Markov chain Monte Carlo (MCMC) algorithms - - Run MCMC targeting the marginal posterior of hyperparameters $\theta$. - - Given samples from $p(\theta|y)$, simulate latent states from the smoothing distribution $p(\alpha | y, \theta)$. - - $\theta$ is often low dimensional so simple adaptive random walk Metropolis works well. - -??? - -Bayesian estimation of state space models, so how do you estimate these kind of models? There are two special classes of SSMs, first a case where the distributions of observations and states are both linear-Gaussian. Another one is where states are categorical, but I will not discuss this here because that is not supported by bssm. - -What is so special about these models is that the marginal likelihood can be computed in analytically tractable way. For this linear-Gaussian case we can use Kalman filter algorithm gives us the marginal likelihood of $y$ given $\theta$. This marginalization of latent states alpha can be used to construct an efficient Markov chain Monte Carlo (MCMC) algorithm. - -So what we what to do is to run MCMC targeting only the marginal posterior of hyperparameters $\theta$, and given samples from this marginal posterior, we simulate the states from the so called smoothing distribution which is the conditional distribution of all the latent states given all the data and current $\theta$. - -And because $\theta$ is often low-dimensional, simple adaptive random walk Metropolis works typically well. - ---- - -## Bayesian inference for general SSMs - -- In general, marginal likelihood $p(y | \theta)$ is not analytically tractable. Three routes forward: - - Sample both $\theta$ and $\alpha$ directly using, e.g., BUGS (Lunn et al. 2000) or Stan (Stan Development Team 2021). Typically inefficient due to strong correlation structures and high dimensionality of $\alpha$. - - Use (deterministic) approximation of $p(y | \theta)$, e.g, INLA (Rue et al. 2009), extended Kalman filter (EKF). Fast(er) but biased. Bias is hard to quantify. - - Use particle MCMC (Andrieu et al. 2010) where $p(y|\theta)$ is replaced with its unbiased estimator from particle filter. Leads to asymptotically exact inference, but often computationally intensive. Tuning of MCMC nontrivial with respect to number of particles and acceptance rate. - -??? - -For the Bayesian inference of general SSMs, things are more complicated because the marginal likelihood is now longer analytically tractable. Instead, we could consider at least three different options: - -First would be that we forget the special nature of the states and treat the states similarly as $\theta$, and sample both using some general MCMC machinery provided for example by BUGS or Stan. Unfortunately, this is often inefficient, due to the strong correlation structures and high dimensionality of alpha. - -Second option is to leverage some approximate methods, such as Laplace approximations as in INLA. This is often fast, but by biased by construction. Although the bias can often be negligible in practice, but it is hard to quantify the amount of bias in specific application. - -Third option would be to use so-called pseudo-marginal MCMC or particle MCMC methods where the marginal likelihood is replaced by its unbiased estimator from particle filter. This is asymptotically exact like the first option, but often computationally heavy as we need to run particle filter at each iteration, possibly with large number of particles. It can also be bit tricky to tune the MCMC algorithm and define a good number of particles for efficient inference. - ---- - -## IS-MCMC for state space models - -- What if we could combine fast approximations and exact methods? -- Vihola, Helske and Franks (2020) suggest targeting an approximate marginal posterior of $\theta$, combined with importance sampling type post-correction (IS-MCMC): - - Given $\theta$, assume that we can compute approximation $\hat p(y | \theta) = p(y | \theta) / w(\theta)$. - - Run MCMC targeting $\hat p(\theta | y)$, where the marginal likelihood is replaced with the the approximation $\hat p(y | \theta)$. - - For each $\theta$ from approximate marginal, run particle filter to obtain samples of $\alpha$ and unbiased estimate of $p(y | \theta)$. - - We now have weighted samples of $(\theta, \alpha)$ from the correct posterior, with weights $w(\theta)= p(y | \theta) / \hat p(y | \theta)$. - -??? - -IS-MCMC for state space models. So, what bssm does is it combines the fast approximations and exact methods by first finding an approximate marginal posterior of $\theta$s, and then correcting this with importance sampling type of weighting. We call this IS-MCMC method. - -First, assume that we can compute approximation $\hat p(y | \theta)$ of the marginal likelihood for a given $\theta$. -Then we run MCMC targeting approximate marginal posterior of $\theta$ where true likelihood is replaced by its approximation. -Then, for each theta from this approximate posterior, we run particle filter which gives us samples of $\alpha$ and unbiased estimate of likelihood. -So in the end we have a weighted samples from the joint posterior where weights correspond to the ratio of true and approximate likelihood terms. - -This works really well for the models supported for bssm, but of course in general the approximation should be "good enough", similarly as in typical importance sampling. - ---- - - -## Post-correction - -- For post-correction we recommend particle filter called $\psi$-APF (Vihola, Helske, Franks, 2020), which uses the dynamics of the approximating model with look-ahead strategy. -- Based on the approximating densities $\hat g_t(y_t | \alpha_t)$, and $\hat p_t(\alpha_{t+1} | \alpha_t)$ -- Produces correct smoothing distribution and unbiased estimate of the marginal likelihood -- For state space models supported by `bssm`, often only a small number (e.g. 10) particles is enough for accurate likelihood estimate. - -- Post-correction is easy to parallelize and the needs to be done only for accepted $\theta$. - -??? - -For the post correction, bssm uses by default a particle filter called $\psi$-APF, which again leverages the approximate model computed earlier, leading to a particle filter which in many cases needs only few particles, making it computationally efficient. Other particle filters could also be used, for example the basic bootstrap particle filter is also implemented in bssm. - -Note that the post correction needs only be done for each accepted $\theta$ independently, so it is trivial to parallelize efficiently. - - ---- - -## Linear-Gaussian state space models (LGSSM) - -$$ -\begin{aligned} -y_t &= d_t + Z_t \alpha_t + H_t\epsilon_t, \quad \epsilon_t \sim N(0, I)\\ -\alpha_{t+1} &= c_t + T_t\alpha_t + R_t \eta_t, \quad \eta_t \sim N(0, I)\\ -\alpha_1 &\sim N(a_1, P_1) -\end{aligned} -$$ - -- $d_t$, $Z_t$, $H_t$, $c_t$, $T_t$, $R_t$, $a_1$, $P_1$ can depend on $\theta$. -- Kalman filter gives us marginal likelihood $p(y|\theta)$. -- Smoothing algorithms give $p(\alpha|y,\theta)$. -- Building general LGSSM and some special cases in bssm: - -```{r, eval = FALSE} -# univariate LGSSM, ssm_mlg for multivariate version -ssm_ulg(y, Z, H, T, R, a1, P1, D, C, - init_theta, prior_fn, update_fn) - -# Basic structural time series model -bsm_lg(y, sd_level = gamma(1, 2, 10), sd_y = 1, - xreg = X, beta = normal(0, 0, 10)) -``` - -??? - -Ok, so what kind of models bssm supports? - -First we have linear-Gaussian models, where observations y are a linear combination of states plus some Gaussian error term and optional intercept term, and similarly states depend on states of the previous time points. - -Different models can be defined by defining different model components d, Z, H, c, T, R, a1 and P1. These are vectors, matrices or arrays, depending on whether we have univariate or multivariate model, and whether these model components depend on time. - -Often we know the structure of some of the model components whereas some of these depend on the parameter vector $\theta$. - -We can build these models with the bssm using several functions, for example ssm_ulg defines general univariate model, and bsm_lg can be used to define structural time series model, where unknown parameters correspond standard deviations of the noise terms as well as possible regression coefficients of the exogenous variables X. ---- - -## Non-Gaussian observations - -- State equation has the same form as in LGSSMs, but observations are non-Gaussian -- For example, $g_t(y_t | \alpha_t) = \textrm{Poisson}(u_t \exp(d_t + Z_t \alpha_t))$, where $u_t$ is the known exposure at time $t$. -- Filtering, smoothing and likelihood available via sequential Monte Carlo (SMC) i.e. particle filtering. -- Approximate inference possible via Laplace approximation - - Find LGSSM with same mode of $p(\alpha | y, \theta)$ (iteratively) -```{r, eval = FALSE} -ssm_ung(y, Z, T, R, distribution = "poisson") -ssm_mng(...) -bsm_ng(...) -svm(...) -ar1_ng(...) -``` - -??? - -Non-Gaussian observations. This is another class of models supported by the bssm. Here the observations are non-Gaussian, while states are still linear-Gaussian. Model building is similar as in the previous case, we just now have to define the distribution of observations as well. - -Bssm currently supports, Gaussian, Poisson, Binomial, negative binomial, and gamma models, and you can have multivariate models with mixed distributions as well. For these, we can use Laplace approximation for efficient approximate inference, or exact inference based on the IS-MCMC approach. Essentially we iteratively find a linear-Gaussian model which has the same conditional mode of the states as the original model. - ---- - -## Bivariate Poisson model with bssm - -```{r mng, eval = FALSE} - -# latent random walk -alpha <- cumsum(rnorm(100, sd = 0.1)) -# observations -y <- cbind(rpois(100, exp(alpha)), rpois(100, exp(alpha))) - -# function which defines the log-prior density -prior_fun <- function(theta) { - dgamma(theta, 2, 0.01, log = TRUE) -} -# function which returns updated model components -update_fun <- function(theta) { - list(R = array(theta, c(1, 1, 1))) -} - -model <- ssm_mng(y = y, Z = matrix(1, 2, 1), T = 1, - R = 0.1, P1 = 1, distribution = "poisson", - init_theta = 0.1, - prior_fn = prior_fun, update_fn = update_fun) -``` - -??? - -Here is an example of simple bivariate Poisson model where we assume that both time series are generated by the same latent state process $\alpha$. - -So I'm first simulating some data, and then I define two R functions which are used within ssm_mng function which defines the whole model. - -So these two R functions, prior_fun and update_fun which define the log-prior density and how the model components depend on the parameters $\theta$. Even though these are R functions which are used within the compiled C++ code, we only need to call these functions once per each MCMC iteration, so the overhead is small compared actual likelihood computations and so on. These functions, together with definition of model components such as Z and T are then given to the ssm_mng function, which returns a model object usable as an input for other functions of the package. - ---- - -## Other models supported by bssm - -- Non-linear Gaussian models: -$$ -\begin{aligned} -y_t &= Z_t(\alpha_t) + H_t(\alpha_t)\epsilon_t,\\ -\alpha_{t+1} &= T_t(\alpha_t) + R_t(\alpha_t)\eta_t,\\ -\alpha_1 &\sim N(a_1, P_1), -\end{aligned} -$$ - - - Unbiased estimation via particle filtering. - - Approximations with mode matching based on extended Kalman filter and smoother. - -- Models where the state equation is defined as a continuous-time diffusion: -$$ -\textrm{d} \alpha_t = -\mu(\alpha_t) \textrm{d} t + -\sigma(\alpha_t) \textrm{d} B_t, \quad t\geq0, -$$ - - - $B_t$ is a Brownian motion, $\mu$ and $\sigma$ are real-valued functions - - Observation density $p_k(y_k | \alpha_k)$ defined at integer times $k=1\ldots,n$. - -- These use user-defined C++ -snippets for model components based on a template provided - -??? - -In addition, bssm also supports two model types, non-linear models and models where the state equation is defined as a continuous-time diffusion. - -For the nonlinear model, the approximation is based on mode matching similarly as in the non-Gaussian case, but this time using extended Kalman filter and smoother. And unbiased estimation is possibly using particle filter. - -For models where the state equation is defined as a continuous-time diffusion, we assume that we have observations at integer times. Here the approximation is related to the coarseness of the time-discretization mesh. So finer the time-discretization, more computationally demanding the particle filter is, so we can use coarser approximation in the first phase and then do the post-correction using the finer mesh. - -These models are quite general in a way that these nonlinear functions are defined, so we can't use R functions for defining those, as we would need to go go back-and-forth from R and C++ within particle filtering. So instead users should define the models using small C++-snippets using templates provided in the vignettes. - ---- - - -## Illustration: Modelling deaths by drowning in Finland 1969-2019 - -- Yearly drownings $y_t$ assumed to follow Poisson distribution -- Predictor $x_t$ is (centered) average summer temperature (June to August) -- Exposure $u_t$ is the yearly population in hundreds of thousands -$$ -\begin{aligned} -y_t &\sim Poisson(u_t\exp(\beta x_t + \mu_t)) & t=1,\ldots, T\\ -\mu_{t+1} &= \mu_t + \nu_t + \eta_t, & \eta_t \sim N(0, \sigma_\eta^2)\\ -\nu_{t+1} &= \nu_t + \xi_t, & \xi_t \sim N(0, \sigma_\xi^2) -\end{aligned} -$$ -- Hyperparameters $\theta = (\beta, \sigma_\eta, \sigma_\xi)$ -- Latent states $\alpha_t = (\mu_t, \nu_t)$ - -??? - -Now as an example, I show how to analyse yearly drownings in Finland from 1969 to 2019. - -We have data on yearly drownings, which we assume follows Poisson distribution, conditional on the latent level $\mu$, average summer temperature, and yearly population. - -The latent process $\mu$ is assumed to follow a random walk with random slope, and the random slope is again defined as a random walk. - -So we have three hyperparameters, regression coefficient $\beta$, and two standard deviation parameters, and the latent state vector $\alpha_t$ contains the level and the slope terms. ---- - -## Estimating the model with bssm - -```{r, echo = TRUE} -data("drownings") - -model <- bsm_ng( - y = drownings[, "deaths"], - u = drownings[, "population"], - xreg = drownings[, "summer_temp"], - distribution = "poisson", - beta = normal(init = 0, mean = 0, sd = 1), - sd_level = gamma(init = 0.1, shape = 2, rate = 10), - sd_slope = gamma(0, 2, 10)) - -fit <- run_mcmc(model, iter = 20000, particles = 10) -summary(fit, TRUE)[,1:4] -``` - - -??? - -Estimating this model with the bssm: We can build this model using bsm_ng function, where we define our data, the population size (exposure), the distribution (Poisson), and predictor variable (summer temperature), and finally some priors. For priors of basic structural model we can use helper functions normal for regression coefficient and gamma for standard deviations, where we first define the initial value and the parameters of the prior. - -Then we just call the function run_mcmc, with certain number of iterations and number of particles used in the post-correction phase. By default, this uses the IS-MCMC, but fully approximate inference is also possible, as well as normal particle MCMC methods and it's delayed acceptance variant. - -We then see from the summary of theta that the temperature has a small effect, one degree rise in average summer temperature, in Celsius, leads to about 10% more deaths. - - ---- - -## Decrease in drownings after adjusting temperature and population growth - -```{r, echo = FALSE, fig.height = 5, fig.width = 10, fig.align = "center", alt = "Figure showing how the number of drownings per 100,00 has decreased from over 5 to under 2 per year in last 50 years."} - -d_states <- as.data.frame(fit, variable = "states", - time = 1:length(model$y)) - -# weighted summary statistics with Hmisc -intensity <- d_states %>% - filter(variable == "level") %>% - group_by(time) %>% - summarise(mean = Hmisc::wtd.mean(exp(value), weight, normwt = TRUE), - lwr = Hmisc::wtd.quantile(exp(value), weight, - 0.025, normwt = TRUE), - upr = Hmisc::wtd.quantile(exp(value), weight, - 0.975, normwt = TRUE)) -intensity$observations <- model$y / model$u - -intensity %>% - ggplot(aes(x = time, y = mean)) + - geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "#0038A8", alpha = 0.3) + - geom_line(aes(colour = "estimated intensity")) + - geom_point(aes(y = observations, - colour = "observations"),show.legend=FALSE) + - theme_bw() + - theme(legend.title = element_blank()) + - scale_x_continuous("Time") + - ylab("Deaths per 100,000") + - ggtitle("Temperature-adjusted drownings per 100,000 in Finland") + - scale_colour_manual(values = c("#0038A8", "#C85300")) - -``` - -??? - -And finally we have a figure of intensity $\exp(\mu)$, number of deaths per 100,000, which shows that the drownings have drastically decreased in recent decades. In the seventies we had around six drownings per year, per 100,000, and now only have around two. ---- - -### Thank you! - -Some references: - -* Helske, J. (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), - 1-39. https://www.jstatsoft.org/article/view/v078i10 -* Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. ArXiv preprint 2101.08492, https://arxiv.org/abs/2101.08492 -* Vihola M, Helske J, Franks J (2020). Importance Sampling Type Estimators Based on Approximate Marginal MCMC. -Scandinavian Journal of Statistics. https://doi.org/10.1111/sjos.12492 -* Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS — a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325–337. -* Stan Development Team (2021). Stan Modeling Language Users Guide and Reference Manual, 2.27. https://mc-stan.org -* Rue, H., Martino, S. and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71: 319-392. https://doi.org/10.1111/j.1467-9868.2008.00700.x -* Andrieu, C., Doucet, A. and Holenstein, R. (2010), Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B, 72: 269-342. https://doi.org/10.1111/j.1467-9868.2009.00736.x - -??? -Thank you for listening! Here are some references, please check out the package on CRAN if you're interested in state space modellling. diff --git a/slides_UseR2021/JouniHelske_bssm.html b/slides_UseR2021/JouniHelske_bssm.html deleted file mode 100644 index 712277af..00000000 --- a/slides_UseR2021/JouniHelske_bssm.html +++ /dev/null @@ -1,504 +0,0 @@ - - - - bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R - - - - - - - - - - - - - - - - - diff --git a/slides_UseR2021/JouniHelske_bssm_files/figure-html/unnamed-chunk-5-1.png b/slides_UseR2021/JouniHelske_bssm_files/figure-html/unnamed-chunk-5-1.png deleted file mode 100644 index 05bc7f8c7d2ad5a65907a2e8918cd1313a4ce469..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7537 zcmd6Mc{o)6|LB>`8nTVDR`xQ=(qe4KmaUc(KvLuXst7M6A z>}AM@#FV9k?BM>>P0fA0L8G0U@r1wL_YJ_Q9n1(R_*%yDr(1#!$v8zTV^B>YjcBc91rk&r;6CD0f-6+;C+ zgn~&%*}Jb5Za{?_zM_IDtfGR!XtM;`;s|YVK5cPvZEcJ;27{J>rzN=2Dwt=*4mXWK zV^lB!jlq~K@R=-#n`E?2G8i*6GmHv1Mq3<{VP|r(fWg?2#}vRMWH1=(j7ei=Y}l_D z+4%ziC*RIM^p1vY0)WVtvA&Mg_4GLkm&-^+Xp6a)@4W~8ls?N;wQscCYyMB{-VcAn zx5s>SYw-Rg<6T`QY&X8b5S9cJ(66;Ya;p#!l5v91U}9Y2#3njoAKObmHWQ*X8+&Ddyb{i2ZDX?zrNP_pfGKt`}d;2E9C-rRv+b^YZhxmS^)G+|QNX z<@tS{m0rBieM~i}#-8YOKE2m6plB~8Vm^Cg`hI_b{0jTwF)Uf9u0nm8ca_LZfQFxh{lIieetSB&1AR^d!T2py*RemTL`*hyM9xe$$tu_RL1sQ%$lKb3 zf#zL{O<#Pq*YgHEn@|rO3J?EGZ64PfH9p|?{u*PNAXq~-3n^$Ho;|~3rSyws%&l0? zfH&Vn<{^+~Wd^F;yB6;&t|&e>q8%RMzjE=7pi6)x_mQ{rnL=^{M;3<_f8RsTo;rZo zs8CNNyTPL3{w?~RiO-Y;p9GK%iv#o4#=j66Xd{}RUS;0c*U;Seva5ORQ7y&vg;fTa zsEN5#0|Aep-S0kLphdWm;1pIK;l}x_3eF-~SZnEUYDNWpUZ1b&Ilj$Kd=Wk!dUIrJ?q_vG?$9|f9&Sor7uMCP-VCJX z853oozyiN#*H6zadB{p`?Ik0zZZw_$rM=#BFR|{P%#MCu{v1W0=bznjd7G4v0h^MN z|F3}bS*WJ_fr^1Itaf~=tn{qjobgv zfKLk!X7jUI1p_nprL>?@*cAZblxP2+0(m6m>J2+lw5dSKJ93wBwf#8^^Cu~yf=|qJ z`SrwQ1pM-*G>54@=ne#ZOSvB5Lp}<4OX5?$M>G#1A;%r!XXGwmX#&TKaztH54(pN> z22e4@7_gQjMNUB;QMBl_|1UY83M^R64269Ja;r(ATWc%M7eTZQQHVf>ZX|8F__5Y> zmOZB1=Cfj*Od&9QF}^Nx8-hPW-cJX)9c5IK^O2bK&W_Dd&IZ@ZPYqD_v42H%I`~5J z8L|JS42h|sgN-xudYtFyKqn{ussG5UjrBVLP#m2s|0iMMA($ltvGi7<2N@db$nT92 zc8qhibH=0{tuq7rQX@;OcoZ5^tjNcHokGi4DePutO>UrBFHuDhmT?Lr{-{L$Fj)b4ks-k1ke=rNk(VzB+`j@%?*xhA;qaCm; 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z7jTPY)cMDG zEN!eVbmc(!qFwD?g`xuVphwTk15+d zr_L-5#>U~MYr#Gz|=O>KMqhaHN-@F+YmaK@+m{C$bY z@V&V9jl&IJrhOXj?~P3LyeZddy$8=iPp|A66FL?DTixEE8fQ;murIGsSi9>zy)jnb z+3NdrMy}54i(%qK2;&%P`7PVyElaQQ0rWBOi6I40IST9;lZ<*CQ{$CEZ0*>cL zOQ*a)EJU%t%q@^}iPNroVR$PyT6s#&kpFAvbC+|Wx33cAyD+wIEx+htXFtW{7l!G+XgMamAU$hl;Yx&};RPn0MMT3JgNx+< fB=x_$u7{Jv*>1=HSD)?tb!u#2rvFm+!p;8yVGj^F From 09e681e73d4f6742f72eedfcd6ac3615457129e4 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 31 Aug 2021 23:11:00 +0300 Subject: [PATCH 008/180] started to clean up codes --- DESCRIPTION | 4 ++-- NEWS | 4 ++++ R/approx.R | 6 ++++-- R/as.data.frame.mcmc_output.R | 8 ++++---- R/as_bssm.R | 5 ++--- R/bssm-package.R | 2 +- R/check_arguments.R | 2 +- R/importance_sample.R | 2 +- R/models.R | 16 ++++++++-------- R/particle_smoother.R | 2 +- R/post_correction.R | 2 +- R/predict.R | 4 ++-- R/print_mcmc.R | 4 ++-- R/priors.R | 2 +- tests/testthat/test_basics.R | 2 +- tests/testthat/test_models.R | 2 +- 16 files changed, 36 insertions(+), 31 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2b7317ef..3c548a2e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,8 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.5 -Date: 2021-07-09 +Version: 1.1.6 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -33,6 +32,7 @@ SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) RoxygenNote: 7.1.1 VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues +URL: https://github.com/helske/bssm ByteCompile: true Encoding: UTF-8 NeedsCompilation: yes diff --git a/NEWS b/NEWS index 4cc40784..f6bf520a 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,7 @@ +bssm 1.1.6 (Release date: ) +============== + * Cleaned some codes and added more comprehensive tests in line with pkgcheck tests. + bssm 1.1.5 (Release date: 2021-06-14) ============== * Added explicit check for nsim > 0 in predict method as sample function diff --git a/R/approx.R b/R/approx.R index b2481b4a..c42ae0c6 100644 --- a/R/approx.R +++ b/R/approx.R @@ -1,7 +1,9 @@ #' Gaussian Approximation of Non-Gaussian/Non-linear State Space Model #' -#' Returns the approximating Gaussian model. This function is rarely needed itself, -#' and is mainly available for testing and debugging purposes. +#' Returns the approximating Gaussian model which has the same conditional +#' mode of p(alpha|y, theta) as the original model. +#' This function is rarely needed itself, and is mainly available for +#' testing and debugging purposes. #' #' @param model Model to be approximated. #' @param max_iter Maximum number of iterations. diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index 124648c3..9f3bcd6f 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -56,10 +56,10 @@ as.data.frame.mcmc_output <- function(x, variable = rep(colnames(values), each = nrow(values)), weight = weights) } else { - if (missing(times)) times <- 1:nrow(x$alpha) - if (missing(states)) states <- 1:ncol(x$alpha) + if (missing(times)) times <- seq_len(nrow(x$alpha)) + if (missing(states)) states <- seq_len(ncol(x$alpha)) if (expand) { - values <- aperm(x$alpha[times, states, rep(1:nrow(x$theta), times = x$counts), drop = FALSE], 3:1) + values <- aperm(x$alpha[times, states, rep(seq_len(nrow(x$theta)), times = x$counts), drop = FALSE], 3:1) iters <- seq(x$burnin + 1, x$iter, by = x$thin) weights <- if(x$mcmc_type %in% paste0("is", 1:3)) rep(x$weights, times = x$counts) else 1 } else { @@ -67,7 +67,7 @@ as.data.frame.mcmc_output <- function(x, iters <- x$burnin + cumsum(x$counts) weights <- x$counts * (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } - times <- time(ts(1:nrow(x$alpha), + times <- time(ts(seq_len(nrow(x$alpha)), start = attr(x, "ts")$start, frequency = attr(x, "ts")$frequency))[times] d <- data.frame(iter = iters, diff --git a/R/as_bssm.R b/R/as_bssm.R index 502854c1..cc56b611 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -46,7 +46,7 @@ as_bssm <- function(model, kappa = 100, ...) { Z <- aperm(model$Z, c(2, 3, 1)) dim(Z) <- dim(Z)[1:2] } else { - Z = model$Z + Z <- model$Z } if (any(model$distribution != "gaussian")) { @@ -121,8 +121,7 @@ as_bssm <- function(model, kappa = 100, ...) { } else { if (attr(model, "p") == 1) { - H = sqrt(c(model$H)) - out <- ssm_ulg(y = model$y, Z =Z, H = H, T = model$T, R = R, + out <- ssm_ulg(y = model$y, Z = Z, H = sqrt(c(model$H)), T = model$T, R = R, a1 = c(model$a1), P1 = model$P1, state_names = rownames(model$a1), ...) } else { H <- model$H diff --git a/R/bssm-package.R b/R/bssm-package.R index 54f0ec91..15cb307d 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -77,4 +77,4 @@ NULL #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) #' y <- rpois(100, exp(cumsum(slope + c(0, rnorm(99, sd = 0.1))))) -NULL \ No newline at end of file +NULL diff --git a/R/check_arguments.R b/R/check_arguments.R index ed035253..b6d0fbbe 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -29,7 +29,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } check_distribution <- function(x, distribution) { - for(i in 1:ncol(x)) { + for(i in seq_len(ncol(x))) { if(distribution[i] != "gaussian" && any(na.omit(x[,i]) < 0)) { stop(paste0("Negative values not allowed for ", distribution[i], " distribution. ")) } else { diff --git a/R/importance_sample.R b/R/importance_sample.R index ab0fe4bf..83bc7c84 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -49,4 +49,4 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, rownames(out$alpha) <- names(model$a1) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out -} \ No newline at end of file +} diff --git a/R/models.R b/R/models.R index f0f49c8f..6bf9107a 100644 --- a/R/models.R +++ b/R/models.R @@ -242,7 +242,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), rownames(P1) <- colnames(P1) <- state_names if(is.null(names(init_theta)) && length(init_theta) > 0) - names(init_theta) <- paste0("theta_", 1:length(init_theta)) + names(init_theta) <- paste0("theta_", seq_along(init_theta)) # xreg and beta are need in C++ side in order to combine constructors @@ -404,7 +404,7 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, rownames(P1) <- colnames(P1) <- state_names if(is.null(names(init_theta)) && length(init_theta) > 0) - names(init_theta) <- paste0("theta_", 1:length(init_theta)) + names(init_theta) <- paste0("theta_", seq_along(init_theta)) # xreg and beta are need in C++ side in order to combine constructors structure(list(y = as.ts(y), Z = Z, T = T, R = R, a1 = a1, P1 = P1, @@ -550,7 +550,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), rownames(P1) <- colnames(P1) <- state_names if(is.null(names(init_theta)) && length(init_theta) > 0) - names(init_theta) <- paste0("theta_", 1:length(init_theta)) + names(init_theta) <- paste0("theta_", seq_along(init_theta)) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, a1 = a1, P1 = P1, D = D, C = C, update_fn = update_fn, @@ -702,7 +702,7 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, rownames(P1) <- colnames(P1) <- state_names if(is.null(names(init_theta)) && length(init_theta) > 0) - names(init_theta) <- paste0("theta_", 1:length(init_theta)) + names(init_theta) <- paste0("theta_", seq_along(init_theta)) structure(list(y = as.ts(y), Z = Z, T = T, R = R, a1 = a1, P1 = P1, phi = phi, u = u, @@ -783,7 +783,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, } check_beta(coefs, nx) if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_",1:ncol(xreg)) + colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) } names(coefs) <- colnames(xreg) } @@ -1055,7 +1055,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, check_beta(coefs, nx) if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_",1:ncol(xreg)) + colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) } names(coefs) <- colnames(xreg) @@ -1393,7 +1393,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NUL check_beta(coefs, nx) if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_",1:ncol(xreg)) + colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) } names(coefs) <- colnames(xreg) @@ -1524,7 +1524,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { check_beta(coefs, nx) if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_",1:ncol(xreg)) + colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) } names(coefs) <- colnames(xreg) diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 324a7563..caaf4691 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -75,7 +75,7 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", out$Vt <- array(apply(out$alpha[1, , ], 1, var), c(1, 1, nrow(out$alphahat))) } else { out$Vt <- array(NA, c(ncol(out$alphahat), ncol(out$alphahat), nrow(out$alphahat))) - for(i in 1:nrow(out$alphahat)) { + for(i in seq_len(nrow(out$alphahat))) { out$Vt[,, i] <- cov(t(out$alpha[,i,])) } } diff --git a/R/post_correction.R b/R/post_correction.R index 26cac129..026f7e00 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -235,4 +235,4 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, mcmc_output$seed <- c(mcmc_output$seed, seed) mcmc_output$call <- c(mcmc_output$call, match.call()) mcmc_output -} \ No newline at end of file +} diff --git a/R/predict.R b/R/predict.R index 6b29870b..a3fd06b4 100644 --- a/R/predict.R +++ b/R/predict.R @@ -134,7 +134,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = log(object$theta[,1:(ncol(object$theta) - length(model$beta))]) } w <- object$counts * (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(1:nrow(object$theta), size = nsim, prob = w, replace = TRUE) + idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) theta <- t(object$theta[idx, ]) alpha <- matrix(object$alpha[nrow(object$alpha),,idx], nrow = ncol(object$alpha)) @@ -207,7 +207,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = } w <- object$counts * (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(1:nrow(object$theta), size = nsim, prob = w, replace = TRUE) + idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) n <- nrow(object$alpha) - 1L m <- ncol(object$alpha) diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 0fa5f8c9..0196b9f6 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -271,8 +271,8 @@ expand_sample <- function(x, variable = "theta", times, states, by_states = TRUE out <- apply(x$theta, 2, rep, times = x$counts) } else { if (x$output_type == 1) { - if(missing(times)) times <- 1:nrow(x$alpha) - if(missing(states)) states <- 1:ncol(x$alpha) + if(missing(times)) times <- seq_len(nrow(x$alpha)) + if(missing(states)) states <- seq_len(ncol(x$alpha)) if(by_states) { out <- lapply(states, function(i) { diff --git a/R/priors.R b/R/priors.R index 6deda193..7a23a59c 100644 --- a/R/priors.R +++ b/R/priors.R @@ -122,7 +122,7 @@ combine_priors <- function(x) { prior_distributions <- sapply(x, "[[", "prior_distribution") parameters <- matrix(NA, 4, length(prior_distributions)) - for(i in 1:length(prior_distributions)) { + for(i in seq_along(prior_distributions)) { parameters[1:(length(x[[i]])-2), i] <- as.numeric(x[[i]][-(1:2)]) } list(prior_distributions = diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index add7bf88..f9a565ff 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -181,4 +181,4 @@ test_that("multivariate normal pdf works", { a[2,] <- a[, 2] <- 0 logp3 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) expect_equivalent(logp3, -12.5587625856078, tolerance = 1e-6) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index 15bbf0a8..0517da5d 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -80,4 +80,4 @@ test_that("multivariate non-gaussian model", { R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun), NA) expect_error(logLik(mng_model, particles = 10), NA) -}) \ No newline at end of file +}) From fbdddba7aa6caea2b2bfafdf92ae3f8a82086466 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 31 Aug 2021 23:40:23 +0300 Subject: [PATCH 009/180] regression construction to own function, sapply to vapply --- R/check_arguments.R | 32 ++++++++ R/models.R | 195 +++++++++----------------------------------- R/print_mcmc.R | 8 +- R/priors.R | 2 +- 4 files changed, 75 insertions(+), 162 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index b6d0fbbe..dbbfc90d 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -155,3 +155,35 @@ check_C <- function(x, m, n) { stop("'C' must be m x 1 or m x n matrix, where m is the number of states.") } } + +create_regression <- function(beta, xreg, n) { + if(is.null(xreg)) { + list(xreg = matrix(0, 0, 0), coefs = numeric(0), beta = NULL) + } else { + if (missing(beta) || is.null(beta)) { + stop("No prior defined for beta. ") + } else { + if(!is_prior(beta) && !is_prior_list(beta)) { + stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") + } else { + if (is.null(dim(xreg)) && length(xreg) == n) { + dim(xreg) <- c(n, 1) + } + check_xreg(xreg, n) + nx <- ncol(xreg) + if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] + if(nx > 1) { + coefs <- vapply(beta, "[[", "init", FUN.VALUE = 1) + } else { + coefs <- beta$init + } + check_beta(coefs, nx) + if (nx > 0 && is.null(colnames(xreg))) { + colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) + } + names(coefs) <- colnames(xreg) + } + } + list(xreg = xreg, coefs = coefs, beta = beta) + } +} \ No newline at end of file diff --git a/R/models.R b/R/models.R index 6bf9107a..3c7d51e8 100644 --- a/R/models.R +++ b/R/models.R @@ -756,37 +756,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, check_y(y) n <- length(y) - if (is.null(xreg)) { - xreg <- matrix(0, 0, 0) - coefs <- numeric(0) - beta <- NULL - } else { - - if (missing(beta) || is.null(beta)) { - stop("No prior defined for beta. ") - } - if(!is_prior(beta) && !is_prior_list(beta)) { - stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") - } - - if (is.null(dim(xreg)) && length(xreg) == n) { - xreg <- matrix(xreg, n, 1) - } - - check_xreg(xreg, n) - nx <- ncol(xreg) - if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] - if(nx > 1) { - coefs <- sapply(beta, "[[", "init") - } else { - coefs <- beta$init - } - check_beta(coefs, nx) - if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) - } - names(coefs) <- colnames(xreg) - } + regression_part <- create_regression(beta, xreg, n) notfixed <- c("y" = 1, "level" = 1, "slope" = 1, "seasonal" = 1) @@ -923,12 +893,15 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, if(ncol(xreg) > 1) { - priors <- c(list(sd_y, sd_level, sd_slope, sd_seasonal), beta) + priors <- c(list(sd_y, sd_level, sd_slope, sd_seasonal), + regression_part$beta) } else { - priors <- list(sd_y, sd_level, sd_slope, sd_seasonal, beta) + priors <- list(sd_y, sd_level, sd_slope, sd_seasonal, + regresssion_part$beta) } - names(priors) <- c("sd_y", "sd_level", "sd_slope", "sd_seasonal", names(coefs)) - priors <- priors[sapply(priors, is_prior)] + names(priors) <- c("sd_y", "sd_level", "sd_slope", "sd_seasonal", + names(regression_part$coefs)) + priors <- priors[vapply(priors, is_prior, TRUE)] if (!missing(D)) { check_D(D, 1L, n) @@ -940,11 +913,11 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, } else { C <- matrix(0, m, 1) } - theta <- if (length(priors) > 0) sapply(priors, "[[", "init") else numeric(0) + theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, - a1 = a1, P1 = P1, xreg = xreg, beta = coefs, + a1 = a1, P1 = P1, xreg = regression_part$xreg, beta = regression_part$coefs, D = D, C = C, slope = slope, seasonal = seasonal, period = period, @@ -1027,39 +1000,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, check_y(y, multivariate = FALSE, distribution) n <- length(y) - if (is.null(xreg)) { - xreg <- matrix(0, 0, 0) - coefs <- numeric(0) - beta <- NULL - } else { - - if (missing(beta) || is.null(beta)) { - stop("No prior defined for beta. ") - } - if(!is_prior(beta) && !is_prior_list(beta)) { - stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") - } - - if (is.null(dim(xreg)) && length(xreg) == n) { - xreg <- matrix(xreg, n, 1) - } - - check_xreg(xreg, n) - nx <- ncol(xreg) - if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] - if(nx > 1) { - coefs <- sapply(beta, "[[", "init") - } else { - coefs <- beta$init - } - check_beta(coefs, nx) - - if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) - } - names(coefs) <- colnames(xreg) - - } + regression_part <- create_regression(beta, xreg, n) notfixed <- c("level" = 1, "slope" = 1, "seasonal" = 1) @@ -1217,13 +1158,15 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, rownames(T) <- colnames(T) <- rownames(R) <- state_names if(ncol(xreg) > 1) { - priors <- c(list(sd_level, sd_slope, sd_seasonal, sd_noise, phi), beta) + priors <- c(list(sd_level, sd_slope, sd_seasonal, sd_noise, phi), + regression_part$beta) } else { - priors <- list(sd_level, sd_slope, sd_seasonal, sd_noise, phi, beta) + priors <- list(sd_level, sd_slope, sd_seasonal, sd_noise, phi, + regression_part$beta) } names(priors) <- c("sd_level", "sd_slope", "sd_seasonal", "sd_noise", "phi", - names(coefs)) - priors <- priors[sapply(priors, is_prior)] + names(regression_part$coefs)) + priors <- priors[vapply(priors, is_prior, TRUE)] if (phi_est) { phi <- phi$init @@ -1236,17 +1179,17 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, } else { C <- matrix(0, m, 1) } - theta <- if (length(priors) > 0) sapply(priors, "[[", "init") else numeric(0) + theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, - a1 = a1, P1 = P1, phi = phi, u = u, xreg = xreg, beta = coefs, - D = D, - C = C, + a1 = a1, P1 = P1, phi = phi, u = u, xreg = regression_part$xreg, + beta = regression_part$coefs, D = D, C = C, slope = slope, seasonal = seasonal, noise = noise, period = period, fixed = as.integer(!notfixed), distribution = distribution, initial_mode = initial_mode, - prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, + prior_distributions = priors$prior_distribution, + prior_parameters = priors$parameters, theta = theta, phi_est = phi_est, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), class = c("bsm_ng", "ssm_ung", "nongaussian")) @@ -1317,14 +1260,14 @@ svm <- function(y, mu, rho, sd_ar, sigma) { rownames(T) <- colnames(T) <- rownames(R) <- "signal" priors <- list(rho, sd_ar, if(svm_type==0) sigma else mu) - priors <- priors[!sapply(priors, is.null)] + priors <- priors[!vapply(priors, is.null, TRUE)] names(priors) <- c("rho", "sd_ar", if(svm_type==0) "sigma" else "mu") C <- if (svm_type) matrix(mu$init * (1 - T[1])) else matrix(0) D <- matrix(0) - theta <- if (length(priors) > 0) sapply(priors, "[[", "init") else numeric(0) + theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, @@ -1366,39 +1309,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NUL check_y(y, multivariate = FALSE, distribution) n <- length(y) - if (is.null(xreg)) { - xreg <- matrix(0, 0, 0) - coefs <- numeric(0) - beta <- NULL - } else { - - if (missing(beta) || is.null(beta)) { - stop("No prior defined for beta. ") - } - if(!is_prior(beta) && !is_prior_list(beta)) { - stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") - } - n <- length(y) - if (is.null(dim(xreg)) && length(xreg) == n) { - xreg <- matrix(xreg, n, 1) - } - check_xreg(xreg, n) - nx <- ncol(xreg) - if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] - if(nx > 1) { - coefs <- sapply(beta, "[[", "init") - } else { - coefs <- beta$init - } - check_beta(coefs, nx) - - if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) - } - names(coefs) <- colnames(xreg) - - } - + regression_part <- create_regression(beta, xreg, n) check_rho(rho$init) check_sd(sigma$init, "rho") @@ -1452,19 +1363,20 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NUL priors <- list(rho, sigma, mu, phi, beta) } names(priors) <- - c("rho", "sigma", "mu", "phi", names(coefs)) - priors <- priors[sapply(priors, is_prior)] + c("rho", "sigma", "mu", "phi", names(regression_part$coefs)) + priors <- priors[vapply(priors, is_prior, TRUE)] if (phi_est) { phi <- phi$init } D <- matrix(0) - theta <- if (length(priors) > 0) sapply(priors, "[[", "init") else numeric(0) + theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, - a1 = a1, P1 = P1, phi = phi, u = u, xreg = xreg, beta = coefs, + a1 = a1, P1 = P1, phi = phi, u = u, + regression_part$xreg = xreg, beta = regression_part$coefs, D = D, C = C, initial_mode = initial_mode, distribution = distribution, mu_est = mu_est, phi_est = phi_est, @@ -1497,39 +1409,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { check_y(y) n <- length(y) - if (is.null(xreg)) { - xreg <- matrix(0, 0, 0) - coefs <- numeric(0) - beta <- NULL - } else { - - if (missing(beta) || is.null(beta)) { - stop("No prior defined for beta. ") - } - if(!is_prior(beta) && !is_prior_list(beta)) { - stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") - } - n <- length(y) - if (is.null(dim(xreg)) && length(xreg) == n) { - xreg <- matrix(xreg, n, 1) - } - check_xreg(xreg, n) - nx <- ncol(xreg) - if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] - if(nx > 1) { - coefs <- sapply(beta, "[[", "init") - } else { - coefs <- beta$init - } - check_beta(coefs, nx) - - if (nx > 0 && is.null(colnames(xreg))) { - colnames(xreg) <- paste0("coef_", seq_len(ncol(xreg))) - } - names(coefs) <- colnames(xreg) - - } - + regression_part <- create_regression(beta, xreg, n) check_rho(rho$init) check_sd(sigma$init, "rho") @@ -1568,21 +1448,22 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { if(ncol(xreg) > 1) { - priors <- c(list(rho, sigma, mu, sd_y), beta) + priors <- c(list(rho, sigma, mu, sd_y), regression_part$beta) } else { - priors <- list(rho, sigma, mu, sd_y, beta) + priors <- list(rho, sigma, mu, sd_y, regression_part$beta) } names(priors) <- - c("rho", "sigma", "mu", "sd_y", names(coefs)) - priors <- priors[sapply(priors, is_prior)] + c("rho", "sigma", "mu", "sd_y", names(regression_part$coefs)) + priors <- priors[vapply(priors, is_prior, TRUE)] D <- matrix(0) - theta <- if (length(priors) > 0) sapply(priors, "[[", "init") else numeric(0) + theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, - a1 = a1, P1 = P1, xreg = xreg, beta = coefs, + a1 = a1, P1 = P1, + xreg = regression_part$xreg, beta = regression_part$coefs, D = D, C = C, mu_est = mu_est, sd_y_est = sd_y_est, prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 0196b9f6..a899661b 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -221,16 +221,16 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", } else { alpha <- expand_sample(object, "states") - mean_alpha <- ts(sapply(alpha, colMeans), + mean_alpha <- ts(vapply(alpha, colMeans, numeric(nrow(object$alpha))), start = attr(object, "ts")$start, frequency = attr(object, "ts")$frequency, names = colnames(object$alpha)) - sd_alpha <- sapply(alpha, function(x) apply(x, 2, sd)) + sd_alpha <- vapply(alpha, function(x) apply(x, 2, sd), numeric(nrow(object$alpha))) if(return_se) { - se_alpha <- sapply(alpha, function(x) + se_alpha <- vapply(alpha, function(x) apply(x, 2, function(z) - sqrt(spectrum0.ar(z)$spec / length(z)))) + sqrt(spectrum0.ar(z)$spec / length(z))), numeric(nrow(object$alpha))) ess_alpha <- (sd_alpha / se_alpha)^2 summary_alpha <- list( "Mean" = mean_alpha, "SD" = sd_alpha, diff --git a/R/priors.R b/R/priors.R index 7a23a59c..ddcbd8e1 100644 --- a/R/priors.R +++ b/R/priors.R @@ -120,7 +120,7 @@ combine_priors <- function(x) { if (length(x) == 0) return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) - prior_distributions <- sapply(x, "[[", "prior_distribution") + prior_distributions <- vapply(x, "[[", "prior_distribution", character(1)) parameters <- matrix(NA, 4, length(prior_distributions)) for(i in seq_along(prior_distributions)) { parameters[1:(length(x[[i]])-2), i] <- as.numeric(x[[i]][-(1:2)]) From 1711a62ed5382e3737bf4dd83df49a0d06c2a024 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 13:14:51 +0300 Subject: [PATCH 010/180] shortened long lines, check model components mroe cleanly --- R/approx.R | 20 +- R/as.data.frame.mcmc_output.R | 28 +- R/as_bssm.R | 30 +- R/bootstrap_filter.R | 32 +- R/bssm-package.R | 36 +- R/check_arguments.R | 164 +- R/ekpf_filter.R | 24 +- R/importance_sample.R | 24 +- R/init_mode.R | 3 +- R/kfilter.R | 20 +- R/loglik.R | 66 +- R/models.R | 665 +++--- R/particle_smoother.R | 61 +- R/post_correction.R | 117 +- R/predict.R | 78 +- R/print_mcmc.R | 99 +- R/priors.R | 90 +- R/run_mcmc.R | 234 ++- R/sim_smoother.R | 15 +- R/smoother.R | 17 +- man/ar1_lg.Rd | 74 +- man/as.data.frame.mcmc_output.Rd | 121 +- man/as_bssm.Rd | 68 +- man/bootstrap_filter.Rd | 168 +- man/bssm.Rd | 48 +- man/drownings.Rd | 65 +- man/ekpf_filter.Rd | 74 +- man/exchange.Rd | 56 +- man/gaussian_approx.Rd | 86 +- man/importance_sample.Rd | 118 +- man/kfilter.Rd | 85 +- man/logLik.Rd | 41 +- man/logLik.nongaussian.Rd | 24 +- man/logLik.ssm_nlg.Rd | 90 +- man/logLik.ssm_sde.Rd | 57 +- man/ssm_ulg.Rd | 402 ++-- src/RcppExports.cpp | 2651 ++++++++++++------------ tests/testthat/test_approx.R | 172 +- tests/testthat/test_as_bssm.R | 17 +- tests/testthat/test_basics.R | 36 +- tests/testthat/test_bootstrap_filter.R | 32 +- tests/testthat/test_mcmc.R | 47 +- tests/testthat/test_models.R | 6 +- vignettes/ssm_nlg_template.cpp | 2 +- 44 files changed, 3429 insertions(+), 2934 deletions(-) diff --git a/R/approx.R b/R/approx.R index c42ae0c6..6fe0401b 100644 --- a/R/approx.R +++ b/R/approx.R @@ -8,11 +8,15 @@ #' @param model Model to be approximated. #' @param max_iter Maximum number of iterations. #' @param conv_tol Tolerance parameter. -#' @param iekf_iter For non-linear models, number of iterations in iterated EKF (defaults to 0). +#' @param iekf_iter For non-linear models, number of iterations in iterated EKF +#' (defaults to 0). #' @param ... Ignored. #' @references -#' Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press. -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space +#' Methods. Second edition. Oxford: Oxford University Press. +#' +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' @export #' @rdname gaussian_approx @@ -27,7 +31,8 @@ gaussian_approx <- function(model, max_iter, conv_tol, ...) { #' @rdname gaussian_approx #' @method gaussian_approx nongaussian #' @export -gaussian_approx.nongaussian <- function(model, max_iter = 100, conv_tol = 1e-8, ...) { +gaussian_approx.nongaussian <- function(model, max_iter = 100, + conv_tol = 1e-8, ...) { model$max_iter <- max_iter model$conv_tol <- conv_tol @@ -43,8 +48,8 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, conv_tol = 1e-8, if(length(model$beta) > 0) D <- as.numeric(D) + t(model$xreg %*% model$beta) approx_model <- ssm_ulg(y = out$y, Z = model$Z, H = out$H, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, init_theta = model$theta, - D = D, C = model$C, state_names = names(model$a1), update_fn = model$update_fn, - prior_fn = model$prior_fn) + D = D, C = model$C, state_names = names(model$a1), + update_fn = model$update_fn, prior_fn = model$prior_fn) } else { out$y <- ts(t(out$y), start = start(model$y), end = end(model$y), frequency = frequency(model$y)) @@ -72,7 +77,8 @@ gaussian_approx.ssm_nlg <- function(model, max_iter = 100, as.integer(model$time_varying), max_iter, conv_tol, iekf_iter) - out$y <- ts(t(out$y), start = start(model$y), end = end(model$y), frequency = frequency(model$y)) + out$y <- ts(t(out$y), start = start(model$y), end = end(model$y), + frequency = frequency(model$y)) ssm_mlg(y = out$y, Z = out$Z, H = out$H, T = out$T, R = out$R, a1 = c(out$a1), P1 = out$P1, init_theta = model$theta, D = out$D, C = out$C) diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index 9f3bcd6f..46749161 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -6,13 +6,16 @@ #' @param x Output from \code{\link{run_mcmc}}. #' @param row.names Ignored. #' @param optional Ignored. -#' @param variable Return samples of \code{"theta"} (default) or \code{"states"}? -#' @param times Vector of indices. In case of states, what time points to return? Default is all. -#' @param states Vector of indices. In case of states, what states to return? Default is all. +#' @param variable Return samples of \code{"theta"} (default) or +#' \code{"states"}? +#' @param times Vector of indices. In case of states, +#' what time points to return? Default is all. +#' @param states Vector of indices. In case of states, +#' what states to return? Default is all. #' @param expand Should the jump-chain be expanded? #' Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. #' For \code{expand = FALSE} and always for IS-MCMC, -#' the resulting data.frame contains variable weight (= counts times IS-weights). +#' the resulting data.frame contains variable weight (= counts * IS-weights). #' @param ... Ignored. #' @export #' @examples @@ -45,11 +48,14 @@ as.data.frame.mcmc_output <- function(x, if (expand) { values <- suppressWarnings(expand_sample(x, "theta")) iters <- seq(x$burnin + 1, x$iter, by = x$thin) - weights <- if(x$mcmc_type %in% paste0("is", 1:3)) rep(x$weights, times = x$counts) else 1 + weights <- if(x$mcmc_type %in% paste0("is", 1:3)) { + rep(x$weights, times = x$counts) + } else 1 } else { values <- x$theta iters <- x$burnin + cumsum(x$counts) - weights <- x$counts * (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) + weights <- + x$counts * (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } d <- data.frame(iter = iters, value = as.numeric(values), @@ -59,13 +65,17 @@ as.data.frame.mcmc_output <- function(x, if (missing(times)) times <- seq_len(nrow(x$alpha)) if (missing(states)) states <- seq_len(ncol(x$alpha)) if (expand) { - values <- aperm(x$alpha[times, states, rep(seq_len(nrow(x$theta)), times = x$counts), drop = FALSE], 3:1) + values <- aperm(x$alpha[times, states, + rep(seq_len(nrow(x$theta)), times = x$counts), drop = FALSE], 3:1) iters <- seq(x$burnin + 1, x$iter, by = x$thin) - weights <- if(x$mcmc_type %in% paste0("is", 1:3)) rep(x$weights, times = x$counts) else 1 + weights <- if(x$mcmc_type %in% paste0("is", 1:3)) { + rep(x$weights, times = x$counts) + } else 1 } else { values <- aperm(x$alpha[times, states, , drop = FALSE], 3:1) iters <- x$burnin + cumsum(x$counts) - weights <- x$counts * (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) + weights <- x$counts * + (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } times <- time(ts(seq_len(nrow(x$alpha)), start = attr(x, "ts")$start, diff --git a/R/as_bssm.R b/R/as_bssm.R index cc56b611..a94a6be9 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -1,14 +1,16 @@ #' Convert KFAS Model to bssm Model #' -#' Converts \code{SSModel} object of \code{KFAS} package to general -#' \code{bssm} model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. +#' Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} +#' model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +#' \code{ssm_mng}. #' #' @param model Object of class \code{SSModel}. #' @param kappa For \code{SSModel} object, a prior variance for initial state #' used to replace exact diffuse elements of the original model. #' @param ... Additional arguments to model building functions of \code{bssm} #' (such as prior and updating functions). -#' @return Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. +#' @return Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +#' \code{ssm_mng}. #' @export #' @examples #' library("KFAS") @@ -52,11 +54,14 @@ as_bssm <- function(model, kappa = 100, ...) { if (any(model$distribution != "gaussian")) { if (attr(model, "p") == 1) { - if (model$distribution == "negative binomial" && length(unique(model$u)) > 1) { - stop("Time-varying dispersion parameter for negative binomial is not (yet) supported in 'bssm'.") + if (model$distribution == "negative binomial" && + length(unique(model$u)) > 1) { + stop(paste("Time-varying dispersion parameter for negative binomial", + "is not (yet) supported in 'bssm'.", sep = " ")) } if (model$distribution == "gamma" && length(unique(model$u)) > 1) { - stop("Time-varying shape parameter for gamma is not (yet) supported in 'bssm'.") + stop(paste("Time-varying shape parameter for gamma is not (yet)", + "supported in 'bssm'.", sep = " ")) } switch(model$distribution, @@ -95,19 +100,23 @@ as_bssm <- function(model, kappa = 100, ...) { }, gamma = { if(length(unique(model$u[,i])) > 1) - stop("Time-varying shape parameter for gamma is not (yet) supported in 'bssm'.") + stop(paste0("Time-varying shape parameter for gamma is not", + "(yet) supported in 'bssm'.", sep = " ")) phi[i] <- model$u[1,i] u[,i] <- 1 # no exposure for Gamma in KFAS }, "negative binomial" = { if(length(unique(model$u[,i])) > 1) - stop("Time-varying dispersion parameter for negative binomial is not (yet) supported in 'bssm'.") + stop(paste("Time-varying dispersion parameter for negative", + "binomial is not (yet) supported in 'bssm'.", sep = " ")) phi[i] <- model$u[1,i] u[,i] <- 1 # no exposure for NB in KFAS }, gaussian = { if(length(unique(model$u[,i])) > 1) - stop("Time-varying standard deviation for gaussian distribution with non-gaussian series is not supported in 'bssm'.") + stop(paste("Time-varying standard deviation for gaussian", + "distribution with non-gaussian series is not supported", + "in 'bssm'.", sep = " ")) phi[i] <- sqrt(model$u[1,i]) u[,i] <- 1 }) @@ -121,7 +130,8 @@ as_bssm <- function(model, kappa = 100, ...) { } else { if (attr(model, "p") == 1) { - out <- ssm_ulg(y = model$y, Z = Z, H = sqrt(c(model$H)), T = model$T, R = R, + out <- ssm_ulg(y = model$y, Z = Z, H = sqrt(c(model$H)), T = model$T, + R = R, a1 = c(model$a1), P1 = model$P1, state_names = rownames(model$a1), ...) } else { H <- model$H diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 6b2e1b7c..ae168432 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -1,18 +1,20 @@ #' Bootstrap Filtering #' -#' Function \code{bootstrap_filter} performs a bootstrap filtering with stratification -#' resampling. +#' Function \code{bootstrap_filter} performs a bootstrap filtering with +#' stratification resampling. #' @param model of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}. #' @param particles Number of particles. #' @param seed Seed for RNG. #' @param ... Ignored. -#' @return List with samples (\code{alpha}) from the filtering distribution and corresponding weights (\code{weights}), -#' as well as filtered and predicted states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, \code{Ptt}), -#' and estimated log-likelihood (\code{logLik}). +#' @return List with samples (\code{alpha}) from the filtering distribution and +#' corresponding weights (\code{weights}), as well as filtered and predicted +#' states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, +#' \code{Ptt}), and estimated log-likelihood (\code{logLik}). #' @export #' @references #' Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113. +#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +#' IEE Proceedings-F, 140, 107–113. #' @rdname bootstrap_filter bootstrap_filter <- function(model, particles, ...) { UseMethod("bootstrap_filter", model) @@ -40,9 +42,11 @@ bootstrap_filter.gaussian <- function(model, particles, out <- bsf(model, particles, seed, TRUE, model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- - colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- names(model$a1) + colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- + names(model$a1) out$at <- ts(out$at, start = start(model$y), frequency = frequency(model$y)) - out$att <- ts(out$att, start = start(model$y), frequency = frequency(model$y)) + out$att <- ts(out$att, start = start(model$y), + frequency = frequency(model$y)) rownames(out$alpha) <- names(model$a1) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out @@ -65,7 +69,8 @@ bootstrap_filter.nongaussian <- function(model, particles, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste0("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } @@ -77,7 +82,8 @@ bootstrap_filter.nongaussian <- function(model, particles, out <- bsf(model, particles, seed, FALSE, model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- - colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- names(model$a1) + colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- + names(model$a1) out$at <- ts(out$at, start = start(model$y), frequency = frequency(model$y)) out$att <- ts(out$att, start = start(model$y), frequency = frequency(model$y)) rownames(out$alpha) <- names(model$a1) @@ -93,7 +99,8 @@ bootstrap_filter.ssm_nlg <- function(model, particles, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } @@ -124,7 +131,8 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } diff --git a/R/bssm-package.R b/R/bssm-package.R index 15cb307d..d682c89c 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -1,15 +1,25 @@ #' Bayesian Inference of State Space Models #' -#' This package contains functions for Bayesian inference of basic stochastic volatility model -#' and exponential family state space models, where the state equation is linear and Gaussian, -#' and the conditional observation density is either Gaussian, Poisson, -#' binomial, negative binomial or Gamma density. General non-linear Gaussian models and models -#' with continuous SDE dynamics are also supported. For formal definition of the -#' currently supported models and methods, as well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, -#' see the package vignettes and Vihola, Helske, Franks (2020). +#' This package contains functions for efficient Bayesian inference of state +#' space models, where model is assumed to be either +#' * Exponential family state space models, where the state equation is linear +#' Gaussian, and the conditional observation density is either Gaussian, +#' Poisson, binomial, negative binomial or Gamma density. +#' * Basic stochastic volatility model. +#' * General non-linear model with Gaussian noise terms. +#' * Model with continuous SDE dynamics. +#' For formal definition of the currently supported models and methods, as +#' well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, +#' see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package +#' vignettes. #' #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Helske J, Vihola M (2021). “bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R.” 2101.08492, +#' . +#' +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' #' @docType package @@ -17,7 +27,8 @@ #' @aliases bssm #' @importFrom Rcpp evalCpp #' @importFrom coda mcmc -#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start time ts ts.union tsp tsp<- sd na.omit +#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start +#' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm NULL #' Deaths by drowning in Finland in 1969-2019 @@ -31,7 +42,8 @@ NULL #' @name drownings #' @docType data #' @format A time series object containing 51 observations. -#' @source Statistics Finland \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. +#' @source Statistics Finland +#' \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. #' @keywords datasets #' @examples #' data("drownings") @@ -54,8 +66,8 @@ NULL #' @format A vector of length 945. #' @source \url{http://www.ssfpack.com/DKbook.html}. #' @keywords datasets -#' @references James Durbin, Siem Jan Koopman (2012). "Time Series Analysis by State Space Methods". -#' Oxford University Press. +#' @references James Durbin, Siem Jan Koopman (2012). +#' Time Series Analysis by State Space Methods. Oxford University Press. #' @examples #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), diff --git a/R/check_arguments.R b/R/check_arguments.R index dbbfc90d..4e3ab96a 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -10,11 +10,14 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { stop("Argument y must be a numeric vector or ts object.") } if(distribution != "gaussian" && any(na.omit(x) < 0)) { - stop(paste0("Negative values not allowed for ", distribution, " distribution. ")) + stop(paste0("Negative values not allowed for ", distribution, + " distribution. ")) } else { if(distribution %in% - c("negative binomial", "binomial", "poisson") && any(na.omit(x != as.integer(x)))) { - stop(paste0("Non-integer values not allowed for ", distribution, " distribution. ")) + c("negative binomial", "binomial", "poisson") && + any(na.omit(x != as.integer(x)))) { + stop(paste0("Non-integer values not allowed for ", distribution, + " distribution. ")) } } } @@ -31,11 +34,14 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { check_distribution <- function(x, distribution) { for(i in seq_len(ncol(x))) { if(distribution[i] != "gaussian" && any(na.omit(x[,i]) < 0)) { - stop(paste0("Negative values not allowed for ", distribution[i], " distribution. ")) + stop(paste0("Negative values not allowed for ", distribution[i], + " distribution. ")) } else { if(distribution[i] %in% - c("negative binomial", "binomial", "poisson") && any(na.omit(x[,i] != as.integer(x[,i])))) { - stop(paste0("Non-integer values not allowed for ", distribution[i], " distribution. ")) + c("negative binomial", "binomial", "poisson") && + any(na.omit(x[,i] != as.integer(x[,i])))) { + stop(paste0("Non-integer values not allowed for ", distribution[i], + " distribution. ")) } } } @@ -76,7 +82,8 @@ check_xreg <- function(x, n) { check_beta <- function(x, k) { if (length(x) != k) { - stop("Number of coefficients in beta is not equal to the number of columns of xreg.") + stop(paste("Number of coefficients in beta is not equal to the number", + "of columns of xreg.", sep = " ")) } if (any(!is.finite(x))) { stop("Argument 'beta' must contain only finite values. ") @@ -138,22 +145,37 @@ check_target <- function(target) { } } + check_D <- function(x, p, n) { - if(p == 1) { - if (!(length(x) %in% c(1,n))) { - stop("'D' must be a scalar or length n, where n is the number of observations.") - } + if(missing(x)) { + if(p == 1) 0 else matrix(0, p, 1) } else { - if (is.null(dim(x)) || nrow(x) != p || !(ncol(x) %in% c(1,n))) { - stop("'D' must be p x 1 or p x n matrix, where p is the number of series.") - } + if(p == 1) { + if (!(length(x) %in% c(1, n))) { + stop(paste("'D' must be a scalar or length n, where n is the number of", + "observations.", sep = " ")) + x <- as.numeric(x) + } + } else { + if (is.null(dim(x)) || nrow(x) != p || !(ncol(x) %in% c(1, n))) { + stop(paste("'D' must be p x 1 or p x n matrix, where p is the number", + "of series.", sep = " ")) + } + } } + x } check_C <- function(x, m, n) { - if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1,n))) { - stop("'C' must be m x 1 or m x n matrix, where m is the number of states.") - } + if(missing(x)) { + matrix(0, m, 1) + } else { + if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1,n))) { + stop(paste("'C' must be m x 1 or m x n matrix, where m is", + "the number of states.", sep = " ")) + } + } + x } create_regression <- function(beta, xreg, n) { @@ -164,7 +186,8 @@ create_regression <- function(beta, xreg, n) { stop("No prior defined for beta. ") } else { if(!is_prior(beta) && !is_prior_list(beta)) { - stop("Prior for beta must be of class 'bssm_prior' or 'bssm_prior_list.") + stop(paste("Prior for beta must be of class 'bssm_prior' or", + "'bssm_prior_list.", sep = " " )) } else { if (is.null(dim(xreg)) && length(xreg) == n) { dim(xreg) <- c(n, 1) @@ -186,4 +209,107 @@ create_regression <- function(beta, xreg, n) { } list(xreg = xreg, coefs = coefs, beta = beta) } -} \ No newline at end of file +} + +check_Z <- function(x, p, n) { + if(p == 1) { + if (length(x) == 1) { + dim(x) <- c(1, 1) + } else { + if (!(dim(x)[2] %in% c(1, NA, n))) { + stop(paste("'Z' must be a (m x 1) or (m x n) matrix, where", + "m is the number of states and n is the length of the series. ", + sep = " ")) + } else { + dim(x) <- + c(dim(x)[1], (n - 1) * (max(dim(x)[2], 0, na.rm = TRUE) > 1) + 1) + } + } + } else { + if (dim(x)[1] != p || !(dim(x)[3] %in% c(1, NA, n))) { + stop(paste("'Z' must be a (p x m) matrix or (p x m x n) array", + "where p is the number of series, m is the number of states,", + "and n is the length of the series. ", sep = " ")) + } else { + dim(x) <- + c(p, dim(x)[2], (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) + } + } + x +} + +check_T <- function(x, m, n) { + if (length(x) == 1 && m == 1) { + dim(x) <- c(1, 1, 1) + } else { + if ((length(x) == 1) || any(dim(x)[1:2] != m) || + !(dim(x)[3] %in% c(1, NA, n))) { + stop(paste("'T' must be a (m x m) matrix, (m x m x 1) or", + "(m x m x n) array, where m is the number of states. ", sep = " ")) + } + dim(x) <- c(m, m, (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) + } + x +} + +check_R <- function(x, m, n) { + if (length(x) == m) { + dim(x) <- c(m, 1, 1) + } else { + if (!(dim(x)[1] == m) || dim(x)[2] > m || !dim(x)[3] %in% c(1, NA, n)) { + stop(paste("'R' must be a (m x k) matrix, (m x k x 1) or", + "(m x k x n) array, where k<=m is the number of disturbances eta,", + "and m is the number of states. ", sep = " ")) + } else { + dim(x) <- + c(m, dim(x)[2], (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) + } + } + x +} + +check_a1 <- function(x, m) { + if (missing(x)) { + x <- numeric(m) + } else { + if (length(x) == 1 || length(x) == m) { + x <- rep(x, length.out = m) + } else { + stop(paste("Misspecified a1, argument a1 must be a vector of length m,", + "where m is the number of state_names and 1<=t<=m.", sep = " ")) + } + } + x +} + +check_P1 <- function(x, m) { + if (missing(x)) { + x <- matrix(0, m, m) + } else { + if (length(x) == 1 && m == 1) { + dim(x) <- c(1, 1) + } else { + if (!identical(dim(x), c(m, m))) + stop(paste("Argument P1 must be (m x m) matrix, where m is the number", + "of states. ", sep = " ")) + } + } + x +} + +check_H <- function(x, p, n) { + if(p == 1) { + if (!(length(x) %in% c(1, n))) { + stop(paste("'H' must be a scalar or length n, where n is the length of", + "the time series y", sep = " ")) + } else x <- as.numeric(x) + } else { + if (any(dim(x)[1:2] != p) || !(dim(x)[3] %in% c(1, n, NA))) { + stop(paste("'H' must be p x p matrix or p x p x n array, where p is the", + "number of series and n is the length of the series.", sep = " ")) + } else { + dim(x) <- c(p, p, (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) + } + } + x +} diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 2b381799..3a22ce40 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -1,15 +1,17 @@ #' Extended Kalman Particle Filtering #' -#' Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification -#' resampling, based on Van Der Merwe et al (2001). +#' Function \code{ekpf_filter} performs a extended Kalman particle filtering +#' with stratification resampling, based on Van Der Merwe et al (2001). #' #' @param object of class \code{ssm_nlg}. #' @param particles Number of particles. #' @param seed Seed for RNG. #' @param ... Ignored. -#' @return A list containing samples, filtered estimates and the corresponding covariances, -#' weights, and an estimate of log-likelihood. -#' @references Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. In Advances in neural information processing systems (pp. 584-590). +#' @return A list containing samples, filtered estimates and the +#' corresponding covariances, weights, and an estimate of log-likelihood. +#' @references Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. +#' (2001). The unscented particle filter. In Advances in neural +#' information processing systems (pp. 584-590). #' @export #' @rdname ekpf_filter ekpf_filter <- function(object, particles, ...) { @@ -18,12 +20,14 @@ ekpf_filter <- function(object, particles, ...) { #' @method ekpf_filter ssm_nlg #' @export #' @rdname ekpf_filter -ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$integer.max, size = 1), ...) { +ekpf_filter.ssm_nlg <- function(object, particles, + seed = sample(.Machine$integer.max, size = 1), ...) { if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument", + "`particles` instead.", sep = " ")) particles <- nsim } } @@ -37,8 +41,10 @@ ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$intege colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- rownames(out$alpha) <- object$state_names - out$at <- ts(out$at, start = start(object$y), frequency = frequency(object$y)) - out$att <- ts(out$att, start = start(object$y), frequency = frequency(object$y)) + out$at <- ts(out$at, start = start(object$y), + frequency = frequency(object$y)) + out$att <- ts(out$att, start = start(object$y), + frequency = frequency(object$y)) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out } diff --git a/R/importance_sample.R b/R/importance_sample.R index 83bc7c84..1b60eeb5 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -1,15 +1,18 @@ #' Importance Sampling from non-Gaussian State Space Model #' -#' Returns \code{nsim} samples from the approximating Gaussian model with corresponding -#' (scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages. +#' Returns \code{nsim} samples from the approximating Gaussian model with +#' corresponding (scaled) importance weights. +#' Probably mostly useful for comparing KFAS and bssm packages. #' -#' @param model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}. +#' @param model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +#' \code{ssm_ung}, or \code{ssm_mng}. #' @param nsim Number of samples. #' @param use_antithetic Logical. If \code{TRUE} (default), use antithetic -#' variable for location in simulation smoothing. Ignored for \code{ssm_mng} models. +#' variable for location in simulation smoothing. Ignored for \code{ssm_mng} +#' models. #' @param max_iter Maximum number of iterations used for the approximation. -#' @param conv_tol Convergence threshold for the approximation. Approximation is -#' claimed to be converged when the mean squared difference of the modes is +#' @param conv_tol Convergence threshold for the approximation. Approximation +#' is claimed to be converged when the mean squared difference of the modes is #' less than \code{conv_tol}. #' @param seed Seed for the random number generator. #' @param ... Ignored. @@ -17,7 +20,8 @@ #' @rdname importance_sample #' @examples #' data("sexratio", package = "KFAS") -#' model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, u = sexratio[, "Total"], +#' model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, +#' u = sexratio[, "Total"], #' distribution = "binomial") #' #' imp <- importance_sample(model, nsim = 1000) @@ -38,14 +42,16 @@ importance_sample <- function(model, nsim, use_antithetic, #' @rdname importance_sample #' @export importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, - max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1), ...) { + max_iter = 100, conv_tol = 1e-8, + seed = sample(.Machine$integer.max, size = 1), ...) { model$max_iter <- max_iter model$conv_tol <- conv_tol model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 - out <- importance_sample_ng(model, nsim, use_antithetic, seed, model_type(model)) + out <- importance_sample_ng(model, nsim, use_antithetic, seed, + model_type(model)) rownames(out$alpha) <- names(model$a1) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out diff --git a/R/init_mode.R b/R/init_mode.R index 0ee78eff..0d3d209b 100644 --- a/R/init_mode.R +++ b/R/init_mode.R @@ -24,7 +24,8 @@ init_mode <- function(y, u, distribution) { gaussian = { }, - stop("Argument distribution must be 'poisson', 'binomial', 'gamma', 'gaussian', or 'negative binomial'.") + stop(paste("Argument distribution must be 'poisson', 'binomial', 'gamma',", + "'gaussian', or 'negative binomial'.", sep = " ")) ) y } diff --git a/R/kfilter.R b/R/kfilter.R index b40259e6..6892c952 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -4,14 +4,15 @@ #' and returns the filtered estimates and one-step-ahead predictions of the #' states \eqn{\alpha_t} given the data up to time \eqn{t}. #' -#' For non-Gaussian models, the filtering is based on the approximate Gaussian model. +#' For non-Gaussian models, the filtering is based on the approximate +#' Gaussian model. #' #' @param model Model Model object. #' @param ... Ignored. -#' @return List containing the log-likelihood (approximate in non-Gaussian case), -#' one-step-ahead predictions \code{at} and filtered -#' estimates \code{att} of states, and the corresponding variances \code{Pt} and -#' \code{Ptt}. +#' @return List containing the log-likelihood +#' (approximate in non-Gaussian case), one-step-ahead predictions \code{at} +#' and filtered estimates \code{att} of states, and the corresponding +#' variances \code{Pt} and \code{Ptt}. #' @seealso \code{\link{bootstrap_filter}} #' @export #' @rdname kfilter @@ -30,7 +31,8 @@ kfilter.gaussian <- function(model, ...) { out <- gaussian_kfilter(model, model_type = model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- - colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- names(model$a1) + colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- + names(model$a1) out$at <- ts(out$at, start = start(model$y), frequency = frequency(model$y)) out$att <- ts(out$att, start = start(model$y), frequency = frequency(model$y)) out @@ -69,7 +71,8 @@ ekf <- function(model, iekf_iter = 0) { as.integer(model$time_varying), iekf_iter) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- - colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- model$state_names + colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- + model$state_names out$at <- ts(out$at, start = start(model$y), frequency = frequency(model$y)) out$att <- ts(out$att, start = start(model$y), frequency = frequency(model$y)) @@ -101,7 +104,8 @@ ukf <- function(model, alpha = 1, beta = 0, kappa = 2) { alpha, beta, kappa) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- - colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- model$state_names + colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- + model$state_names out$at <- ts(out$at, start = start(model$y), frequency = frequency(model$y)) out$att <- ts(out$att, start = start(model$y), frequency = frequency(model$y)) diff --git a/R/loglik.R b/R/loglik.R index 353e327b..32edc3b9 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -1,6 +1,7 @@ #' Log-likelihood of a Gaussian State Space Model #' -#' Computes the log-likelihood of a linear-Gaussian state space model of \code{bssm} package. +#' Computes the log-likelihood of a linear-Gaussian state space model of +#' \code{bssm} package. #' #' @param object Model model. #' @param ... Ignored. @@ -17,21 +18,30 @@ logLik.gaussian <- function(object, ...) { #' Log-likelihood of a Non-Gaussian State Space Model #' -#' Computes the log-likelihood of a non-Gaussian state space model of \code{bssm} package. +#' Computes the log-likelihood of a non-Gaussian state space model of +#' \code{bssm} package. #' #' @param object Model model. -#' @param particles Number of samples for particle filter or importance sampling. If 0, +#' @param particles Number of samples for particle filter or +#' importance sampling. If 0, #' approximate log-likelihood based on the Gaussian approximation is returned. -#' @param method Sampling method, default is psi-auxiliary filter (\code{"psi"}), -#' other choices are \code{"bsf"} bootstrap particle filter, and \code{"spdk"}, -#' which uses the importance sampling approach by Shephard and Pitt (1997) and -#' Durbin and Koopman (1997). -#' @param max_iter Maximum number of iterations for Gaussian approximation algorithm. +#' @param method Sampling method, default is psi-auxiliary filter +#' (\code{"psi"}). Other choices are \code{"bsf"} bootstrap particle filter, +#' and \code{"spdk"}, which uses the importance sampling approach by +#' Shephard and Pitt (1997) and Durbin and Koopman (1997). +#' @param max_iter Maximum number of iterations for Gaussian approximation +#' algorithm. #' @param conv_tol Tolerance parameter for the approximation algorithm. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @method logLik nongaussian #' @export +#' @references +#' Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation +#' Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. +#' +#' Shephard, N., & Pitt, M. (1997). Likelihood Analysis of +#' Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. #' @examples #' model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, #' distribution = "poisson") @@ -43,7 +53,8 @@ logLik.gaussian <- function(object, ...) { #' logLik(model, particles = 10, seed = 1) #' logLik(model2, particles = 10, seed = 1) logLik.nongaussian <- function(object, particles, method = "psi", - max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1),...) { + max_iter = 100, conv_tol = 1e-8, + seed = sample(.Machine$integer.max, size = 1),...) { object$max_iter <- max_iter object$conv_tol <- conv_tol @@ -51,14 +62,16 @@ logLik.nongaussian <- function(object, particles, method = "psi", if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } method <- match.arg(method, c("psi", "bsf", "spdk")) method <- pmatch(method, c("psi", "bsf", "spdk")) - if (method == 2 && particles == 0) stop("'particles' must be positive for bootstrap filter.") + if (method == 2 && particles == 0) + stop("'particles' must be positive for bootstrap filter.") object$distribution <- pmatch(object$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), @@ -68,19 +81,23 @@ logLik.nongaussian <- function(object, particles, method = "psi", } #' Log-likelihood of a Non-linear State Space Model #' -#' Computes the log-likelihood of a state space model of class \code{ssm_nlg} package. +#' Computes the log-likelihood of a state space model of class +#' \code{ssm_nlg} package. #' #' @param object Model model. #' @param particles Number of samples for particle filter. If 0, -#' approximate log-likelihood is returned either based on the Gaussian approximation or EKF, -#' depending on the \code{method} argument. -#' @param method Sampling method. Default is the bootstrap particle filter (\code{"bsf"}). -#' Other choices are \code{"psi"} which uses psi-auxiliary filter -#' (or approximating Gaussian model in the case of \code{particles = 0}), and \code{"ekf"} which -#' uses EKF-based particle filter (or just EKF approximation in the case of \code{particles = 0}). -#' @param max_iter Maximum number of iterations for gaussian approximation algorithm. +#' approximate log-likelihood is returned either based on the Gaussian +#' approximation or EKF, depending on the \code{method} argument. +#' @param method Sampling method. Default is the bootstrap particle filter +#' (\code{"bsf"}). Other choices are \code{"psi"} which uses +#' psi-auxiliary filter (or approximating Gaussian model in the case of +#' \code{particles = 0}), and \code{"ekf"} which uses EKF-based particle +#' filter (or just EKF approximation in the case of \code{particles = 0}). +#' @param max_iter Maximum number of iterations for the gaussian approximation +#' algorithm. #' @param conv_tol Tolerance parameter for the approximation algorithm. -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is used with +#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter +#' is used with #' \code{iekf_iter} iterations in place of standard EKF. Defaults to zero. #' @param seed Seed for the random number generator. #' @param ... Ignored. @@ -93,7 +110,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } @@ -112,7 +130,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", } #' Log-likelihood of a State Space Model with SDE dynamics #' -#' Computes the log-likelihood of a state space model of class \code{ssm_sde} package. +#' Computes the log-likelihood of a state space model of class +#' \code{ssm_sde} package. #' #' @param object Model model. #' @param particles Number of samples for particle filter. @@ -127,7 +146,8 @@ logLik.ssm_sde <- function(object, particles, L, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } diff --git a/R/models.R b/R/models.R index 3c7d51e8..47eea35d 100644 --- a/R/models.R +++ b/R/models.R @@ -4,40 +4,49 @@ default_update_fn <- function(theta) {} #' #' General univariate linear-Gaussian state space models #' -#' Construct an object of class \code{ssm_ulg} by directly defining the corresponding terms of -#' the model. +#' Construct an object of class \code{ssm_ulg} by directly defining the +#' corresponding terms of the model. #' #' The general univariate linear-Gaussian model is defined using the following #' observational and state equations: #' -#' \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, (\textrm{observation equation})} -#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} +#' \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +#' (\textrm{observation equation})} +#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +#' (\textrm{transition equation})} #' #' where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and #' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -#' Here k is the number of disturbance terms which can be less than m, the number of states. +#' Here k is the number of disturbance terms which can be less than m, the +#' number of states. #' #' The \code{update_fn} function should take only one #' vector argument which is used to create list with elements named as -#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +#' and \code{C}, #' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. theta. +#' If any of these components is missing, it is assumed to be constant wrt. +#' theta. #' Note that while you can input say R as m x k matrix for \code{ssm_ulg}, #' \code{update_fn} should return R as m x k x 1 in this case. -#' It might be useful to first construct the model without updating function and then check -#' the expected structure of the model components from the output. +#' It might be useful to first construct the model without updating function +#' and then check the expected structure of the model components from the +#' output. #' #' @param y Observations as time series (or vector) of length \eqn{n}. -#' @param Z System matrix Z of the observation equation as m x 1 or m x n matrix. -#' @param H Vector of standard deviations. Either a scalar or a vector of length n. +#' @param Z System matrix Z of the observation equation as m x 1 or +#' m x n matrix. +#' @param H Vector of standard deviations. Either a scalar or a vector of +#' length n. #' @param T System matrix T of the state equation. Either a m x m matrix or a #' m x m x n array. -#' @param R Lower triangular matrix R the state equation. Either a m x k matrix or a -#' m x k x n array. +#' @param R Lower triangular matrix R the state equation. Either a +#' m x k matrix or a m x k x n array. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. #' @param init_theta Initial values for the unknown hyperparameters theta. -#' @param D Intercept terms for observation equation, given as a length n vector. +#' @param D Intercept terms for observation equation, given as a +#' length n vector. #' @param C Intercept terms for state equation, given as m x n matrix. #' @param update_fn Function which returns list of updated model #' components given input vector theta. See details. @@ -132,7 +141,8 @@ default_update_fn <- function(theta) {} #' out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) #' out #' -#' # Above the regression coefficients are modelled as time-invariant latent states. +#' # Above the regression coefficients are modelled as +#' # time-invariant latent states. #' # Here is an alternative way where we use variable D so that the #' # coefficients are part of parameter vector theta: #' @@ -165,75 +175,31 @@ default_update_fn <- function(theta) {} #' out2 #' } ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), - D, C, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { + D, C, state_names, update_fn = default_update_fn, + prior_fn = default_prior_fn) { check_y(y) n <- length(y) - if (length(Z) == 1) { - dim(Z) <- c(1, 1) - m <- 1 - } else { - if (!(dim(Z)[2] %in% c(1, NA, n))) - stop("Argument Z must be a (m x 1) or (m x n) matrix, - where m is the number of states and n is the length of the series. ") - m <- dim(Z)[1] - dim(Z) <- c(m, (n - 1) * (max(dim(Z)[2], 0, na.rm = TRUE) > 1) + 1) - } + # create Z + Z <- check_Z(Z, 1L, n) + m <- dim(Z)[1] + # create T - if (length(T) == 1 && m == 1) { - dim(T) <- c(1, 1, 1) - } else { - if ((length(T) == 1) || any(dim(T)[1:2] != m) || !(dim(T)[3] %in% c(1, NA, n))) - stop("Argument T must be a (m x m) matrix, (m x m x 1) or (m x m x n) array, where m is the number of states. ") - dim(T) <- c(m, m, (n - 1) * (max(dim(T)[3], 0, na.rm = TRUE) > 1) + 1) - } + T <- check_T(T, m, n) # create R - if (length(R) == m) { - dim(R) <- c(m, 1, 1) - k <- 1 - } else { - if (!(dim(R)[1] == m) || dim(R)[2] > m || !dim(R)[3] %in% c(1, NA, n)) - stop("Argument R must be a (m x k) matrix, (m x k x 1) or (m x k x n) array, where k<=m is the number of disturbances eta, and m is the number of states. ") - k <- dim(R)[2] - dim(R) <- c(m, k, (n - 1) * (max(dim(R)[3], 0, na.rm = TRUE) > 1) + 1) - } + R <- check_R(R) + k <- dim(R)[2] - # create a1 - if (missing(a1)) { - a1 <- rep(0, m) - } else { - if (length(a1) <= m) { - a1 <- rep(a1, length.out = m) - } else stop("Misspecified a1, argument a1 must be a vector of length m, where m is the number of state_names and 1<=t<=m.") - } - # create P1 - if (missing(P1)) { - P1 <- matrix(0, m, m) - } else { - if (length(P1) == 1 && m == 1) { - dim(P1) <- c(1, 1) - } else { - if (any(dim(P1)[1:2] != m)) - stop("Argument P1 must be (m x m) matrix, where m is the number of states. ") - } - } - if (!missing(D)) { - check_D(D, 1L, n) - D <- as.numeric(D) - } else { - D <- 0 - } - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } + a1 <- check_a1(a1, m) + + P1 <- check_P1(P1, m, m) - if (length(H)[3] %in% c(1, n)) - stop("Argument H must be a scalar or a vector of length n, where n is the length of the time series y.") + D <- check_D(D, 1L, n) + C <- check_C(C, m, n) + H <- check_H(H, 1L, n) if (missing(state_names)) { state_names <- paste("state", 1:m) @@ -253,47 +219,57 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), } #' General univariate non-Gaussian state space model #' -#' Construct an object of class \code{ssm_ung} by directly defining the corresponding terms of -#' the model. +#' Construct an object of class \code{ssm_ung} by directly defining the +#' corresponding terms of the model. #' #' The general univariate non-Gaussian model is defined using the following #' observational and state equations: #' #' \deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} +#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +#' (\textrm{transition equation})} #' #' where \eqn{\eta_t \sim N(0, I_k)} and #' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, -#' and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or negative binomial distribution. -#' Here k is the number of disturbance terms which can be less than m, the number of states. +#' and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or +#' negative binomial distribution. +#' Here k is the number of disturbance terms which can be less than m, +#' the number of states. #' #' The \code{update_fn} function should take only one #' vector argument which is used to create list with elements named as -#' \code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +#' \code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +#' and \code{C}, #' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. theta. +#' If any of these components is missing, it is assumed to be constant +#' wrt. theta. #' Note that while you can input say R as m x k matrix for \code{ssm_ung}, #' \code{update_fn} should return R as m x k x 1 in this case. -#' It might be useful to first construct the model without updating function and then check -#' the expected structure of the model components from the output. +#' It might be useful to first construct the model without updating function +#' and then check the expected structure of the model components from +#' the output. #' #' @param y Observations as time series (or vector) of length \eqn{n}. -#' @param Z System matrix Z of the observation equation. Either a vector of length m, +#' @param Z System matrix Z of the observation equation. Either a +#' vector of length m, #' a m x n matrix, or object which can be coerced to such. #' @param T System matrix T of the state equation. Either a m x m matrix or a #' m x m x n array, or object which can be coerced to such. -#' @param R Lower triangular matrix R the state equation. Either a m x k matrix or a -#' m x k x n array, or object which can be coerced to such. +#' @param R Lower triangular matrix R the state equation. Either +#' a m x k matrix or a m x k x n array, or object which can be coerced to such. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param distribution Distribution of the observed time series. Possible choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}. +#' @param distribution Distribution of the observed time series. Possible +#' choices are +#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +#' \code{"negative binomial"}. #' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for gamma distribution -#' this is the shape parameter, and for other distributions this is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -#' negative binomial distribution, this corresponds to the offset term. For binomial, -#' this is the number of trials. +#' For negative binomial distribution this is the dispersion term, for +#' gamma distribution this is the shape parameter, and for other +#' distributions this is ignored. +#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' gamma, and negative binomial distribution, this corresponds to the offset +#' term. For binomial, this is the number of trials. #' @param state_names Names for the states. #' @param C Intercept terms \eqn{C_t} for the state equation, given as a #' m times 1 or m times n matrix. @@ -325,68 +301,21 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, check_y(y, distribution = distribution) n <- length(y) + Z <- check_Z(Z, 1L, n) + m <- dim(Z)[1] - if (length(Z) == 1) { - dim(Z) <- c(1, 1) - m <- 1 - } else { - if (!(dim(Z)[2] %in% c(1, NA, n))) - stop("Argument Z must be a vector of length m, or (m x 1) or (m x n) matrix, - where m is the number of states and n is the length of the series. ") - m <- dim(Z)[1] - dim(Z) <- c(m, (n - 1) * (max(dim(Z)[2], 0, na.rm = TRUE) > 1) + 1) - } - # create T - if (length(T) == 1 && m == 1) { - dim(T) <- c(1, 1, 1) - } else { - if ((length(T) == 1) || any(dim(T)[1:2] != m) || !(dim(T)[3] %in% c(1, NA, n))) - stop("Argument T must be a (m x m) matrix, (m x m x 1) or (m x m x n) array, where m is the number of states. ") - dim(T) <- c(m, m, (n - 1) * (max(dim(T)[3], 0, na.rm = TRUE) > 1) + 1) - } + T <- check_T(T, m, n) # create R - if (length(R) == m) { - dim(R) <- c(m, 1, 1) - k <- 1 - } else { - if (!(dim(R)[1] == m) || dim(R)[2] > m || !dim(R)[3] %in% c(1, NA, n)) - stop("Argument R must be a (m x k) matrix, (m x k x 1) or (m x k x n) array, where k<=m is the number of disturbances eta, and m is the number of states. ") - k <- dim(R)[2] - dim(R) <- c(m, k, (n - 1) * (max(dim(R)[3], 0, na.rm = TRUE) > 1) + 1) - } + R <- check_R(R) + k <- dim(R)[2] - # create a1 - if (missing(a1)) { - a1 <- rep(0, m) - } else { - if (length(a1) <= m) { - a1 <- rep(a1, length.out = m) - } else stop("Misspecified a1, argument a1 must be a vector of length m, where m is the number of state_names and 1<=t<=m.") - } - # create P1 - if (missing(P1)) { - P1 <- matrix(0, m, m) - } else { - if (length(P1) == 1 && m == 1) { - dim(P1) <- c(1, 1) - } else { - if (any(dim(P1)[1:2] != m)) - stop("Argument P1 must be (m x m) matrix, where m is the number of states. ") - } - } + a1 <- check_a1(a1, m) - if (!missing(D)) { - check_D(D, 1L, n) - D <- as.numeric(D) - } else { - D <- 0 - } - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } + P1 <- check_P1(P1, m, m) + + D <- check_D(D, 1L, n) + C <- check_C(C, m, n) check_phi(phi) @@ -418,14 +347,16 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' General multivariate linear Gaussian state space models #' -#' Construct an object of class \code{ssm_mlg} by directly defining the corresponding terms of -#' the model. +#' Construct an object of class \code{ssm_mlg} by directly defining the +#' corresponding terms of the model. #' -#' The general multivariate linear-Gaussian model is defined using the following -#' observational and state equations: +#' The general multivariate linear-Gaussian model is defined using the +#' following observational and state equations: #' -#' \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, (\textrm{observation equation})} -#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} +#' \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +#' (\textrm{observation equation})} +#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +#' (\textrm{transition equation})} #' #' where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and #' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. @@ -434,20 +365,25 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' #' The \code{update_fn} function should take only one #' vector argument which is used to create list with elements named as -#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +#' and \code{C}, #' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. theta. +#' If any of these components is missing, it is assumed to be +#' constant wrt. theta. #' Note that while you can input say R as m x k matrix for \code{ssm_mlg}, #' \code{update_fn} should return R as m x k x 1 in this case. #' It might be useful to first construct the model without updating function #' -#' @param y Observations as multivariate time series or matrix with dimensions n x p. -#' @param Z System matrix Z of the observation equation as p x m matrix or p x m x n array. -#' @param H Lower triangular matrix H of the observation. Either a scalar or a vector of length n. +#' @param y Observations as multivariate time series or matrix with +#' dimensions n x p. +#' @param Z System matrix Z of the observation equation as p x m matrix or +#' p x m x n array. +#' @param H Lower triangular matrix H of the observation. Either a scalar or +#' a vector of length n. #' @param T System matrix T of the state equation. Either a m x m matrix or a #' m x m x n array. -#' @param R Lower triangular matrix R the state equation. Either a m x k matrix or a -#' m x k x n array. +#' @param R Lower triangular matrix R the state equation. Either a m x k matrix +#' or a m x k x n array. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. #' @param init_theta Initial values for the unknown hyperparameters theta. @@ -456,9 +392,11 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' @param update_fn Function which returns list of updated model #' components given input vector theta. This function should take only one #' vector argument which is used to create list with elements named as -#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +#' and \code{C}, #' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. theta. +#' If any of these components is missing, it is assumed to be +#' onstant wrt. theta. #' @param prior_fn Function which returns log of prior density #' given input vector theta. #' @param state_names Names for the states. @@ -472,7 +410,8 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) #' ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), - D, C, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { + D, C, state_names, update_fn = default_update_fn, + prior_fn = default_prior_fn) { # create y check_y(y, multivariate = TRUE) @@ -480,68 +419,22 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), p <- ncol(y) # create Z - if (dim(Z)[1] != p || !(dim(Z)[3] %in% c(1, NA, n))) - stop("Argument Z must be a (p x m) matrix or (p x m x n) array - where p is the number of series, m is the number of states, and n is the length of the series. ") + Z <- check_Z(Z, p, n) m <- dim(Z)[2] - dim(Z) <- c(p, m, (n - 1) * (max(dim(Z)[3], 0, na.rm = TRUE) > 1) + 1) - # create T - if (length(T) == 1 && m == 1) { - dim(T) <- c(1, 1, 1) - } else { - if ((length(T) == 1) || any(dim(T)[1:2] != m) || !(dim(T)[3] %in% c(1, NA, n))) - stop("Argument T must be a (m x m) matrix, (m x m x 1) or (m x m x n) array, where m is the number of states. ") - dim(T) <- c(m, m, (n - 1) * (max(dim(T)[3], 0, na.rm = TRUE) > 1) + 1) - } + T <- check_T(T, m, n) # create R - if (length(R) == m) { - dim(R) <- c(m, 1, 1) - k <- 1 - } else { - if (!(dim(R)[1] == m) || dim(R)[2] > m || !dim(R)[3] %in% c(1, NA, n)) - stop("Argument R must be a (m x k) matrix, (m x k x 1) or (m x k x n) array, where k<=m is the number of disturbances eta, and m is the number of states. ") - k <- dim(R)[2] - dim(R) <- c(m, k, (n - 1) * (max(dim(R)[3], 0, na.rm = TRUE) > 1) + 1) - } + R <- check_R(R) + k <- dim(R)[2] - # create a1 - if (missing(a1)) { - a1 <- rep(0, m) - } else { - if (length(a1) <= m) { - a1 <- rep(a1, length.out = m) - } else stop("Misspecified a1, argument a1 must be a vector of length m, where m is the number of state_names and 1<=t<=m.") - } - # create P1 - if (missing(P1)) { - P1 <- matrix(0, m, m) - } else { - if (length(P1) == 1 && m == 1) { - dim(P1) <- c(1, 1) - } else { - if (any(dim(P1)[1:2] != m)) - stop("Argument P1 must be (m x m) matrix, where m is the number of states. ") - } - } + a1 <- check_a1(a1, m) + P1 <- check_P1(P1, m, m) - if (!missing(D)) { - check_D(D, p, n) - } else { - D <- matrix(0, p, 1) - } - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } - - # create H - if (any(dim(H)[1:2] != p) || !(dim(H)[3] %in% c(1, n, NA))) - stop("Argument H must be a p x p matrix or a p x p x n array.") - dim(H) <- c(p, p, (n - 1) * (max(dim(H)[3], 0, na.rm = TRUE) > 1) + 1) + D <- check_D(D, p, n) + C <- check_C(C, m, n) + H <- check_H(H, p, n) if (missing(state_names)) { state_names <- paste("state", 1:m) @@ -560,35 +453,42 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), #' General Non-Gaussian State Space Model #' -#' Construct an object of class \code{ssm_mng} by directly defining the corresponding terms of -#' the model. +#' Construct an object of class \code{ssm_mng} by directly defining the +#' corresponding terms of the model. #' #' The general multivariate non-Gaussian model is defined using the following #' observational and state equations: #' #' \deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} +#' \deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +#' (\textrm{transition equation})} #' #' where \eqn{\eta_t \sim N(0, I_k)} and -#' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and \eqn{p^i(y_t | .)} -#' is either Poisson, binomial, gamma, Gaussian, or negative binomial distribution for -#' each observation series \eqn{i=1,...,p}.Here k is the number of disturbance terms -#' (which can be less than m, the number of states). +#' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and +#' \eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or +#' negative binomial distribution for each observation series \eqn{i=1,...,p}. +#' Here k is the number of disturbance terms (which can be less than m, +#' the number of states). #' -#' @param y Observations as multivariate time series or matrix with dimensions n x p. -#' @param Z System matrix Z of the observation equation as p x m matrix or p x m x n array. +#' @param y Observations as multivariate time series or matrix with dimensions +#' n x p. +#' @param Z System matrix Z of the observation equation as p x m matrix or +#' p x m x n array. #' @param T System matrix T of the state equation. Either a m x m matrix or a #' m x m x n array. -#' @param R Lower triangular matrix R the state equation. Either a m x k matrix or a +#' @param R Lower triangular matrix R the state equation. Either a m x k +#' matrix or a #' m x k x n array. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param distribution vector of distributions of the observed series. Possible choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, \code{"gamma"}, -#' and \code{"gaussian"}. +#' @param distribution vector of distributions of the observed series. +#' Possible choices are +#' \code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, +#' \code{"gamma"}, and \code{"gaussian"}. #' @param phi Additional parameters relating to the non-Gaussian distributions. -#' For negative binomial distribution this is the dispersion term, for gamma distribution -#' this is the shape parameter, for Gaussian this is standard deviation, +#' For negative binomial distribution this is the dispersion term, for +#' gamma distribution this is the shape parameter, for Gaussian this is +#' standard deviation, #' and for other distributions this is ignored. #' @param u Constant parameter for non-Gaussian models. For Poisson, gamma, #' and negative binomial distribution, this corresponds to the offset term. @@ -602,7 +502,8 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), #' \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and #' \code{phi}, #' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. theta. +#' If any of these components is missing, it is assumed to be constant wrt. +#' theta. #' @param prior_fn Function which returns log of prior density #' given input vector theta. #' @param state_names Names for the states. @@ -626,70 +527,28 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, check_phi(phi[i]) } - # create Z - if (dim(Z)[1] != p || !(dim(Z)[3] %in% c(1, NA, n))) - stop("Argument Z must be a (p x m) matrix or (p x m x n) array - where p is the number of series, m is the number of states, and n is the length of the series. ") + Z <- check_Z(Z, p, n) m <- dim(Z)[2] - dim(Z) <- c(p, m, (n - 1) * (max(dim(Z)[3], 0, na.rm = TRUE) > 1) + 1) - # create T - if (length(T) == 1 && m == 1) { - dim(T) <- c(1, 1, 1) - } else { - if ((length(T) == 1) || any(dim(T)[1:2] != m) || !(dim(T)[3] %in% c(1, NA, n))) - stop("Argument T must be a (m x m) matrix, (m x m x 1) or (m x m x n) array, where m is the number of states. ") - dim(T) <- c(m, m, (n - 1) * (max(dim(T)[3], 0, na.rm = TRUE) > 1) + 1) - } + T <- check_T(T, m, n) # create R - if (length(R) == m) { - dim(R) <- c(m, 1, 1) - k <- 1 - } else { - if (!(dim(R)[1] == m) || dim(R)[2] > m || !dim(R)[3] %in% c(1, NA, n)) - stop("Argument R must be a (m x k) matrix, (m x k x 1) or (m x k x n) array, where k<=m is the number of disturbances eta, and m is the number of states. ") - k <- dim(R)[2] - dim(R) <- c(m, k, (n - 1) * (max(dim(R)[3], 0, na.rm = TRUE) > 1) + 1) - } + R <- check_R(R) + k <- dim(R)[2] - # create a1 - if (missing(a1)) { - a1 <- rep(0, m) - } else { - if (length(a1) <= m) { - a1 <- rep(a1, length.out = m) - } else stop("Misspecified a1, argument a1 must be a vector of length m, where m is the number of state_names and 1<=t<=m.") - } - # create P1 - if (missing(P1)) { - P1 <- matrix(0, m, m) - } else { - if (length(P1) == 1 && m == 1) { - dim(P1) <- c(1, 1) - } else { - if (any(dim(P1)[1:2] != m)) - stop("Argument P1 must be (m x m) matrix, where m is the number of states. ") - } - } + a1 <- check_a1(a1, m) + P1 <- check_P1(P1, m, m) - if (!missing(D)) { - check_D(D, p, n) - } else { - D <- matrix(0, p, 1) - } - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } + D <- check_D(D, p, n) + C <- check_C(C, m, n) if (length(u) == 1) { u <- matrix(u, n, p) } check_u(u) - if(!identical(dim(y), dim(u))) stop("Dimensions of 'y' and 'u' do not match. ") + if(!identical(dim(y), dim(u))) + stop("Dimensions of 'y' and 'u' do not match. ") initial_mode <- y for(i in 1:p) { initial_mode[, i] <- init_mode(y[, i], u[, i], distribution[i]) @@ -705,8 +564,8 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, names(init_theta) <- paste0("theta_", seq_along(init_theta)) - structure(list(y = as.ts(y), Z = Z, T = T, R = R, a1 = a1, P1 = P1, phi = phi, u = u, - D = D, C = C, distribution = distribution, + structure(list(y = as.ts(y), Z = Z, T = T, R = R, a1 = a1, P1 = P1, + phi = phi, u = u, D = D, C = C, distribution = distribution, initial_mode = initial_mode, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), @@ -714,8 +573,8 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, } #' Basic Structural (Time Series) Model #' -#' Constructs a basic structural model with local level or local trend component -#' and seasonal component. +#' Constructs a basic structural model with local level or local trend +#' component and seasonal component. #' #' @param y Vector or a \code{\link{ts}} object of observations. #' @param sd_y A fixed value or prior for the standard error of @@ -736,7 +595,8 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' @param P1 Prior covariance for the initial states (level, slope, seasonals). #' Default is diagonal matrix with 1000 on the diagonal. #' @param D,C Intercept terms for observation and -#' state equations, given as a length n vector and m times n matrix respectively. +#' state equations, given as a length n vector and m times n matrix +#' respectively (or scalar and m times 1 matrix). #' @return Object of class \code{bsm_lg}. #' @export #' @examples @@ -822,24 +682,15 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, m <- as.integer(1L + as.integer(slope) + as.integer(seasonal) * (period - 1)) - if (missing(a1)) { - a1 <- numeric(m) - } else { - if (length(a1) != m) { - stop("Argument a1 must be a vector of length ", m) - } - } + a1 <- check_a1(a1, m) + if (missing(P1)) { P1 <- diag(100, m) } else { - if (is.null(dim(P1)) && length(P1) == 1L) { - P1 <- matrix(P1) - } - if (!identical(dim(P1), c(m, m))) { - stop("Argument P1 must be m x m matrix, where m = ", m) - } + P1 <- check_P1(P1, m, m) } + if (slope) { state_names <- c("level", "slope", seasonal_names) } else { @@ -903,26 +754,21 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, names(regression_part$coefs)) priors <- priors[vapply(priors, is_prior, TRUE)] - if (!missing(D)) { - check_D(D, 1L, n) - } else { - D <- matrix(0) - } - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } - theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) + D <- check_D(D, 1L, n) + C <- check_C(C, m, n) + + theta <- if (length(priors) > 0) { + vapply(priors, "[[", "init", FUN.VALUE = 1) + } else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, - a1 = a1, P1 = P1, xreg = regression_part$xreg, beta = regression_part$coefs, - D = D, - C = C, + a1 = a1, P1 = P1, xreg = regression_part$xreg, + beta = regression_part$coefs, D = D, C = C, slope = slope, seasonal = seasonal, period = period, fixed = as.integer(!notfixed), - prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, + prior_distributions = priors$prior_distribution, + prior_parameters = priors$parameters, theta = theta), class = c("bsm_lg", "ssm_ulg", "gaussian")) } @@ -942,15 +788,19 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' of the noise in seasonal equation. See \link[=uniform]{priors} for details. #' If missing, the seasonal component is omitted from the model. #' @param sd_noise Prior for the standard error of the additional noise term. -#' See \link[=uniform]{priors} for details. If missing, no additional noise term is used. -#' @param distribution Distribution of the observed time series. Possible choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}. +#' See \link[=uniform]{priors} for details. If missing, no additional noise +#' term is used. +#' @param distribution Distribution of the observed time series. Possible +#' choices are +#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +#' \code{"negative binomial"}. #' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for gamma distribution -#' this is the shape parameter, and for other distributions this is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -#' negative binomial distribution, this corresponds to the offset term. For binomial, -#' this is the number of trials. +#' For negative binomial distribution this is the dispersion term, for +#' gamma distribution this is the shape parameter, and for other distributions +#' this is ignored. +#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' gamma, and negative binomial distribution, this corresponds to the offset +#' term. For binomial, this is the number of trials. #' @param beta Prior for the regression coefficients. #' @param xreg Matrix containing covariates. #' @param period Length of the seasonal component i.e. the number of @@ -1057,25 +907,15 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, npar_R <- 1L + as.integer(slope) + as.integer(seasonal) + as.integer(noise) - m <- as.integer(1L + as.integer(slope) + as.integer(seasonal) * (period - 1) + as.integer(noise)) + m <- as.integer(1L + as.integer(slope) + + as.integer(seasonal) * (period - 1) + as.integer(noise)) - if (missing(a1)) { - a1 <- numeric(m) - } else { - if (length(a1) != m) { - stop("Argument a1 must be a vector of length ", m) - } - } + a1 <- check_a1(a1, m) if (missing(P1)) { P1 <- diag(100, m) } else { - if (is.null(dim(P1)) && length(P1) == 1L) { - P1 <- matrix(P1) - } - if (!identical(dim(P1), c(m, m))) { - stop("Argument P1 must be m x m matrix, where m = ", m) - } + P1 <- check_P1(P1, m, m) } if (slope) { @@ -1172,14 +1012,12 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, phi <- phi$init } - D <- matrix(0) + D <- numeric(1) + C <- check_C(C, m, n) - if (!missing(C)) { - check_C(C, m, n) - } else { - C <- matrix(0, m, 1) - } - theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) + theta <- if (length(priors) > 0) { + vapply(priors, "[[", "init", FUN.VALUE = 1) + } else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, @@ -1204,11 +1042,13 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' @param mu Prior for mu parameter of transition equation. #' @param rho prior for autoregressive coefficient. #' @param sd_ar Prior for the standard deviation of noise of the AR-process. -#' @param sigma Prior for sigma parameter of observation equation, internally denoted as phi. Ignored -#' if \code{mu} is provided. Note that typically parametrization using mu is preferred due to -#' better numerical properties and availability of better Gaussian approximation. -#' Most notably the global approximation approach does not work with sigma parameterization as -#' sigma is not a parameter of the resulting approximate model. +#' @param sigma Prior for sigma parameter of observation equation, internally +#' denoted as phi. Ignored if \code{mu} is provided. Note that typically +#' parametrization using mu is preferred due to better numerical properties and +#' availability of better Gaussian approximation. +#' Most notably the global approximation approach does not work with sigma +#' parameterization as sigma is not a parameter of the resulting approximate +#' model. #' @return Object of class \code{svm}. #' @export #' @rdname svm @@ -1224,7 +1064,8 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' sd_ar = halfnormal(pars[2],sd=5), #' sigma = halfnormal(pars[3],sd=2)), particles = 0) #' } -#' opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), upper = c(0.999,10,10)) +#' opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), +#' upper = c(0.999,10,10)) #' pars <- opt$par #' model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), #' sd_ar = halfnormal(pars[2],sd=5), @@ -1247,7 +1088,8 @@ svm <- function(y, mu, rho, sd_ar, sigma) { } else { svm_type <- 0L check_sd(sigma$init, "sigma", FALSE) - initial_mode <- matrix(log(pmax(1e-4, y^2)) - 2 * log(sigma$init), ncol = 1) + initial_mode <- + matrix(log(pmax(1e-4, y^2)) - 2 * log(sigma$init), ncol = 1) } a1 <- if(svm_type) mu$init else 0 P1 <- matrix(sd_ar$init^2 / (1 - rho$init^2)) @@ -1267,41 +1109,51 @@ svm <- function(y, mu, rho, sd_ar, sigma) { C <- if (svm_type) matrix(mu$init * (1 - T[1])) else matrix(0) D <- matrix(0) - theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) + theta <- if (length(priors) > 0) { + vapply(priors, "[[", "init", FUN.VALUE = 1) + } else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, - a1 = a1, P1 = P1, phi = if (svm_type == 0) sigma$init else 1, xreg = matrix(0, 0, 0), + a1 = a1, P1 = P1, phi = if (svm_type == 0) sigma$init else 1, + xreg = matrix(0, 0, 0), beta = numeric(0), D = D, C = C, initial_mode = initial_mode, - svm_type = svm_type, distribution = "svm", u = 1, phi_est = !as.logical(svm_type), - prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, + svm_type = svm_type, distribution = "svm", u = 1, + phi_est = !as.logical(svm_type), + prior_distributions = priors$prior_distribution, + prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), class = c("svm", "ssm_ung", "nongaussian")) } #' Non-Gaussian model with AR(1) latent process #' -#' Constructs a simple non-Gaussian model where the state dynamics follow an AR(1) process. +#' Constructs a simple non-Gaussian model where the state dynamics follow an +#' AR(1) process. #' #' @param y Vector or a \code{\link{ts}} object of observations. #' @param rho prior for autoregressive coefficient. -#' @param mu A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0. +#' @param mu A fixed value or a prior for the stationary mean of the latent +#' AR(1) process. Parameter is omitted if this is set to 0. #' @param sigma Prior for the standard deviation of noise of the AR-process. #' @param beta Prior for the regression coefficients. #' @param xreg Matrix containing covariates. -#' @param distribution Distribution of the observed time series. Possible choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}. +#' @param distribution Distribution of the observed time series. Possible +#' choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +#' \code{"negative binomial"}. #' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for gamma distribution -#' this is the shape parameter, and for other distributions this is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -#' negative binomial distribution, this corresponds to the offset term. For binomial, -#' this is the number of trials. +#' For negative binomial distribution this is the dispersion term, for gamma +#' distribution this is the shape parameter, and for other distributions this +#' is ignored. +#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' gamma, and negative binomial distribution, this corresponds to the offset +#' term. For binomial, this is the number of trials. #' @return Object of class \code{ar1_ng}. #' @export #' @rdname ar1_ng -ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) { +ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, + xreg = NULL) { distribution <- match.arg(distribution, c("poisson", "binomial", "negative binomial", "gamma")) @@ -1345,7 +1197,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NUL if (length(u) != n) { u <- rep(u, length.out = n) } - + initial_mode <- matrix(init_mode(y, u, distribution), ncol = 1) P1 <- matrix(sigma$init^2 / (1 - rho$init^2)) @@ -1371,27 +1223,31 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NUL } D <- matrix(0) - theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) + theta <- if (length(priors) > 0) { + vapply(priors, "[[", "init", FUN.VALUE = 1) + } else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, T = T, R = R, a1 = a1, P1 = P1, phi = phi, u = u, - regression_part$xreg = xreg, beta = regression_part$coefs, + xreg = regression_part$xreg, beta = regression_part$coefs, D = D, C = C, initial_mode = initial_mode, distribution = distribution, mu_est = mu_est, phi_est = phi_est, - prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, - theta = theta, + prior_distributions = priors$prior_distribution, + prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), class = c("ar1_ng", "ssm_ung", "nongaussian")) } #' Univariate Gaussian model with AR(1) latent process #' -#' Constructs a simple Gaussian model where the state dynamics follow an AR(1) process. +#' Constructs a simple Gaussian model where the state dynamics +#' follow an AR(1) process. #' #' @param y Vector or a \code{\link{ts}} object of observations. #' @param rho prior for autoregressive coefficient. -#' @param mu A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0. +#' @param mu A fixed value or a prior for the stationary mean of the latent +#' AR(1) process. Parameter is omitted if this is set to 0. #' @param sigma Prior for the standard deviation of noise of the AR-process. #' @param sd_y Prior for the standard deviation of observation equation. #' @param beta Prior for the regression coefficients. @@ -1458,7 +1314,9 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { D <- matrix(0) - theta <- if (length(priors) > 0) vapply(priors, "[[", "init", FUN.VALUE = 1) else numeric(0) + theta <- if (length(priors) > 0) { + vapply(priors, "[[", "init", FUN.VALUE = 1) + } else numeric(0) priors <- combine_priors(priors) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, @@ -1466,8 +1324,8 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { xreg = regression_part$xreg, beta = regression_part$coefs, D = D, C = C, mu_est = mu_est, sd_y_est = sd_y_est, - prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, - theta = theta, + prior_distributions = priors$prior_distribution, + prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8), class = c("ar1_lg", "ssm_ulg", "gaussian")) } @@ -1475,23 +1333,27 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' #' General multivariate nonlinear Gaussian state space models #' -#' Constructs an object of class \code{ssm_nlg} by defining the corresponding terms -#' of the observation and state equation. +#' Constructs an object of class \code{ssm_nlg} by defining the corresponding +#' terms of the observation and state equation. #' #' The nonlinear Gaussian model is defined as #' -#' \deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, (\textrm{observation equation})} -#' \deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, (\textrm{transition equation})} +#' \deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, +#' (\textrm{observation equation})} +#' \deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, +#' (\textrm{transition equation})} #' #' where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and #' \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions -#' \eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector \eqn{\theta}. +#' \eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector +#' \eqn{\theta}. #' #' Compared to other models, these general models need a bit more effort from #' the user, as you must provide the several small C++ snippets which define the #' model structure. See examples in the vignette. #' -#' @param y Observations as multivariate time series (or matrix) of length \eqn{n}. +#' @param y Observations as multivariate time series (or matrix) of length +#' \eqn{n}. #' @param Z,H,T,R An external pointers for the C++ functions which #' define the corresponding model functions. #' @param Z_gn,T_gn An external pointers for the C++ functions which @@ -1499,13 +1361,16 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. #' @param theta Parameter vector passed to all model functions. -#' @param known_params Vector of known parameters passed to all model functions. -#' @param known_tv_params Matrix of known parameters passed to all model functions. +#' @param known_params Vector of known parameters passed to all model +#' functions. +#' @param known_tv_params Matrix of known parameters passed to all model +#' functions. #' @param n_states Number of states in the model. #' @param n_etas Dimension of the noise term of the transition equation. #' @param log_prior_pdf An external pointer for the C++ function which #' computes the log-prior density given theta. -#' @param time_varying Optional logical vector of length 4, denoting whether the values of +#' @param time_varying Optional logical vector of length 4, denoting whether +#' the values of #' Z, H, T, and R vary with respect to time variable (given identical states). #' If used, this can speed up some computations. #' @param state_names Names for the states. @@ -1513,7 +1378,8 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @export ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, known_params = NA, known_tv_params = matrix(NA), n_states, n_etas, - log_prior_pdf, time_varying = rep(TRUE, 4), state_names = paste0("state",1:n_states)) { + log_prior_pdf, time_varying = rep(TRUE, 4), + state_names = paste0("state",1:n_states)) { if (is.null(dim(y))) { dim(y) <- c(length(y), 1) @@ -1541,15 +1407,18 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' as well as the log-density of observation equation. We assume that the #' observations are measured at integer times (missing values are allowed). #' -#' As in case of \code{ssm_nlg} models, these general models need a bit more effort from -#' the user, as you must provide the several small C++ snippets which define the -#' model structure. See vignettes for an example. +#' As in case of \code{ssm_nlg} models, these general models need a bit more +#' effort from the user, as you must provide the several small C++ snippets +#' which define the model structure. See vignettes for an example. #' -#' @param y Observations as univariate time series (or vector) of length \eqn{n}. -#' @param drift,diffusion,ddiffusion An external pointers for the C++ functions which +#' @param y Observations as univariate time series (or vector) of length +#' \eqn{n}. +#' @param drift,diffusion,ddiffusion An external pointers for the C++ functions +#' which #' define the drift, diffusion and derivative of diffusion functions of SDE. #' @param obs_pdf An external pointer for the C++ function which -#' computes the observational log-density given the the states and parameter vector theta. +#' computes the observational log-density given the the states and parameter +#' vector theta. #' @param prior_pdf An external pointer for the C++ function which #' computes the prior log-density given the parameter vector theta. #' @param theta Parameter vector passed to all model functions. diff --git a/R/particle_smoother.R b/R/particle_smoother.R index caaf4691..32c069d3 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -1,7 +1,8 @@ #' Particle Smoothing #' #' Function \code{particle_smoother} performs particle smoothing -#' based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary particle filter (\eqn{\psi}-APF) [2], +#' based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary +#' particle filter (\eqn{\psi}-APF) [2], #' or extended Kalman particle filter [3] (or its iterated version [4]). #' The smoothing phase is based on the filter-smoother algorithm by [5]. #' @@ -12,30 +13,44 @@ #' @param particles Number of samples for particle filter. #' @param method Choice of particle filter algorithm. #' For Gaussian and non-Gaussian models with linear dynamics, -#' options are \code{"bsf"} (bootstrap particle filter, default for non-linear models) +#' options are \code{"bsf"} (bootstrap particle filter, default for +#' non-linear models) #' and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and #' for non-linear models options \code{"ekf"} (extended Kalman particle filter) #' is also available. -#' @param max_iter Maximum number of iterations used in Gaussian approximation. Used \eqn{\psi}-APF. -#' @param conv_tol Tolerance parameter used in Gaussian approximation. Used \eqn{\psi}-APF. +#' @param max_iter Maximum number of iterations used in Gaussian approximation. +#' Used \eqn{\psi}-APF. +#' @param conv_tol Tolerance parameter used in Gaussian approximation. +#' Used \eqn{\psi}-APF. #' @param iekf_iter If zero (default), first approximation for non-linear #' Gaussian models is obtained from extended Kalman filter. If #' \code{iekf_iter > 0}, iterated extended Kalman filter is used with #' \code{iekf_iter} iterations. #' @param seed Seed for RNG. #' @param ... Ignored. -#' @return List with samples (\code{alpha}) from the smoothing distribution and corresponding weights (\code{weights}), -#' as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) of the states and +#' @return List with samples (\code{alpha}) from the smoothing distribution +#' and corresponding weights (\code{weights}), +#' as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) +#' of the states and #' estimated log-likelihood (\code{logLik}). #' @references #' [1] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113. -#' [2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +#' IEE Proceedings-F, 140, 107–113. +#' +#' [2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -#' [3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. +#' +#' [3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). +#' The unscented particle filter. #' In Advances in neural information processing systems (pp. 584-590). -#' [4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. Academic Press. -#' [5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. +#' +#' [4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. +#' Academic Press. +#' +#' [5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian +#' nonlinear state space models. #' Journal of Computational and Graphical Statistics, 5, 1–25. #' @export #' @rdname particle_smoother @@ -53,7 +68,8 @@ particle_smoother <- function(model, particles, ...) { #' model <- ssm_ulg(y, Z = 1, T = 1, R = 1, H = 1, P1 = 1) #' system.time(out <- particle_smoother(model, particles = 1000)) #' # same with simulation smoother: -#' system.time(out2 <- sim_smoother(model, particles = 1000, use_antithetic = TRUE)) +#' system.time(out2 <- sim_smoother(model, particles = 1000, +#' use_antithetic = TRUE)) #' ts.plot(out$alphahat, rowMeans(out2), col = 1:2) #' particle_smoother.gaussian <- function(model, particles, method = "psi", @@ -62,19 +78,23 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } if(method == "psi") { out <- list() - out$alpha <- gaussian_psi_smoother(model, particles, seed, model_type(model)) + out$alpha <- gaussian_psi_smoother(model, particles, seed, + model_type(model)) out$alphahat <- t(apply(out$alpha, 1:2, mean)) if(ncol(out$alphahat) == 1L) { - out$Vt <- array(apply(out$alpha[1, , ], 1, var), c(1, 1, nrow(out$alphahat))) + out$Vt <- array(apply(out$alpha[1, , ], 1, var), + c(1, 1, nrow(out$alphahat))) } else { - out$Vt <- array(NA, c(ncol(out$alphahat), ncol(out$alphahat), nrow(out$alphahat))) + out$Vt <- array(NA, c(ncol(out$alphahat), ncol(out$alphahat), + nrow(out$alphahat))) for(i in seq_len(nrow(out$alphahat))) { out$Vt[,, i] <- cov(t(out$alpha[,i,])) } @@ -103,7 +123,8 @@ particle_smoother.nongaussian <- function(model, particles, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } @@ -142,7 +163,8 @@ particle_smoother.ssm_nlg <- function(model, particles, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } @@ -191,7 +213,8 @@ particle_smoother.ssm_sde <- function(model, particles, L, if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } diff --git a/R/post_correction.R b/R/post_correction.R index 026f7e00..b787273d 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -5,31 +5,37 @@ get_map <- function(x) { #' Suggest Number of Particles for \eqn{\psi}-APF Post-correction #' -#' Function \code{estimate_N} estimates suitable number particles needed for accurate -#' post-correction of approximate MCMC +#' Function \code{estimate_N} estimates suitable number particles needed for +#' accurate post-correction of approximate MCMC. #' #' Function \code{suggest_N} estimates the standard deviation of the #' logarithm of the post-correction weights at approximate MAP of theta, #' using various particle sizes and suggest smallest number of particles -#' which still leads standard deviation less than 1. Similar approach was suggested in -#' the context of pseudo-marginal MCMC by Doucet et al. (2015), but see also -#' Section 10.3 in Vihola et al (2020). +#' which still leads standard deviation less than 1. Similar approach was +#' suggested in the context of pseudo-marginal MCMC by Doucet et al. (2015), +#' but see also Section 10.3 in Vihola et al (2020). #' #' @param model Model of class \code{nongaussian} or \code{ssm_nlg}. -#' @param mcmc_output An output from \code{run_mcmc} used to compute the MAP estimate of theta. -#' While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., +#' @param mcmc_output An output from \code{run_mcmc} used to compute the MAP +#' estimate of theta. While the intended use assumes this is from +#' approximate MCMC, it is not actually checked, i.e., #' it is also possible to input previous (asymptotically) exact output. -#' @param candidates Vector containing the candidate number of particles to test. Default -#' is \code{seq(10, 100, by = 10)}. -#' @param replications How many replications should be used for computing the standard deviations? -#' Default is 100. +#' @param candidates Vector containing the candidate number of particles to +#' test. Default is \code{seq(10, 100, by = 10)}. +#' @param replications How many replications should be used for computing +#' the standard deviations? Default is 100. #' @param seed Seed for the random number generator. -#' @return List with suggested number of particles \code{N} and matrix containing -#' estimated standard deviations of the log-weights and corresponding number of particles. -#' @references A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, -#' Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, -#' Biometrika, Volume 102, Issue 2, 2015, Pages 295–313, https://doi.org/10.1093/biomet/asu075 -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' @return List with suggested number of particles \code{N} and matrix +#' containing estimated standard deviations of the log-weights and +#' corresponding number of particles. +#' @references +#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +#' Efficient implementation of Markov chain Monte Carlo when using an +#' unbiased likelihood estimator, Biometrika, 102, 2, 2015, Pages 295–313, +#' https://doi.org/10.1093/biomet/asu075 +#' +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples @@ -67,15 +73,17 @@ get_map <- function(x) { suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), replications = 100, seed = sample(.Machine$integer.max, size = 1)) { - if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") + if (!inherits(mcmc_output, "mcmc_output")) + stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") theta <- get_map(mcmc_output) if (inherits(model, "nongaussian")) { model$distribution <- pmatch(model$distribution, - c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), - duplicates.ok = TRUE) - 1 + c("svm", "poisson", "binomial", "negative binomial", "gamma", + "gaussian"), duplicates.ok = TRUE) - 1 - out <- suggest_n_nongaussian(model, theta, candidates, replications, seed, model_type(model)) + out <- suggest_n_nongaussian(model, theta, candidates, replications, seed, + model_type(model)) } else { if (inherits(model, "ssm_nlg")) { out <- suggest_n_nonlinear(t(model$y), model$Z, model$H, model$T, @@ -84,35 +92,48 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), model$known_tv_params, model$n_states, model$n_etas, as.integer(model$time_varying), theta, candidates, replications, seed) - } else stop("Function 'suggest_N' is only available for models of class 'nongaussian' and 'nlg_ssm'.") + } else + stop(paste("Function 'suggest_N' is only available for models of", + "class 'nongaussian' and 'nlg_ssm'.", sep = " ")) } - list(N = candidates[which(out < 1)[1]], results = data.frame(N = candidates, sd = out)) + list(N = candidates[which(out < 1)[1]], results = data.frame(N = candidates, + sd = out)) } #' Run Post-correction for Approximate MCMC using \eqn{\psi}-APF #' -#' Function \code{post_correct} updates previously obtained approximate MCMC output -#' with post-correction weights leading to asymptotically exact weighted posterior, -#' and returns updated MCMC output where components \code{weights}, \code{posterior}, -#' \code{alpha}, \code{alphahat}, and \code{Vt} are updated (depending on the original output type). +#' Function \code{post_correct} updates previously obtained approximate MCMC +#' output with post-correction weights leading to asymptotically exact +#' weighted posterior, and returns updated MCMC output where components +#' \code{weights}, \code{posterior}, \code{alpha}, \code{alphahat}, and +#' \code{Vt} are updated (depending on the original output type). #' #' @param model Model of class \code{nongaussian} or \code{ssm_nlg}. -#' @param mcmc_output An output from \code{run_mcmc} used to compute the MAP estimate of theta. -#' While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., -#' it is also possible to input previous (asymptotically) exact output. +#' @param mcmc_output An output from \code{run_mcmc} used to compute the MAP +#' estimate of theta. +#' While the intended use assumes this is from approximate MCMC, it is not +#' actually checked, i.e., it is also possible to input previous +#' (asymptotically) exact output. #' @param particles Number of particles for \eqn{\psi}-APF. #' @param threads Number of parallel threads. #' @param is_type Type of IS-correction. Possible choices are -#'\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), +#'\code{"is3"} for simple importance sampling (weight is computed for each +#'MCMC iteration independently), #' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of particles used for -#' weight computations is proportional to the length of the jump chain block. +#' \code{"is1"} for importance sampling type weighting where the number of +#' particles used forweight computations is proportional to the length of the +#' jump chain block. #' @param seed Seed for the random number generator. -#' @return List with suggested number of particles \code{N} and matrix containing -#' estimated standard deviations of the log-weights and corresponding number of particles. -#' @references A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, -#' Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, -#' Biometrika, Volume 102, Issue 2, 2015, Pages 295–313, https://doi.org/10.1093/biomet/asu075 -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' @return List with suggested number of particles \code{N} and matrix +#' containing estimated standard deviations of the log-weights and +#' corresponding number of particles. +#' @references +#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +#' Efficient implementation of Markov chain Monte Carlo when using an unbiased +#' likelihood estimator. Biometrika, 102, 2, 2015, Pages 295–313, +#' https://doi.org/10.1093/biomet/asu075 +#' +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples @@ -188,19 +209,21 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), post_correct <- function(model, mcmc_output, particles, threads = 1L, is_type = "is2", seed = sample(.Machine$integer.max, size = 1)) { - if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") + if (!inherits(mcmc_output, "mcmc_output")) + stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") is_type <- pmatch(match.arg(is_type, paste0("is", 1:3)), paste0("is", 1:3)) a <- proc.time() if (inherits(model, "nongaussian")) { model$distribution <- pmatch(model$distribution, - c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), - duplicates.ok = TRUE) - 1 + c("svm", "poisson", "binomial", "negative binomial", "gamma", + "gaussian"), duplicates.ok = TRUE) - 1 out <- postcorrection_nongaussian(model, model_type(model), mcmc_output$output_type, particles, - seed, threads, is_type, mcmc_output$counts, t(mcmc_output$theta), mcmc_output$modes) + seed, threads, is_type, mcmc_output$counts, t(mcmc_output$theta), + mcmc_output$modes) } else { if (inherits(model, "ssm_nlg")) { out <- postcorrection_nonlinear(t(model$y), model$Z, model$H, model$T, @@ -212,7 +235,9 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, particles, seed, threads, is_type, mcmc_output$counts, t(mcmc_output$theta), mcmc_output$modes) - } else stop("Function 'post_correct' is only available for models of class 'nongaussian' and 'ssm_nlg'.") + } else + stop(paste("Function 'post_correct' is only available for models of", + "class 'nongaussian' and 'ssm_nlg'.", sep = " ")) } mcmc_output$weights <- out$weights mcmc_output$posterior <- mcmc_output$posterior + out$posterior @@ -223,8 +248,8 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, if (mcmc_output$output_type == 2) { mcmc_output$alphahat <- out$alphahat mcmc_output$Vt <- out$Vt - colnames(mcmc_output$alphahat) <- colnames(mcmc_output$Vt) <- rownames(mcmc_output$Vt) <- - names(model$a1) + colnames(mcmc_output$alphahat) <- colnames(mcmc_output$Vt) <- + rownames(mcmc_output$Vt) <- names(model$a1) mcmc_output$alphahat <- ts(mcmc_output$alphahat, start = start(model$y), frequency = frequency(model$y)) } diff --git a/R/predict.R b/R/predict.R index a3fd06b4..6cd2c02b 100644 --- a/R/predict.R +++ b/R/predict.R @@ -1,9 +1,10 @@ #' Predictions for State Space Models #' -#' Draw samples from the posterior predictive distribution for future time points -#' given the posterior draws of hyperparameters \eqn{\theta} and \eqn{alpha_{n+1}}. -#' Function can also be used to draw samples from the posterior predictive distribution -#' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. +#' Draw samples from the posterior predictive distribution for future +#' time points given the posterior draws of hyperparameters \eqn{\theta} and +#' \eqn{alpha_{n+1}}. Function can also be used to draw samples from the +#' posterior predictive distribution +#' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. #' #' @param object mcmc_output object obtained from #' \code{\link{run_mcmc}} @@ -11,12 +12,14 @@ #' \code{"response"}, or \code{"state"} level. #' @param model Model for future observations. #' Should have same structure as the original model which was used in MCMC, -#' in order to plug the posterior samples of the model parameters to the right places. -#' It is also possible to input the original model, which can be useful for example for -#' posterior predictive checks. In this case, set argument \code{future} to \code{FALSE}. +#' in order to plug the posterior samples of the model parameters to the right +#' places. +#' It is also possible to input the original model, which can be useful for +#' example for posterior predictive checks. In this case, set argument +#' \code{future} to \code{FALSE}. #' @param nsim Number of samples to draw. -#' @param future Default is \code{TRUE}, in which case predictions are for the future, -#' using posterior samples of (theta, alpha_T+1) i.e. the +#' @param future Default is \code{TRUE}, in which case predictions are for the +#' future, using posterior samples of (theta, alpha_T+1) i.e. the #' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. #' @param seed Seed for RNG. @@ -111,19 +114,21 @@ #' time = time(model$y))) #' #' -predict.mcmc_output <- function(object, model, type = "response", nsim, future = TRUE, - seed = sample(.Machine$integer.max, size = 1), ...) { +predict.mcmc_output <- function(object, model, type = "response", nsim, + future = TRUE, seed = sample(.Machine$integer.max, size = 1), ...) { type <- match.arg(type, c("response", "mean", "state")) - if (object$output_type != 1) stop("MCMC output must contain posterior samples of the states.") + if (object$output_type != 1) + stop("MCMC output must contain posterior samples of the states.") if(!identical(attr(object, "model_type"), class(model)[1])) { stop("Model class does not correspond to the MCMC output. ") } if(!identical(ncol(object$theta), length(model$theta))) { - stop("Number of unknown parameters 'theta' does not correspond to the MCMC output. ") + stop(paste("Number of unknown parameters 'theta' does not correspond to", + "the MCMC output. ", sep = " ")) } if(nsim < 1) stop("Number of samples 'nsim' should be at least one.") @@ -133,10 +138,13 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = object$theta[,1:(ncol(object$theta) - length(model$beta))] <- log(object$theta[,1:(ncol(object$theta) - length(model$beta))]) } - w <- object$counts * (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) + w <- object$counts * + (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) + idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, + replace = TRUE) theta <- t(object$theta[idx, ]) - alpha <- matrix(object$alpha[nrow(object$alpha),,idx], nrow = ncol(object$alpha)) + alpha <- matrix(object$alpha[nrow(object$alpha),,idx], + nrow = ncol(object$alpha)) switch(attr(object, "model_type"), ssm_mlg = , @@ -144,7 +152,8 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = bsm_lg = , ar1_lg = { if (!identical(length(model$a1), ncol(object$alpha))) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } pred <- gaussian_predict(model, theta, alpha, pmatch(type, c("response", "mean", "state")), @@ -159,21 +168,25 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = svm = , ar1_ng = { if (!identical(length(model$a1), ncol(object$alpha))) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } model$distribution <- pmatch(model$distribution, - c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), + c("svm", "poisson", "binomial", "negative binomial", "gamma", + "gaussian"), duplicates.ok = TRUE) - 1 pred <- nongaussian_predict(model, theta, alpha, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) - if(anyNA(pred)) warning("NA or NaN values in predictions, possible under/overflow?") + if(anyNA(pred)) + warning("NA or NaN values in predictions, possible under/overflow?") }, ssm_nlg = { if (!identical(model$n_states, ncol(object$alpha))) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } pred <- nonlinear_predict(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, @@ -203,11 +216,13 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = } else { if(!identical(nrow(object$alpha) - 1L, length(model$y))) { - stop("Number of observations of the model and MCMC output do not match. ") + stop("Number of observations of the model and MCMC output do not match.") } - w <- object$counts * (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) + w <- object$counts * + (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) + idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, + replace = TRUE) n <- nrow(object$alpha) - 1L m <- ncol(object$alpha) @@ -242,7 +257,8 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = bsm_lg = , ar1_lg = { if (!identical(length(model$a1), m)) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } pred <- gaussian_predict_past(model, theta, states, pmatch(type, c("response", "mean", "state")), @@ -257,21 +273,25 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = svm = , ar1_ng = { if (!identical(length(model$a1), m)) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } model$distribution <- pmatch(model$distribution, - c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), + c("svm", "poisson", "binomial", "negative binomial", "gamma", + "gaussian"), duplicates.ok = TRUE) - 1 pred <- nongaussian_predict_past(model, theta, states, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) - if(anyNA(pred)) warning("NA or NaN values in predictions, possible under/overflow?") + if(anyNA(pred)) + warning("NA or NaN values in predictions, possible under/overflow?") }, ssm_nlg = { if (!identical(model$n_states, m)) { - stop("Model does not correspond to the MCMC output: Wrong number of states. ") + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) } pred <- nonlinear_predict_past(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, diff --git a/R/print_mcmc.R b/R/print_mcmc.R index a899661b..c9d2f319 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,7 +1,8 @@ iact <- function(x) { n <- length(x) x_ <- (x - mean(x)) / sd(x) - # Sokal: Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms + # Sokal: + # Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms C <- max(5.0, log10(n)) tau <- 1 for(k in 1:(n-1)) { @@ -41,13 +42,14 @@ print.mcmc_output <- function(x, ...) { if (x$mcmc_type %in% paste0("is", 1:3)) { theta <- mcmc(x$theta) if(x$output_type == 1) - alpha <- mcmc(matrix(x$alpha[nrow(x$alpha),,], ncol = ncol(x$alpha), byrow = TRUE, - dimnames = list(NULL, colnames(x$alpha)))) + alpha <- mcmc(matrix(x$alpha[nrow(x$alpha),,], ncol = ncol(x$alpha), + byrow = TRUE, dimnames = list(NULL, colnames(x$alpha)))) w <- x$counts * x$weights } else { theta <- expand_sample(x, "theta") if(x$output_type == 1) - alpha <- expand_sample(x, "state", times = nrow(x$alpha), by_states = FALSE)[[1]] + alpha <- + expand_sample(x, "state", times = nrow(x$alpha), by_states = FALSE)[[1]] } cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), @@ -56,7 +58,8 @@ print.mcmc_output <- function(x, ...) { cat("\n", "Iterations = ", x$burnin + 1, ":", x$iter, "\n", sep = "") cat("Thinning interval = ",x$thin, "\n", sep = "") cat("Length of the final jump chain = ", length(x$counts), "\n", sep = "") - cat("\nAcceptance rate after the burn-in period: ", paste(round(x$acceptance_rate,3),"\n", sep = "")) + cat("\nAcceptance rate after the burn-in period: ", + paste(round(x$acceptance_rate,3),"\n", sep = "")) cat("\nSummary for theta:\n\n") if (x$mcmc_type %in% paste0("is", 1:3)) { @@ -92,7 +95,8 @@ print.mcmc_output <- function(x, ...) { sd_alpha <- sqrt(diag(weighted_var(alpha, w, method = "moment"))) se_alpha_is <- weighted_se(alpha, w) se_alpha <- sqrt(apply(alpha, 2, function(x) asymptotic_var(x, w))) - stats <- matrix(c(mean_alpha, sd_alpha, se_alpha,se_alpha_is), ncol = 4, + stats <- matrix(c(mean_alpha, sd_alpha, se_alpha,se_alpha_is), + ncol = 4, dimnames = list(colnames(x$alpha), c("Mean", "SD", "SE", "SE-IS"))) } else { mean_alpha <- colMeans(alpha) @@ -124,23 +128,26 @@ print.mcmc_output <- function(x, ...) { #' Summary of MCMC object #' -#' This functions returns a list containing mean, standard deviations, standard errors, and -#' effective sample size estimates for parameters and states. +#' This functions returns a list containing mean, standard deviations, +#' standard errors, and effective sample size estimates for parameters and +#' states. #' #' For IS-MCMC two types of standard errors are reported. #' SE-IS can be regarded as the square root of independent IS variance, -#' whereas SE corresponds to the square root of total asymptotic variance ( -#' see Remark 3 of Vihola et al. (2020)). +#' whereas SE corresponds to the square root of total asymptotic variance +#' (see Remark 3 of Vihola et al. (2020)). #' #' @param object Output from \code{run_mcmc} #' @param return_se if \code{FALSE} (default), computation of standard #' errors and effective sample sizes is omitted. -#' @param variable Are the summary statistics computed for either \code{"theta"} (default), -#' \code{"states"}, or \code{"both"}? -#' @param only_theta Deprecated. If \code{TRUE}, summaries are computed only for hyperparameters theta. +#' @param variable Are the summary statistics computed for either +#' \code{"theta"} (default), \code{"states"}, or \code{"both"}? +#' @param only_theta Deprecated. If \code{TRUE}, summaries are computed only +#' for hyperparameters theta. #' @param ... Ignored. #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' @export summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", @@ -148,7 +155,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", if (only_theta) { parameters <- "theta" - warning("Argument 'only_theta' is deprecated. Use argument 'variable' instead. ") + warning(paste("Argument 'only_theta' is deprecated. Use argument", + "'variable' instead. ", sep = " ")) } variable <- match.arg(variable, c("theta", "states", "both")) @@ -166,7 +174,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", se_theta <- sqrt(apply(theta, 2, function(x) asymptotic_var(x, w))) ess_theta <- (sd_theta / se_theta)^2 ess_w <- apply(object$theta, 2, function(x) ess(w, identity, x)) - summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta, se_theta_is, ess_w), ncol = 6, + summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta, + se_theta_is, ess_w), ncol = 6, dimnames = list(colnames(object$theta), c("Mean", "SD", "SE", "ESS", "SE-IS", "ESS-IS"))) } else { @@ -183,7 +192,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sd_theta <- apply(theta, 2, sd) se_theta <- sqrt(spectrum0.ar(theta)$spec/nrow(theta)) ess_theta <- (sd_theta / se_theta)^2 - summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), ncol = 4, + summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), + ncol = 4, dimnames = list(colnames(object$theta), c("Mean", "SD", "SE", "ESS"))) } else { summary_theta <- matrix(c(mean_theta, sd_theta), ncol = 2, @@ -193,24 +203,32 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", } if (variable %in% c("states", "both")) { - if (object$output_type != 1) stop("Cannot return summary of states as the MCMC type was not 'full'. ") + if (object$output_type != 1) + stop("Cannot return summary of states as the MCMC type was not 'full'. ") m <- ncol(object$alpha) if (object$mcmc_type %in% paste0("is", 1:3)) { w <- object$counts * object$weights - mean_alpha <- ts(weighted_mean(object$alpha, w), start = attr(object, "ts")$start, - frequency = attr(object, "ts")$frequency, names = colnames(object$alpha)) + mean_alpha <- ts(weighted_mean(object$alpha, w), + start = attr(object, "ts")$start, + frequency = attr(object, "ts")$frequency, + names = colnames(object$alpha)) sd_alpha <- weighted_var(object$alpha, w, method = "moment") - sd_alpha <- if(m > 1) sqrt(t(apply(sd_alpha, 3, diag))) else matrix(sqrt(sd_alpha), ncol = 1) + sd_alpha <- if(m > 1) { + sqrt(t(apply(sd_alpha, 3, diag))) + } else matrix(sqrt(sd_alpha), ncol = 1) if(return_se) { - se_alpha_is <- apply(object$alpha, 2, function(x) weighted_se(t(x), w)) + se_alpha_is <- apply(object$alpha, 2, + function(x) weighted_se(t(x), w)) - se_alpha <- apply(object$alpha, 2, function(z) sqrt(apply(z, 1, function(x) asymptotic_var(x, w)))) + se_alpha <- apply(object$alpha, 2, + function(z) sqrt(apply(z, 1, function(x) asymptotic_var(x, w)))) alpha_ess <- (sd_alpha / se_alpha)^2 - ess_w <- apply(object$alpha, 2, function(z) apply(z, 1, function(x) ess(w, identity, x))) + ess_w <- apply(object$alpha, 2, + function(z) apply(z, 1, function(x) ess(w, identity, x))) summary_alpha <- list( "Mean" = mean_alpha, "SD" = sd_alpha, "SE" = se_alpha, "ESS" = alpha_ess, @@ -223,14 +241,17 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", alpha <- expand_sample(object, "states") mean_alpha <- ts(vapply(alpha, colMeans, numeric(nrow(object$alpha))), start = attr(object, "ts")$start, - frequency = attr(object, "ts")$frequency, names = colnames(object$alpha)) - sd_alpha <- vapply(alpha, function(x) apply(x, 2, sd), numeric(nrow(object$alpha))) + frequency = attr(object, "ts")$frequency, + names = colnames(object$alpha)) + sd_alpha <- vapply(alpha, function(x) apply(x, 2, sd), + numeric(nrow(object$alpha))) if(return_se) { se_alpha <- vapply(alpha, function(x) apply(x, 2, function(z) - sqrt(spectrum0.ar(z)$spec / length(z))), numeric(nrow(object$alpha))) + sqrt(spectrum0.ar(z)$spec / length(z))), + numeric(nrow(object$alpha))) ess_alpha <- (sd_alpha / se_alpha)^2 summary_alpha <- list( "Mean" = mean_alpha, "SD" = sd_alpha, @@ -250,23 +271,29 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", #' Expand the Jump Chain representation #' #' The MCMC algorithms of \code{bssm} use a jump chain representation where we -#' store the accepted values and the number of times we stayed in the current value. -#' Although this saves bit memory and is especially convenient for IS-corrected -#' MCMC, sometimes we want to have the usual sample paths. Function \code{expand_sample} -#' returns the expanded sample based on the counts. Note that for IS-corrected output the expanded +#' store the accepted values and the number of times we stayed in the current +#' value. Although this saves bit memory and is especially convenient for +#' IS-corrected MCMC, sometimes we want to have the usual sample paths. +#' Function \code{expand_sample} returns the expanded sample based on the +#' counts. Note that for IS-corrected output the expanded #' sample corresponds to the approximate posterior. #' #' @param x Output from \code{\link{run_mcmc}}. #' @param variable Expand parameters \code{"theta"} or states \code{"states"}. -#' @param times Vector of indices. In case of states, what time points to expand? Default is all. -#' @param states Vector of indices. In case of states, what states to expand? Default is all. -#' @param by_states If \code{TRUE} (default), return list by states. Otherwise by time. +#' @param times Vector of indices. In case of states, +#' what time points to expand? Default is all. +#' @param states Vector of indices. In case of states, +#' what states to expand? Default is all. +#' @param by_states If \code{TRUE} (default), return list by states. +#' Otherwise by time. #' @export -expand_sample <- function(x, variable = "theta", times, states, by_states = TRUE) { +expand_sample <- function(x, variable = "theta", times, states, + by_states = TRUE) { variable <- match.arg(variable, c("theta", "states")) if (x$mcmc_type %in% paste0("is", 1:3)) - warning("Input is based on a IS-weighted MCMC, the results correspond to the approximate posteriors.") + warning(paste("Input is based on a IS-weighted MCMC, the results", + "correspond to the approximate posteriors.", sep = " ")) if(variable == "theta") { out <- apply(x$theta, 2, rep, times = x$counts) } else { diff --git a/R/priors.R b/R/priors.R index ddcbd8e1..1e8126b7 100644 --- a/R/priors.R +++ b/R/priors.R @@ -3,20 +3,23 @@ #' Prior objects for bssm models #' -#' These simple objects of class \code{bssm_prior} are used to construct a prior distributions for the -#' MCMC runs of \code{bssm} package. Currently supported priors are uniform (\code{uniform()}), -#' half-normal (\code{halfnormal()}), normal (\code{normal()}), gamma (\code{gamma}), and -#' truncated normal distribution (\code{tnormal()}).All parameters are vectorized so -#' for regression coefficient vector beta you can define prior for example -#' as \code{normal(0, 0, c(10, 20))}. +#' These simple objects of class \code{bssm_prior} are used to construct a +#' prior distributions for the +#' MCMC runs of \code{bssm} package. Currently supported priors are uniform +#' (\code{uniform()}), half-normal (\code{halfnormal()}), +#' normal (\code{normal()}), gamma (\code{gamma}), and +#' truncated normal distribution (\code{tnormal()}). All parameters are +#' vectorized so for regression coefficient vector beta you can define prior +#' for example as \code{normal(0, 0, c(10, 20))}. #' #' #' @rdname priors -#' @param init Initial value for the parameter, used in initializing the model components and as a starting value -#' in MCMC. +#' @param init Initial value for the parameter, used in initializing the model +#' components and as a starting values in MCMC. #' @param min Lower bound of the uniform and truncated normal prior. #' @param max Upper bound of the uniform and truncated normal prior. -#' @param sd Standard deviation of the (underlying i.e. non-truncated) Normal distribution. +#' @param sd Standard deviation of the (underlying i.e. non-truncated) +#' Normal distribution. #' @param mean Mean of the Normal prior. #' @param shape Shape parameter of the Gamma prior. #' @param rate Rate parameter of the Gamma prior. @@ -32,19 +35,24 @@ uniform <- function(init, min, max){ stop("Parameters for priors must be numeric.") } if (any(min > max)){ - stop("Lower bound of uniform distribution must be smaller than upper bound.") + stop(paste("Lower bound of uniform distribution must be smaller than", + "upper bound.", sep = " ")) } if(any(init < min) || any(init > max)) { - stop("Initial value for parameter with uniform prior is not in the support of the prior.") + stop(paste("Initial value for parameter with uniform prior is not", + "in the support of the prior.", sep = " ")) } n <- max(length(init), length(min), length(max)) if(n > 1) { - structure(lapply(1:n, function(i) structure(list(prior_distribution = "uniform", init = safe_pick(init, i), - min = safe_pick(min, i), max = safe_pick(max, i)), class = "bssm_prior_list")), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "uniform", init = safe_pick(init, i), + min = safe_pick(min, i), max = safe_pick(max, i)), + class = "bssm_prior_list")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "uniform", init = init, min = min, max = max), class = "bssm_prior") + structure(list(prior_distribution = "uniform", init = init, + min = min, max = max), class = "bssm_prior") } } @@ -56,18 +64,23 @@ halfnormal <- function(init, sd){ stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { - stop("Standard deviation parameter for half-Normal distribution must be positive.") + stop(paste("Standard deviation parameter for half-Normal distribution must", + "be positive.", sep = " ")) } if (any(init < 0)) { - stop("Initial value for parameter with half-Normal prior must be non-negative.") + stop(paste("Initial value for parameter with half-Normal prior must be", + "non-negative.", sep = " ")) } n <- max(length(init), length(sd)) if (n > 1) { - structure(lapply(1:n, function(i) structure(list(prior_distribution = "halfnormal", init = safe_pick(init, i), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "halfnormal", + init = safe_pick(init, i), sd = safe_pick(sd, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "halfnormal", init = init, sd = sd), class = "bssm_prior") + structure(list(prior_distribution = "halfnormal", init = init, sd = sd), + class = "bssm_prior") } } @@ -80,18 +93,21 @@ normal <- function(init, mean, sd){ stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { - stop("Standard deviation parameter for Normal distribution must be positive.") + stop(paste("Standard deviation parameter for Normal distribution must", + "be positive.", sep = " ")) } n <- max(length(init), length(mean), length(sd)) if (n > 1) { - structure(lapply(1:n, function(i) structure(list(prior_distribution = "normal", - init = safe_pick(init, i), mean = safe_pick(mean, i), sd = safe_pick(sd, i)), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "normal", + init = safe_pick(init, i), mean = safe_pick(mean, i), + sd = safe_pick(sd, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "normal", init = init, mean = mean, sd = sd), - class = "bssm_prior") + structure(list(prior_distribution = "normal", init = init, mean = mean, + sd = sd), class = "bssm_prior") } } #' @rdname priors @@ -102,23 +118,27 @@ tnormal <- function(init, mean, sd, min = -Inf, max = Inf){ stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { - stop("Standard deviation parameter for Normal distribution must be positive.") + stop(paste("Standard deviation parameter for Normal distribution must be", + "positive.", sep = " ")) } n <- max(length(init), length(mean), length(sd)) if (n > 1) { - structure(lapply(1:n, function(i) structure(list(prior_distribution = "tnormal", - init = safe_pick(init, i), mean = safe_pick(mean, i), sd = safe_pick(sd, i), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "tnormal", + init = safe_pick(init, i), mean = safe_pick(mean, i), + sd = safe_pick(sd, i), min = safe_pick(min, i), max = safe_pick(max, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "tnormal", init = init, mean = mean, sd = sd, - min = min, max = max), class = "bssm_prior") + structure(list(prior_distribution = "tnormal", init = init, mean = mean, + sd = sd, min = min, max = max), class = "bssm_prior") } } combine_priors <- function(x) { - if (length(x) == 0) return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) + if (length(x) == 0) + return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) prior_distributions <- vapply(x, "[[", "prior_distribution", character(1)) parameters <- matrix(NA, 4, length(prior_distributions)) @@ -126,7 +146,8 @@ combine_priors <- function(x) { parameters[1:(length(x[[i]])-2), i] <- as.numeric(x[[i]][-(1:2)]) } list(prior_distributions = - pmatch(prior_distributions, c("uniform", "halfnormal", "normal", "tnormal", "gamma"), duplicates.ok = TRUE)-1, + pmatch(prior_distributions, c("uniform", "halfnormal", "normal", + "tnormal", "gamma"), duplicates.ok = TRUE)-1, parameters = parameters) } #' @rdname priors @@ -144,12 +165,15 @@ gamma <- function(init, shape, rate){ } n <- max(length(init), length(shape), length(rate)) if (n > 1) { - structure(lapply(1:n, function(i) structure(list(prior_distribution = "gamma", - init = safe_pick(init, i), shape = safe_pick(shape, i), rate = safe_pick(rate, i)), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "gamma", + init = safe_pick(init, i), shape = safe_pick(shape, i), + rate = safe_pick(rate, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "gamma", init = init, shape = shape, rate = rate), + structure(list(prior_distribution = "gamma", init = init, + shape = shape, rate = rate), class = "bssm_prior") } } diff --git a/R/run_mcmc.R b/R/run_mcmc.R index a72c53dc..aaecc7ea 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -7,7 +7,8 @@ #' @importFrom stats tsp #' @param model State space model model of \code{bssm} package. #' @param iter Number of MCMC iterations. -#' @param ... Parameters to specific methods. See \code{\link{run_mcmc.gaussian}}, +#' @param ... Parameters to specific methods. See +#' \code{\link{run_mcmc.gaussian}}, #' \code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, #' and \code{\link{run_mcmc.ssm_sde}} for details. #' @export @@ -25,16 +26,20 @@ run_mcmc <- function(model, iter, ...) { #' @param model Model model. #' @param iter Number of MCMC iterations. #' @param output_type Type of output. Default is \code{"full"}, which returns -#' samples from the posterior \eqn{p(\alpha, \theta)}. Option \code{"summary"} does not simulate -#' states directly but computes the posterior means and variances of states using -#' fast Kalman smoothing. This is slightly faster, more memory efficient and -#' more accurate than calculations based on simulation smoother. Using option \code{"theta"} will only -#' return samples from the marginal posterior of the hyperparameters \eqn{\theta}. +#' samples from the posterior \eqn{p(\alpha, \theta)}. +#' Option \code{"summary"} does not simulate +#' states directly but computes the posterior means and variances of states +#' using fast Kalman smoothing. This is slightly faster, +#' more memory efficient and more accurate than calculations based on +#' simulation smoother. Using option \code{"theta"} will only +#' return samples from the marginal posterior of the hyperparameters +#' \eqn{\theta}. #' @param burnin Length of the burn-in period which is disregarded from the -#' results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of \code{bssm} -#' used adaptive MCMC during the burn-in period in order to find good proposal. -#' @param thin Thinning rate. All MCMC algorithms in \code{bssm} use the jump chain -#' representation, and the thinning is applied to these blocks. +#' results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of +#' \code{bssm} used adaptive MCMC during the burn-in period in order to find +#' good proposal. +#' @param thin Thinning rate. All MCMC algorithms in \code{bssm} use the +#' jump chain representation, and the thinning is applied to these blocks. #' Defaults to 1. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be #' between 0 and 1 (not checked). @@ -42,14 +47,17 @@ run_mcmc <- function(model, iter, ...) { #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of bsm_lg models) the sampling -#' is done for transformed parameters with internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}. +#' (currently the standard deviation and dispersion parameters of bsm_lg +#' models) the sampling is done for transformed parameters with +#' internal_theta = log(theta). +#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the +#' burnin period. Default is \code{FALSE}. #' @param threads Number of threads for state simulation. The default is 1. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples @@ -78,7 +86,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", seed = sample(.Machine$integer.max, size = 1), ...) { - if(length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") + if(length(model$theta) == 0) + stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) @@ -88,7 +97,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", if (inherits(model, "bsm_lg")) { names_ind <- !model$fixed & c(TRUE, TRUE, model$slope, model$seasonal) model$theta[c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]] <- - log(pmax(1e-8, model$theta[c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]])) + log(pmax(1e-8, model$theta[ + c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]])) } if (missing(S)) { @@ -111,11 +121,13 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", } } - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + names(model$theta) if (inherits(model, "bsm_lg")) { out$theta[, c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]] <- - exp(out$theta[, c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]]) + exp(out$theta[, + c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]]) } out$call <- match.call() out$seed <- seed @@ -128,7 +140,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), frequency=frequency(model$y)) + list(start = start(model$y), end = end(model$y), + frequency = frequency(model$y)) out } @@ -145,8 +158,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' @param particles Number of state samples per MCMC iteration. #' Ignored if \code{mcmc_type} is \code{"approx"}. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and hyperparameters theta), -#' \code{"theta"} (for marginal posterior of theta), +#' (default, returns posterior samples of states alpha and +#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and #' covariances are computed using the full output of particle filter @@ -154,40 +167,46 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' @param mcmc_type What MCMC algorithm to use? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, #' \code{"da"} for delayed acceptance version of PMCMC , -#' \code{"approx"} for approximate inference based on the Gaussian approximation of the model, +#' \code{"approx"} for approximate inference based on the Gaussian +#' approximation of the model, #' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), +#' \code{"is3"} for simple importance sampling (weight is computed for each +#' MCMC iteration independently), #' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of particles used for +#' \code{"is1"} for importance sampling type weighting where the number of +#' particles used for #' weight computations is proportional to the length of the jump chain block. -#' @param sampling_method If \code{"psi"}, \eqn{\psi}-APF is used for state sampling -#' (default). If \code{"spdk"}, non-sequential importance sampling based -#' on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter +#' @param sampling_method If \code{"psi"}, \eqn{\psi}-APF is used for state +#' sampling (default). If \code{"spdk"}, non-sequential importance sampling +#' based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter #' is used. #' @param burnin Length of the burn-in period which is disregarded from the #' results. Defaults to \code{iter / 2}. #' @param thin Thinning rate. Defaults to 1. Increase for large models in -#' order to save memory. For IS-corrected methods, larger -#' value can also be statistically more effective. -#' Note: With \code{output_type = "summary"}, the thinning does not affect the computations -#' of the summary statistics in case of pseudo-marginal methods. +#' order to save memory. For IS-corrected methods, larger value can also be +#' statistically more effective. Note: With \code{output_type = "summary"}, +#' the thinning does not affect the computations of the summary statistics in +#' case of pseudo-marginal methods. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be #' between 0 and 1 (not checked). #' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -#' rate will be smaller. +#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +#' total acceptance rate will be smaller. #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of bsm_ng models) the sampling -#' is done for transformed parameters with internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}. -#' @param local_approx If \code{TRUE} (default), Gaussian approximation needed for -#' importance sampling is performed at each iteration. If \code{FALSE}, approximation is updated only -#' once at the start of the MCMC using the initial model. +#' (currently the standard deviation and dispersion parameters of +#' bsm_ng models) the sampling is done for transformed parameters with +#' internal_theta = log(theta). +#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the +#' burnin period. Default is \code{FALSE}. +#' @param local_approx If \code{TRUE} (default), Gaussian approximation +#' needed for importance sampling is performed at each iteration. +#' If \code{FALSE}, approximation is updated only once at the start of the +#' MCMC using the initial model. #' @param threads Number of threads for state simulation. The default is 1. -#' Note that parallel computing is only used in the post-correction phase of IS-MCMC -#' and when sampling the states in case of approximate models. +#' Note that parallel computing is only used in the post-correction phase of +#' IS-MCMC and when sampling the states in case of approximate models. #' @param seed Seed for the random number generator. #' @param max_iter Maximum number of iterations used in Gaussian approximation. #' @param conv_tol Tolerance parameter used in Gaussian approximation. @@ -303,19 +322,22 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "psi", burnin = floor(iter/2), - thin = 1, gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, - local_approx = TRUE, threads = 1, - seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { + thin = 1, gamma = 2/3, target_acceptance = 0.234, S, + end_adaptive_phase = FALSE, local_approx = TRUE, threads = 1, + seed = sample(.Machine$integer.max, size = 1), max_iter = 100, + conv_tol = 1e-8, ...) { if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } - if(length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") + if(length(model$theta) == 0) + stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) @@ -323,7 +345,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), "approx")) if (mcmc_type == "approx") particles <- 0 if (particles < 2 && mcmc_type != "approx") - stop("Number of state samples less than 2, use 'mcmc_type' 'approx' instead.") + stop(paste("Number of state samples less than 2, use 'mcmc_type' 'approx'", + "instead.", sep = " ")) sampling_method <- pmatch(match.arg(sampling_method, c("psi", "bsf", "spdk")), c("psi", "bsf", "spdk")) @@ -335,7 +358,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", if(inherits(model, "bsm_ng")) { names_ind <- c(!model$fixed & c(TRUE, model$slope, model$seasonal), model$noise) - transformed <- c(c("sd_level", "sd_slope", "sd_seasonal", "sd_noise")[names_ind], + transformed <- c( + c("sd_level", "sd_slope", "sd_seasonal", "sd_noise")[names_ind], if (model$distribution == "negative binomial") "phi") model$theta[transformed] <- log(pmax(1e-8, model$theta[transformed])) } @@ -387,7 +411,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", } } - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + names(model$theta) if(inherits(model, "bsm_ng")) { out$theta[, transformed] <- exp(out$theta[, transformed]) } @@ -402,7 +427,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), frequency=frequency(model$y)) + list(start = start(model$y), end = end(model$y), + frequency = frequency(model$y)) out } #' Bayesian Inference of non-linear state space models @@ -415,8 +441,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' @param particles Number of state samples per MCMC iteration. #' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and hyperparameters theta), -#' \code{"theta"} (for marginal posterior of theta), +#' (default, returns posterior samples of states alpha and +#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and #' covariances are computed using the full output of particle filter @@ -424,14 +450,18 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' @param mcmc_type What MCMC algorithm to use? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, #' \code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -#' \code{"approx"} for approximate inference based on the Gaussian approximation of the model, +#' \code{"approx"} for approximate inference based on the Gaussian +#' approximation of the model, #' \code{"ekf"} for approximate inference using extended Kalman filter, #' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), +#' \code{"is3"} for simple importance sampling (weight is computed for each +#' MCMC iteration independently), #' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of particles used for +#' \code{"is1"} for importance sampling type weighting where the number of +#' particles used for #' weight computations is proportional to the length of the jump chain block. -#' @param sampling_method If \code{"bsf"} (default), bootstrap filter is used for state sampling. +#' @param sampling_method If \code{"bsf"} (default), bootstrap filter is used +#' for state sampling. #' If \code{"ekf"}, particle filter based on EKF-proposals are used. #' If \code{"psi"}, \eqn{\psi}-APF is used. #' @param burnin Length of the burn-in period which is disregarded from the @@ -439,29 +469,33 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' @param thin Thinning rate. Defaults to 1. Increase for large models in #' order to save memory. For IS-corrected methods, larger #' value can also be statistically more effective. -#' Note: With \code{output_type = "summary"}, the thinning does not affect the computations -#' of the summary statistics in case of pseudo-marginal methods. +#' Note: With \code{output_type = "summary"}, the thinning does not affect the +#' computations of the summary statistics in case of pseudo-marginal methods. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be #' between 0 and 1 (not checked). #' @param target_acceptance Target acceptance ratio for RAM. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -#' rate will be smaller. +#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +#' total acceptance rate will be smaller. #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of bsm_ng models) the sampling +#' (currently the standard deviation and dispersion parameters of +#' bsm_ng models) the sampling #' is done for transformed parameters with internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}. +#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin +#' period. Default is \code{FALSE}. #' @param threads Number of threads for state simulation. #' @param seed Seed for the random number generator. #' @param max_iter Maximum number of iterations used in Gaussian approximation. #' @param conv_tol Tolerance parameter used in Gaussian approximation. -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is used with -#' \code{iekf_iter} iterations in place of standard EKF. Defaults to zero. +#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is +#' used with \code{iekf_iter} iterations in place of standard EKF. +#' Defaults to zero. #' @param ... Ignored. #' @export #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "bsf", @@ -473,17 +507,20 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } - if(length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") + if(length(model$theta) == 0) + stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) output_type <- pmatch(output_type, c("full", "summary", "theta")) - mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), "ekf", "approx")) + mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), + "ekf", "approx")) if(mcmc_type %in% c("ekf", "approx")) particles <- 0 sampling_method <- pmatch(match.arg(sampling_method, c("psi", "bsf", "ekf")), c("psi", "bsf", NA, "ekf")) @@ -493,7 +530,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } if (particles < 2 && !(mcmc_type %in% c("ekf", "approx"))) - stop("Number of state samples less than 2, use 'mcmc_type' 'approx' or 'ekf' instead.") + stop(paste("Number of state samples less than 2, use 'mcmc_type'", + "'approx' or 'ekf' instead.", sep = " ")) out <- switch(mcmc_type, @@ -565,7 +603,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + names(model$theta) out$iter <- iter out$burnin <- burnin @@ -578,7 +617,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_nlg" attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), frequency=frequency(model$y)) + list(start = start(model$y), end = end(model$y), + frequency = frequency(model$y)) out } #' Bayesian Inference of SDE @@ -590,8 +630,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", #' @param iter Number of MCMC iterations. #' @param particles Number of state samples per MCMC iteration. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and hyperparameters theta), -#' \code{"theta"} (for marginal posterior of theta), +#' (default, returns posterior samples of states alpha and hyperparameters +#' theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and #' covariances are computed using the full output of particle filter @@ -601,36 +641,41 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", #' \code{"pm"} for pseudo-marginal MCMC, #' \code{"da"} for delayed acceptance version of pseudo-marginal MCMC, #' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), +#' \code{"is3"} for simple importance sampling (weight is computed for each +#' MCMC iteration independently), #' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of particles used for +#' \code{"is1"} for importance sampling type weighting where the number of +#' particles used for #' weight computations is proportional to the length of the jump chain block. #' @param burnin Length of the burn-in period which is disregarded from the #' results. Defaults to \code{iter / 2}. #' @param thin Thinning rate. Defaults to 1. Increase for large models in #' order to save memory. For IS-corrected methods, larger #' value can also be statistically more effective. -#' Note: With \code{output_type = "summary"}, the thinning does not affect the computations -#' of the summary statistics in case of pseudo-marginal methods. +#' Note: With \code{output_type = "summary"}, the thinning does not affect the +#' computations of the summary statistics in case of pseudo-marginal methods. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be #' between 0 and 1 (not checked). #' @param target_acceptance Target acceptance ratio for RAM. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -#' rate will be smaller. +#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., +#' the total acceptance rate will be smaller. #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of bsm_ng models) the sampling -#' is done for transformed parameters with internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}. +#' (currently the standard deviation and dispersion parameters of +#' bsm_ng models) the sampling is done for transformed parameters with +#' internal_theta = log(theta). +#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin +#' period. Default is \code{FALSE}. #' @param threads Number of threads for state simulation. -#' @param L_c,L_f Integer values defining the discretization levels for first and second stages (defined as 2^L). -#' For PM methods, maximum of these is used. +#' @param L_c,L_f Integer values defining the discretization levels for first +#' and second stages (defined as 2^L). For PM methods, maximum of these is used. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @export #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", L_c, L_f, @@ -638,20 +683,23 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { - if(any(c(model$drift, model$diffusion, model$ddiffusion, - model$prior_pdf, model$obs_pdf) %in% c("", ""))) { - stop("NULL pointer detected, please recompile the pointer file and reconstruct the model.") + if(any(c(model$drift, model$diffusion, model$ddiffusion,model$prior_pdf, + model$obs_pdf) %in% c("", ""))) { + stop(paste("NULL pointer detected, please recompile the pointer file", + "and reconstruct the model.", sep = " ")) } if(missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { - warning("Argument `nsim` is deprecated. Use argument `particles` instead.") + warning(paste("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) particles <- nsim } } - if(length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") + if(length(model$theta) == 0) + stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) if(particles <= 0) stop("particles should be positive integer.") @@ -699,7 +747,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", } colnames(out$alpha) <- model$state_names - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + names(model$theta) out$iter <- iter out$burnin <- burnin @@ -712,6 +761,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_sde" attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), frequency=frequency(model$y)) + list(start = start(model$y), end = end(model$y), + frequency = frequency(model$y)) out } diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 7d53c136..37137c92 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -1,10 +1,11 @@ #' Simulation Smoothing #' -#' Function \code{sim_smoother} performs simulation smoothing i.e. simulates the states -#' from the conditional distribution \eqn{p(\alpha | y, \theta)} for linear-Gaussian models. +#' Function \code{sim_smoother} performs simulation smoothing i.e. simulates +#' the states from the conditional distribution \eqn{p(\alpha | y, \theta)} +#' for linear-Gaussian models. #' -#' For non-Gaussian/non-linear models, the simulation is based on the approximating -#' Gaussian model. +#' For non-Gaussian/non-linear models, the simulation is based on the +#' approximating Gaussian model. #' #' @param model Model object. #' @param nsim Number of independent samples. @@ -16,7 +17,8 @@ #' @export #' @rdname sim_smoother #' @examples -#' model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), sd_y = uniform(1,0,5)) +#' model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), +#' sd_y = uniform(1,0,5)) #' sim <- sim_smoother(model, 12) #' ts.plot(sim[, 1, ]) sim_smoother <- function(model, nsim, seed, use_antithetic = FALSE, ...) { @@ -28,7 +30,8 @@ sim_smoother <- function(model, nsim, seed, use_antithetic = FALSE, ...) { sim_smoother.gaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = FALSE, ...) { - out <- gaussian_sim_smoother(model, nsim, use_antithetic, seed, model_type(model)) + out <- gaussian_sim_smoother(model, nsim, use_antithetic, seed, + model_type(model)) rownames(out) <- names(model$a1) aperm(out, c(2, 1, 3))[-(length(model$y) + 1), , , drop = FALSE] } diff --git a/R/smoother.R b/R/smoother.R index 794dfb80..24055645 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -4,7 +4,8 @@ #' computes only smoothed estimates of the states, and function #' \code{smoother} computes also smoothed variances. #' -#' For non-Gaussian models, the smoothing is based on the approximate Gaussian model. +#' For non-Gaussian models, the smoothing is based on the approximate Gaussian +#' model. #' #' @param model Model model. #' @param ... Ignored. @@ -40,7 +41,8 @@ smoother <- function(model, ...) { smoother.gaussian <- function(model, ...) { out <- gaussian_smoother(model, model_type(model)) - colnames(out$alphahat) <- colnames(out$Vt) <- rownames(out$Vt) <- names(model$a1) + colnames(out$alphahat) <- colnames(out$Vt) <- rownames(out$Vt) <- + names(model$a1) out$Vt <- out$Vt[, , -nrow(out$alphahat), drop = FALSE] out$alphahat <- ts(out$alphahat[-nrow(out$alphahat), , drop = FALSE], @@ -57,16 +59,17 @@ smoother.nongaussian <- function(model, ...) { #' Extended Kalman Smoothing #' -#' Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother for -#' the given non-linear Gaussian model of class \code{ssm_nlg}, -#' and returns the smoothed estimates of the states and the corresponding variances. +#' Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother +#' for the given non-linear Gaussian model of class \code{ssm_nlg}, +#' and returns the smoothed estimates of the states and the corresponding +#' variances. #' #' @param model Model model #' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is #' used with \code{iekf_iter} iterations. #' @return List containing the log-likelihood, -#' smoothed state estimates \code{alphahat}, and the corresponding variances \code{Vt} and -#' \code{Ptt}. +#' smoothed state estimates \code{alphahat}, and the corresponding variances +#' \code{Vt} and \code{Ptt}. #' @export #' @rdname ekf_smoother #' @export diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index 9ee0c96b..dc61a32d 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -1,36 +1,38 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ar1_lg} -\alias{ar1_lg} -\title{Univariate Gaussian model with AR(1) latent process} -\usage{ -ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sigma}{Prior for the standard deviation of noise of the AR-process.} - -\item{mu}{A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0.} - -\item{sd_y}{Prior for the standard deviation of observation equation.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} -} -\value{ -Object of class \code{ar1_lg}. -} -\description{ -Constructs a simple Gaussian model where the state dynamics follow an AR(1) process. -} -\examples{ -model <- ar1_lg(BJsales, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(200, 200, 100), - sd_y = halfnormal(1, 10)) -out <- run_mcmc(model, iter = 2e4) -summary(out, return_se = TRUE) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ar1_lg} +\alias{ar1_lg} +\title{Univariate Gaussian model with AR(1) latent process} +\usage{ +ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{rho}{prior for autoregressive coefficient.} + +\item{sigma}{Prior for the standard deviation of noise of the AR-process.} + +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Parameter is omitted if this is set to 0.} + +\item{sd_y}{Prior for the standard deviation of observation equation.} + +\item{beta}{Prior for the regression coefficients.} + +\item{xreg}{Matrix containing covariates.} +} +\value{ +Object of class \code{ar1_lg}. +} +\description{ +Constructs a simple Gaussian model where the state dynamics +follow an AR(1) process. +} +\examples{ +model <- ar1_lg(BJsales, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(200, 200, 100), + sd_y = halfnormal(1, 10)) +out <- run_mcmc(model, iter = 2e4) +summary(out, return_se = TRUE) +} diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index b80105fc..a5a4bf47 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -1,59 +1,62 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/as.data.frame.mcmc_output.R -\name{as.data.frame.mcmc_output} -\alias{as.data.frame.mcmc_output} -\title{Convert MCMC chain to data.frame} -\usage{ -\method{as.data.frame}{mcmc_output}( - x, - row.names, - optional, - variable = c("theta", "states"), - times, - states, - expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), - ... -) -} -\arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} - -\item{row.names}{Ignored.} - -\item{optional}{Ignored.} - -\item{variable}{Return samples of \code{"theta"} (default) or \code{"states"}?} - -\item{times}{Vector of indices. In case of states, what time points to return? Default is all.} - -\item{states}{Vector of indices. In case of states, what states to return? Default is all.} - -\item{expand}{Should the jump-chain be expanded? -Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. -For \code{expand = FALSE} and always for IS-MCMC, -the resulting data.frame contains variable weight (= counts times IS-weights).} - -\item{...}{Ignored.} -} -\description{ -Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. -} -\examples{ -data("poisson_series") -model <- bsm_ng(y = poisson_series, -sd_slope = halfnormal(0.1, 0.1), -sd_level = halfnormal(0.1, 1), - distribution = "poisson") - -out <- run_mcmc(model, iter = 2000, particles = 10) -head(as.data.frame(out, variable = "theta")) -head(as.data.frame(out, variable = "state")) - -# don't expand the jump chain: -head(as.data.frame(out, variable = "theta", expand = FALSE)) - -# IS-weighted version: -out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") -head(as.data.frame(out_is, variable = "theta")) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as.data.frame.mcmc_output.R +\name{as.data.frame.mcmc_output} +\alias{as.data.frame.mcmc_output} +\title{Convert MCMC chain to data.frame} +\usage{ +\method{as.data.frame}{mcmc_output}( + x, + row.names, + optional, + variable = c("theta", "states"), + times, + states, + expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), + ... +) +} +\arguments{ +\item{x}{Output from \code{\link{run_mcmc}}.} + +\item{row.names}{Ignored.} + +\item{optional}{Ignored.} + +\item{variable}{Return samples of \code{"theta"} (default) or +\code{"states"}?} + +\item{times}{Vector of indices. In case of states, +what time points to return? Default is all.} + +\item{states}{Vector of indices. In case of states, +what states to return? Default is all.} + +\item{expand}{Should the jump-chain be expanded? +Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. +For \code{expand = FALSE} and always for IS-MCMC, +the resulting data.frame contains variable weight (= counts * IS-weights).} + +\item{...}{Ignored.} +} +\description{ +Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. +} +\examples{ +data("poisson_series") +model <- bsm_ng(y = poisson_series, +sd_slope = halfnormal(0.1, 0.1), +sd_level = halfnormal(0.1, 1), + distribution = "poisson") + +out <- run_mcmc(model, iter = 2000, particles = 10) +head(as.data.frame(out, variable = "theta")) +head(as.data.frame(out, variable = "state")) + +# don't expand the jump chain: +head(as.data.frame(out, variable = "theta", expand = FALSE)) + +# IS-weighted version: +out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") +head(as.data.frame(out_is, variable = "theta")) + +} diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 6894be8d..150c2a8c 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -1,33 +1,35 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/as_bssm.R -\name{as_bssm} -\alias{as_bssm} -\title{Convert KFAS Model to bssm Model} -\usage{ -as_bssm(model, kappa = 100, ...) -} -\arguments{ -\item{model}{Object of class \code{SSModel}.} - -\item{kappa}{For \code{SSModel} object, a prior variance for initial state -used to replace exact diffuse elements of the original model.} - -\item{...}{Additional arguments to model building functions of \code{bssm} -(such as prior and updating functions).} -} -\value{ -Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. -} -\description{ -Converts \code{SSModel} object of \code{KFAS} package to general -\code{bssm} model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. -} -\examples{ -library("KFAS") -model_KFAS <- SSModel(Nile ~ - SSMtrend(1, Q = 2, P1 = 1e4), H = 2) -model_bssm <- as_bssm(model_KFAS) -logLik(model_KFAS) -logLik(model_bssm) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as_bssm.R +\name{as_bssm} +\alias{as_bssm} +\title{Convert KFAS Model to bssm Model} +\usage{ +as_bssm(model, kappa = 100, ...) +} +\arguments{ +\item{model}{Object of class \code{SSModel}.} + +\item{kappa}{For \code{SSModel} object, a prior variance for initial state +used to replace exact diffuse elements of the original model.} + +\item{...}{Additional arguments to model building functions of \code{bssm} +(such as prior and updating functions).} +} +\value{ +Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +\code{ssm_mng}. +} +\description{ +Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} +model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +\code{ssm_mng}. +} +\examples{ +library("KFAS") +model_KFAS <- SSModel(Nile ~ + SSMtrend(1, Q = 2, P1 = 1e4), H = 2) +model_bssm <- as_bssm(model_KFAS) +logLik(model_KFAS) +logLik(model_bssm) + +} diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 7d255978..256a98f1 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -1,83 +1,85 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bootstrap_filter.R -\name{bootstrap_filter} -\alias{bootstrap_filter} -\alias{bootstrap_filter.gaussian} -\alias{bootstrap_filter.nongaussian} -\alias{bootstrap_filter.ssm_nlg} -\alias{bootstrap_filter.ssm_sde} -\title{Bootstrap Filtering} -\usage{ -bootstrap_filter(model, particles, ...) - -\method{bootstrap_filter}{gaussian}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{nongaussian}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{ssm_nlg}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{ssm_sde}( - model, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}.} - -\item{particles}{Number of particles.} - -\item{...}{Ignored.} - -\item{seed}{Seed for RNG.} - -\item{L}{Integer defining the discretization level for SDE models.} -} -\value{ -List with samples (\code{alpha}) from the filtering distribution and corresponding weights (\code{weights}), - as well as filtered and predicted states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, \code{Ptt}), - and estimated log-likelihood (\code{logLik}). -} -\description{ -Function \code{bootstrap_filter} performs a bootstrap filtering with stratification -resampling. -} -\examples{ -set.seed(1) -x <- cumsum(rnorm(50)) -y <- rnorm(50, x, 0.5) -model <- bsm_lg(y, sd_y = 0.5, sd_level = 1, P1 = 1) - -out <- bootstrap_filter(model, particles = 1000) -ts.plot(cbind(y, x, out$att), col = 1:3) -ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3) - -data("poisson_series") -model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, - P1 = diag(1, 2), distribution = "poisson") - -out <- bootstrap_filter(model, particles = 100) -ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2) - -} -\references{ -Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bootstrap_filter.R +\name{bootstrap_filter} +\alias{bootstrap_filter} +\alias{bootstrap_filter.gaussian} +\alias{bootstrap_filter.nongaussian} +\alias{bootstrap_filter.ssm_nlg} +\alias{bootstrap_filter.ssm_sde} +\title{Bootstrap Filtering} +\usage{ +bootstrap_filter(model, particles, ...) + +\method{bootstrap_filter}{gaussian}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{nongaussian}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{ssm_nlg}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{ssm_sde}( + model, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}.} + +\item{particles}{Number of particles.} + +\item{...}{Ignored.} + +\item{seed}{Seed for RNG.} + +\item{L}{Integer defining the discretization level for SDE models.} +} +\value{ +List with samples (\code{alpha}) from the filtering distribution and +corresponding weights (\code{weights}), as well as filtered and predicted +states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, +\code{Ptt}), and estimated log-likelihood (\code{logLik}). +} +\description{ +Function \code{bootstrap_filter} performs a bootstrap filtering with +stratification resampling. +} +\examples{ +set.seed(1) +x <- cumsum(rnorm(50)) +y <- rnorm(50, x, 0.5) +model <- bsm_lg(y, sd_y = 0.5, sd_level = 1, P1 = 1) + +out <- bootstrap_filter(model, particles = 1000) +ts.plot(cbind(y, x, out$att), col = 1:3) +ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3) + +data("poisson_series") +model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, + P1 = diag(1, 2), distribution = "poisson") + +out <- bootstrap_filter(model, particles = 100) +ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2) + +} +\references{ +Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +IEE Proceedings-F, 140, 107–113. +} diff --git a/man/bssm.Rd b/man/bssm.Rd index e6bf721d..93901a04 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -1,19 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{package} -\name{bssm} -\alias{bssm} -\title{Bayesian Inference of State Space Models} -\description{ -This package contains functions for Bayesian inference of basic stochastic volatility model -and exponential family state space models, where the state equation is linear and Gaussian, -and the conditional observation density is either Gaussian, Poisson, -binomial, negative binomial or Gamma density. General non-linear Gaussian models and models -with continuous SDE dynamics are also supported. For formal definition of the -currently supported models and methods, as well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, -see the package vignettes and Vihola, Helske, Franks (2020). -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{package} +\name{bssm} +\alias{bssm} +\title{Bayesian Inference of State Space Models} +\description{ +This package contains functions for efficient Bayesian inference of state +space models, where model is assumed to be either +* Exponential family state space models, where the state equation is linear and Gaussian, + and the conditional observation density is either Gaussian, Poisson, + binomial, negative binomial or Gamma density. +* Basic stochastic volatility model. +* General non-linear model with Gaussian noise terms. +* Model with continuous SDE dynamics. +For formal definition of the currently supported models and methods, as +well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, +see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package +vignettes. +} +\references{ +Helske J, Vihola M (2021). “bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R.” 2101.08492, +. + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/drownings.Rd b/man/drownings.Rd index 9618e1d6..28c969d5 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -1,32 +1,33 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{data} -\name{drownings} -\alias{drownings} -\title{Deaths by drowning in Finland in 1969-2019} -\format{ -A time series object containing 51 observations. -} -\source{ -Statistics Finland \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. -} -\description{ -Dataset containing number of deaths by drowning in Finland in 1969-2019, -corresponding population sizes (in hundreds of thousands), and -yearly average summer temperatures (June to August), based on simple -unweighted average of three weather stations: Helsinki (Southern Finland), -Jyväskylä (Central Finland), and Sodankylä (Northern Finland). -} -\examples{ -data("drownings") -model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], - xreg = drownings[, "summer_temp"], distribution = "poisson", - beta = normal(0, 0, 1), - sd_level = gamma(0.1,2, 10), sd_slope = gamma(0, 2, 10)) - -fit <- run_mcmc(model, iter = 5000, - output_type = "summary", mcmc_type = "approx") -fit -ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{drownings} +\alias{drownings} +\title{Deaths by drowning in Finland in 1969-2019} +\format{ +A time series object containing 51 observations. +} +\source{ +Statistics Finland +\url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. +} +\description{ +Dataset containing number of deaths by drowning in Finland in 1969-2019, +corresponding population sizes (in hundreds of thousands), and +yearly average summer temperatures (June to August), based on simple +unweighted average of three weather stations: Helsinki (Southern Finland), +Jyväskylä (Central Finland), and Sodankylä (Northern Finland). +} +\examples{ +data("drownings") +model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], + xreg = drownings[, "summer_temp"], distribution = "poisson", + beta = normal(0, 0, 1), + sd_level = gamma(0.1,2, 10), sd_slope = gamma(0, 2, 10)) + +fit <- run_mcmc(model, iter = 5000, + output_type = "summary", mcmc_type = "approx") +fit +ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) +} +\keyword{datasets} diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 63305ccb..733e4f64 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -1,36 +1,38 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ekpf_filter.R -\name{ekpf_filter} -\alias{ekpf_filter} -\alias{ekpf_filter.ssm_nlg} -\title{Extended Kalman Particle Filtering} -\usage{ -ekpf_filter(object, particles, ...) - -\method{ekpf_filter}{ssm_nlg}( - object, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{of class \code{ssm_nlg}.} - -\item{particles}{Number of particles.} - -\item{...}{Ignored.} - -\item{seed}{Seed for RNG.} -} -\value{ -A list containing samples, filtered estimates and the corresponding covariances, -weights, and an estimate of log-likelihood. -} -\description{ -Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification -resampling, based on Van Der Merwe et al (2001). -} -\references{ -Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. In Advances in neural information processing systems (pp. 584-590). -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ekpf_filter.R +\name{ekpf_filter} +\alias{ekpf_filter} +\alias{ekpf_filter.ssm_nlg} +\title{Extended Kalman Particle Filtering} +\usage{ +ekpf_filter(object, particles, ...) + +\method{ekpf_filter}{ssm_nlg}( + object, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{of class \code{ssm_nlg}.} + +\item{particles}{Number of particles.} + +\item{...}{Ignored.} + +\item{seed}{Seed for RNG.} +} +\value{ +A list containing samples, filtered estimates and the +corresponding covariances, weights, and an estimate of log-likelihood. +} +\description{ +Function \code{ekpf_filter} performs a extended Kalman particle filtering +with stratification resampling, based on Van Der Merwe et al (2001). +} +\references{ +Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. +(2001). The unscented particle filter. In Advances in neural +information processing systems (pp. 584-590). +} diff --git a/man/exchange.Rd b/man/exchange.Rd index f00bd32d..5990e5ea 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -1,28 +1,28 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{data} -\name{exchange} -\alias{exchange} -\title{Pound/Dollar daily exchange rates} -\format{ -A vector of length 945. -} -\source{ -\url{http://www.ssfpack.com/DKbook.html}. -} -\description{ -Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] -} -\examples{ -data("exchange") -model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), - sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) - -out <- particle_smoother(model, particles = 500) -plot.ts(cbind(model$y, exp(out$alphahat))) -} -\references{ -James Durbin, Siem Jan Koopman (2012). "Time Series Analysis by State Space Methods". -Oxford University Press. -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{exchange} +\alias{exchange} +\title{Pound/Dollar daily exchange rates} +\format{ +A vector of length 945. +} +\source{ +\url{http://www.ssfpack.com/DKbook.html}. +} +\description{ +Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] +} +\examples{ +data("exchange") +model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), + sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) + +out <- particle_smoother(model, particles = 500) +plot.ts(cbind(model$y, exp(out$alphahat))) +} +\references{ +James Durbin, Siem Jan Koopman (2012). +Time Series Analysis by State Space Methods. Oxford University Press. +} +\keyword{datasets} diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 8d052189..7571740e 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -1,40 +1,46 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/approx.R -\name{gaussian_approx} -\alias{gaussian_approx} -\alias{gaussian_approx.nongaussian} -\alias{gaussian_approx.ssm_nlg} -\title{Gaussian Approximation of Non-Gaussian/Non-linear State Space Model} -\usage{ -gaussian_approx(model, max_iter, conv_tol, ...) - -\method{gaussian_approx}{nongaussian}(model, max_iter = 100, conv_tol = 1e-08, ...) - -\method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) -} -\arguments{ -\item{model}{Model to be approximated.} - -\item{max_iter}{Maximum number of iterations.} - -\item{conv_tol}{Tolerance parameter.} - -\item{...}{Ignored.} - -\item{iekf_iter}{For non-linear models, number of iterations in iterated EKF (defaults to 0).} -} -\description{ -Returns the approximating Gaussian model. This function is rarely needed itself, -and is mainly available for testing and debugging purposes. -} -\examples{ -data("poisson_series") -model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, - distribution = "poisson") -out <- gaussian_approx(model) -} -\references{ -Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press. -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/approx.R +\name{gaussian_approx} +\alias{gaussian_approx} +\alias{gaussian_approx.nongaussian} +\alias{gaussian_approx.ssm_nlg} +\title{Gaussian Approximation of Non-Gaussian/Non-linear State Space Model} +\usage{ +gaussian_approx(model, max_iter, conv_tol, ...) + +\method{gaussian_approx}{nongaussian}(model, max_iter = 100, conv_tol = 1e-08, ...) + +\method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) +} +\arguments{ +\item{model}{Model to be approximated.} + +\item{max_iter}{Maximum number of iterations.} + +\item{conv_tol}{Tolerance parameter.} + +\item{...}{Ignored.} + +\item{iekf_iter}{For non-linear models, number of iterations in iterated EKF +(defaults to 0).} +} +\description{ +Returns the approximating Gaussian model which has the same conditional +mode of p(alpha|y, theta) as the original model. +This function is rarely needed itself, and is mainly available for +testing and debugging purposes. +} +\examples{ +data("poisson_series") +model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, + distribution = "poisson") +out <- gaussian_approx(model) +} +\references{ +Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space +Methods. Second edition. Oxford: Oxford University Press. + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index 89b7933f..fb82f9cd 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -1,57 +1,61 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/importance_sample.R -\name{importance_sample} -\alias{importance_sample} -\alias{importance_sample.nongaussian} -\title{Importance Sampling from non-Gaussian State Space Model} -\usage{ -importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) - -\method{importance_sample}{nongaussian}( - model, - nsim, - use_antithetic = TRUE, - max_iter = 100, - conv_tol = 1e-08, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}.} - -\item{nsim}{Number of samples.} - -\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic -variable for location in simulation smoothing. Ignored for \code{ssm_mng} models.} - -\item{max_iter}{Maximum number of iterations used for the approximation.} - -\item{conv_tol}{Convergence threshold for the approximation. Approximation is -claimed to be converged when the mean squared difference of the modes is -less than \code{conv_tol}.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Returns \code{nsim} samples from the approximating Gaussian model with corresponding -(scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages. -} -\examples{ -data("sexratio", package = "KFAS") -model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, u = sexratio[, "Total"], - distribution = "binomial") - -imp <- importance_sample(model, nsim = 1000) - -est <- matrix(NA, 3, nrow(sexratio)) -for(i in 1:ncol(est)) { - est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, - prob = c(0.05,0.5,0.95), normwt=TRUE) -} - -ts.plot(t(est),lty = c(2,1,2)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/importance_sample.R +\name{importance_sample} +\alias{importance_sample} +\alias{importance_sample.nongaussian} +\title{Importance Sampling from non-Gaussian State Space Model} +\usage{ +importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) + +\method{importance_sample}{nongaussian}( + model, + nsim, + use_antithetic = TRUE, + max_iter = 100, + conv_tol = 1e-08, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}.} + +\item{nsim}{Number of samples.} + +\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic +variable for location in simulation smoothing. Ignored for \code{ssm_mng} +models.} + +\item{max_iter}{Maximum number of iterations used for the approximation.} + +\item{conv_tol}{Convergence threshold for the approximation. Approximation is +claimed to be converged when the mean squared difference of the modes is +less than \code{conv_tol}.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Returns \code{nsim} samples from the approximating Gaussian model with +corresponding (scaled) importance weights. +Probably mostly useful for comparing KFAS and bssm packages. +} +\examples{ +data("sexratio", package = "KFAS") +model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, + u = sexratio[, "Total"], + distribution = "binomial") + +imp <- importance_sample(model, nsim = 1000) + +est <- matrix(NA, 3, nrow(sexratio)) +for(i in 1:ncol(est)) { + est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, + prob = c(0.05,0.5,0.95), normwt=TRUE) +} + +ts.plot(t(est),lty = c(2,1,2)) + +} diff --git a/man/kfilter.Rd b/man/kfilter.Rd index 46b60135..7b8f7aa4 100644 --- a/man/kfilter.Rd +++ b/man/kfilter.Rd @@ -1,42 +1,43 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/kfilter.R -\name{kfilter} -\alias{kfilter} -\alias{kfilter.gaussian} -\alias{kfilter.nongaussian} -\title{Kalman Filtering} -\usage{ -kfilter(model, ...) - -\method{kfilter}{gaussian}(model, ...) - -\method{kfilter}{nongaussian}(model, ...) -} -\arguments{ -\item{model}{Model Model object.} - -\item{...}{Ignored.} -} -\value{ -List containing the log-likelihood (approximate in non-Gaussian case), -one-step-ahead predictions \code{at} and filtered -estimates \code{att} of states, and the corresponding variances \code{Pt} and - \code{Ptt}. -} -\description{ -Function \code{kfilter} runs the Kalman filter for the given model, -and returns the filtered estimates and one-step-ahead predictions of the -states \eqn{\alpha_t} given the data up to time \eqn{t}. -} -\details{ -For non-Gaussian models, the filtering is based on the approximate Gaussian model. -} -\examples{ -x <- cumsum(rnorm(20)) -y <- x + rnorm(20, sd = 0.1) -model <- bsm_lg(y, sd_level = 1, sd_y = 0.1) -ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) -} -\seealso{ -\code{\link{bootstrap_filter}} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kfilter.R +\name{kfilter} +\alias{kfilter} +\alias{kfilter.gaussian} +\alias{kfilter.nongaussian} +\title{Kalman Filtering} +\usage{ +kfilter(model, ...) + +\method{kfilter}{gaussian}(model, ...) + +\method{kfilter}{nongaussian}(model, ...) +} +\arguments{ +\item{model}{Model Model object.} + +\item{...}{Ignored.} +} +\value{ +List containing the log-likelihood +(approximate in non-Gaussian case), one-step-ahead predictions \code{at} +and filtered estimates \code{att} of states, and the corresponding +variances \code{Pt} and \code{Ptt}. +} +\description{ +Function \code{kfilter} runs the Kalman filter for the given model, +and returns the filtered estimates and one-step-ahead predictions of the +states \eqn{\alpha_t} given the data up to time \eqn{t}. +} +\details{ +For non-Gaussian models, the filtering is based on the approximate +Gaussian model. +} +\examples{ +x <- cumsum(rnorm(20)) +y <- x + rnorm(20, sd = 0.1) +model <- bsm_lg(y, sd_level = 1, sd_y = 0.1) +ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) +} +\seealso{ +\code{\link{bootstrap_filter}} +} diff --git a/man/logLik.Rd b/man/logLik.Rd index 102459a4..6ca9c392 100644 --- a/man/logLik.Rd +++ b/man/logLik.Rd @@ -1,20 +1,21 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.gaussian} -\alias{logLik.gaussian} -\title{Log-likelihood of a Gaussian State Space Model} -\usage{ -\method{logLik}{gaussian}(object, ...) -} -\arguments{ -\item{object}{Model model.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a linear-Gaussian state space model of \code{bssm} package. -} -\examples{ -model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) -logLik(model) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.gaussian} +\alias{logLik.gaussian} +\title{Log-likelihood of a Gaussian State Space Model} +\usage{ +\method{logLik}{gaussian}(object, ...) +} +\arguments{ +\item{object}{Model model.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a linear-Gaussian state space model of +\code{bssm} package. +} +\examples{ +model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) +logLik(model) +} diff --git a/man/logLik.nongaussian.Rd b/man/logLik.nongaussian.Rd index cae97503..23c1092b 100644 --- a/man/logLik.nongaussian.Rd +++ b/man/logLik.nongaussian.Rd @@ -17,15 +17,17 @@ \arguments{ \item{object}{Model model.} -\item{particles}{Number of samples for particle filter or importance sampling. If 0, +\item{particles}{Number of samples for particle filter or +importance sampling. If 0, approximate log-likelihood based on the Gaussian approximation is returned.} -\item{method}{Sampling method, default is psi-auxiliary filter (\code{"psi"}), -other choices are \code{"bsf"} bootstrap particle filter, and \code{"spdk"}, -which uses the importance sampling approach by Shephard and Pitt (1997) and -Durbin and Koopman (1997).} +\item{method}{Sampling method, default is psi-auxiliary filter +(\code{"psi"}). Other choices are \code{"bsf"} bootstrap particle filter, +and \code{"spdk"}, which uses the importance sampling approach by +Shephard and Pitt (1997) and Durbin and Koopman (1997).} -\item{max_iter}{Maximum number of iterations for Gaussian approximation algorithm.} +\item{max_iter}{Maximum number of iterations for Gaussian approximation +algorithm.} \item{conv_tol}{Tolerance parameter for the approximation algorithm.} @@ -34,7 +36,8 @@ Durbin and Koopman (1997).} \item{...}{Ignored.} } \description{ -Computes the log-likelihood of a non-Gaussian state space model of \code{bssm} package. +Computes the log-likelihood of a non-Gaussian state space model of +\code{bssm} package. } \examples{ model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, @@ -47,3 +50,10 @@ logLik(model2, particles = 0) logLik(model, particles = 10, seed = 1) logLik(model2, particles = 10, seed = 1) } +\references{ +Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation +Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. + +Shephard, N., & Pitt, M. (1997). Likelihood Analysis of +Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. +} diff --git a/man/logLik.ssm_nlg.Rd b/man/logLik.ssm_nlg.Rd index fbdc3d62..2e9603cc 100644 --- a/man/logLik.ssm_nlg.Rd +++ b/man/logLik.ssm_nlg.Rd @@ -1,43 +1,47 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_nlg} -\alias{logLik.ssm_nlg} -\title{Log-likelihood of a Non-linear State Space Model} -\usage{ -\method{logLik}{ssm_nlg}( - object, - particles, - method = "bsf", - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter. If 0, -approximate log-likelihood is returned either based on the Gaussian approximation or EKF, -depending on the \code{method} argument.} - -\item{method}{Sampling method. Default is the bootstrap particle filter (\code{"bsf"}). -Other choices are \code{"psi"} which uses psi-auxiliary filter -(or approximating Gaussian model in the case of \code{particles = 0}), and \code{"ekf"} which -uses EKF-based particle filter (or just EKF approximation in the case of \code{particles = 0}).} - -\item{max_iter}{Maximum number of iterations for gaussian approximation algorithm.} - -\item{conv_tol}{Tolerance parameter for the approximation algorithm.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is used with -\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class \code{ssm_nlg} package. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.ssm_nlg} +\alias{logLik.ssm_nlg} +\title{Log-likelihood of a Non-linear State Space Model} +\usage{ +\method{logLik}{ssm_nlg}( + object, + particles, + method = "bsf", + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{Model model.} + +\item{particles}{Number of samples for particle filter. If 0, +approximate log-likelihood is returned either based on the Gaussian +approximation or EKF, depending on the \code{method} argument.} + +\item{method}{Sampling method. Default is the bootstrap particle filter +(\code{"bsf"}). Other choices are \code{"psi"} which uses +psi-auxiliary filter (or approximating Gaussian model in the case of +\code{particles = 0}), and \code{"ekf"} which uses EKF-based particle +filter (or just EKF approximation in the case of \code{particles = 0}).} + +\item{max_iter}{Maximum number of iterations for the gaussian approximation +algorithm.} + +\item{conv_tol}{Tolerance parameter for the approximation algorithm.} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter +is used with +\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a state space model of class +\code{ssm_nlg} package. +} diff --git a/man/logLik.ssm_sde.Rd b/man/logLik.ssm_sde.Rd index 529f863e..58ce148e 100644 --- a/man/logLik.ssm_sde.Rd +++ b/man/logLik.ssm_sde.Rd @@ -1,28 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_sde} -\alias{logLik.ssm_sde} -\title{Log-likelihood of a State Space Model with SDE dynamics} -\usage{ -\method{logLik}{ssm_sde}( - object, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter.} - -\item{L}{Integer defining the discretization level defined as (2^L).} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class \code{ssm_sde} package. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.ssm_sde} +\alias{logLik.ssm_sde} +\title{Log-likelihood of a State Space Model with SDE dynamics} +\usage{ +\method{logLik}{ssm_sde}( + object, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{Model model.} + +\item{particles}{Number of samples for particle filter.} + +\item{L}{Integer defining the discretization level defined as (2^L).} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a state space model of class +\code{ssm_sde} package. +} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index e39706b4..9fbf8689 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -1,200 +1,202 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_ulg} -\alias{ssm_ulg} -\title{General univariate linear-Gaussian state space models} -\usage{ -ssm_ulg( - y, - Z, - H, - T, - R, - a1, - P1, - init_theta = numeric(0), - D, - C, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as time series (or vector) of length \eqn{n}.} - -\item{Z}{System matrix Z of the observation equation as m x 1 or m x n matrix.} - -\item{H}{Vector of standard deviations. Either a scalar or a vector of length n.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a -m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as a length n vector.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_ulg}. -} -\description{ -Construct an object of class \code{ssm_ulg} by directly defining the corresponding terms of -the model. -} -\details{ -The general univariate linear-Gaussian model is defined using the following -observational and state equations: - -\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here k is the number of disturbance terms which can be less than m, the number of states. - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_ulg}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function and then check -the expected structure of the model components from the output. -} -\examples{ - -# Regression model with time-varying coefficients -set.seed(1) -n <- 100 -x1 <- rnorm(n) -x2 <- rnorm(n) -b1 <- 1 + cumsum(rnorm(n, sd = 0.5)) -b2 <- 2 + cumsum(rnorm(n, sd = 0.1)) -y <- 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) - -Z <- rbind(1, x1, x2) -H <- 0.1 -T <- diag(3) -R <- diag(c(0, 1, 0.1)) -a1 <- rep(0, 3) -P1 <- diag(10, 3) - -# updates the model given the current values of the parameters -update_fn <- function(theta) { - R <- diag(c(0, theta[1], theta[2])) - dim(R) <- c(3, 3, 1) - list(R = R, H = theta[3]) -} -# prior for standard deviations as half-normal(1) -prior_fn <- function(theta) { - if(any(theta < 0)){ - log_p <- -Inf - } else { - log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) - } - log_p -} - -model <- ssm_ulg(y, Z, H, T, R, a1, P1, - init_theta = c(1, 0.1, 0.1), - update_fn = update_fn, prior_fn = prior_fn) - -out <- run_mcmc(model, iter = 10000) -out -sumr <- summary(out, variable = "state") -ts.plot(sumr$Mean, col = 1:3) -lines(b1, col= 2, lty = 2) -lines(b2, col= 3, lty = 2) - -# Perhaps easiest way to construct a general SSM for bssm is to use the -# model building functionality of KFAS: -library("KFAS") - -model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = 5e-4)+ - SSMseasonal(period = 12, sea.type = "trigonometric", Q = 0) + - log(PetrolPrice) + law, data = Seatbelts, H = 0.005) - -# use as_bssm function for conversion, kappa defines the -# prior variance for diffuse states -model_bssm <- as_bssm(model_kfas, kappa = 100) - -# define updating function for parameter estimation -# we can use SSModel and as_bssm functions here as well -# (for large model it is more efficient to do this -# "manually" by constructing only necessary matrices, -# i.e., in this case a list with H and Q) - -updatefn <- function(theta){ - - model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ - SSMseasonal(period = 12, - sea.type = "trigonometric", Q = theta[2]^2) + - log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) - - as_bssm(model_kfas, kappa = 100) -} - -prior <- function(theta) { - if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) -} -init_theta <- rep(1e-2, 3) -c("sd_level", "sd_seasonal", "sd_y") -model_bssm <- as_bssm(model_kfas, kappa = 100, - init_theta = init_theta, - prior_fn = prior, update_fn = updatefn) - -\dontrun{ -out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) -out - -# Above the regression coefficients are modelled as time-invariant latent states. -# Here is an alternative way where we use variable D so that the -# coefficients are part of parameter vector theta: - -updatefn2 <- function(theta) { - # note no PetrolPrice or law variables here - model_kfas2 <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ - SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2), - data = Seatbelts, H = theta[3]^2) - - X <- model.matrix(~ -1 + law + log(PetrolPrice), data = Seatbelts) - D <- t(X \%*\% theta[4:5]) - as_bssm(model_kfas2, D = D, kappa = 100) -} -prior2 <- function(theta) { - if(any(theta[1:3] < 0)) { - -Inf - } else { - sum(dnorm(theta[1:3], 0, 0.1, log = TRUE)) + - sum(dnorm(theta[4:5], 0, 10, log = TRUE)) - } -} -init_theta <- c(rep(1e-2, 3), 0, 0) -names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y", "law", "Petrol") -model_bssm2 <- updatefn2(init_theta) -model_bssm2$theta <- init_theta -model_bssm2$prior_fn <- prior2 -model_bssm2$update_fn <- updatefn2 - -out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) -out2 -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_ulg} +\alias{ssm_ulg} +\title{General univariate linear-Gaussian state space models} +\usage{ +ssm_ulg( + y, + Z, + H, + T, + R, + a1, + P1, + init_theta = numeric(0), + D, + C, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as time series (or vector) of length \eqn{n}.} + +\item{Z}{System matrix Z of the observation equation as m x 1 or m x n matrix.} + +\item{H}{Vector of standard deviations. Either a scalar or a vector of length n.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a +m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as a length n vector.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. See details.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_ulg}. +} +\description{ +Construct an object of class \code{ssm_ulg} by directly defining the +corresponding terms of the model. +} +\details{ +The general univariate linear-Gaussian model is defined using the following +observational and state equations: + +\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. +Here k is the number of disturbance terms which can be less than m, the number of states. + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant wrt. theta. +Note that while you can input say R as m x k matrix for \code{ssm_ulg}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function and then check +the expected structure of the model components from the output. +} +\examples{ + +# Regression model with time-varying coefficients +set.seed(1) +n <- 100 +x1 <- rnorm(n) +x2 <- rnorm(n) +b1 <- 1 + cumsum(rnorm(n, sd = 0.5)) +b2 <- 2 + cumsum(rnorm(n, sd = 0.1)) +y <- 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) + +Z <- rbind(1, x1, x2) +H <- 0.1 +T <- diag(3) +R <- diag(c(0, 1, 0.1)) +a1 <- rep(0, 3) +P1 <- diag(10, 3) + +# updates the model given the current values of the parameters +update_fn <- function(theta) { + R <- diag(c(0, theta[1], theta[2])) + dim(R) <- c(3, 3, 1) + list(R = R, H = theta[3]) +} +# prior for standard deviations as half-normal(1) +prior_fn <- function(theta) { + if(any(theta < 0)){ + log_p <- -Inf + } else { + log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) + } + log_p +} + +model <- ssm_ulg(y, Z, H, T, R, a1, P1, + init_theta = c(1, 0.1, 0.1), + update_fn = update_fn, prior_fn = prior_fn) + +out <- run_mcmc(model, iter = 10000) +out +sumr <- summary(out, variable = "state") +ts.plot(sumr$Mean, col = 1:3) +lines(b1, col= 2, lty = 2) +lines(b2, col= 3, lty = 2) + +# Perhaps easiest way to construct a general SSM for bssm is to use the +# model building functionality of KFAS: +library("KFAS") + +model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = 5e-4)+ + SSMseasonal(period = 12, sea.type = "trigonometric", Q = 0) + + log(PetrolPrice) + law, data = Seatbelts, H = 0.005) + +# use as_bssm function for conversion, kappa defines the +# prior variance for diffuse states +model_bssm <- as_bssm(model_kfas, kappa = 100) + +# define updating function for parameter estimation +# we can use SSModel and as_bssm functions here as well +# (for large model it is more efficient to do this +# "manually" by constructing only necessary matrices, +# i.e., in this case a list with H and Q) + +updatefn <- function(theta){ + + model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ + SSMseasonal(period = 12, + sea.type = "trigonometric", Q = theta[2]^2) + + log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) + + as_bssm(model_kfas, kappa = 100) +} + +prior <- function(theta) { + if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) +} +init_theta <- rep(1e-2, 3) +c("sd_level", "sd_seasonal", "sd_y") +model_bssm <- as_bssm(model_kfas, kappa = 100, + init_theta = init_theta, + prior_fn = prior, update_fn = updatefn) + +\dontrun{ +out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) +out + +# Above the regression coefficients are modelled as time-invariant latent states. +# Here is an alternative way where we use variable D so that the +# coefficients are part of parameter vector theta: + +updatefn2 <- function(theta) { + # note no PetrolPrice or law variables here + model_kfas2 <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ + SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2), + data = Seatbelts, H = theta[3]^2) + + X <- model.matrix(~ -1 + law + log(PetrolPrice), data = Seatbelts) + D <- t(X \%*\% theta[4:5]) + as_bssm(model_kfas2, D = D, kappa = 100) +} +prior2 <- function(theta) { + if(any(theta[1:3] < 0)) { + -Inf + } else { + sum(dnorm(theta[1:3], 0, 0.1, log = TRUE)) + + sum(dnorm(theta[4:5], 0, 10, log = TRUE)) + } +} +init_theta <- c(rep(1e-2, 3), 0, 0) +names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y", "law", "Petrol") +model_bssm2 <- updatefn2(init_theta) +model_bssm2$theta <- init_theta +model_bssm2$prior_fn <- prior2 +model_bssm2$update_fn <- updatefn2 + +out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) +out2 +} +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 355b1de9..10be8262 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -1,1323 +1,1328 @@ -// Generated by using Rcpp::compileAttributes() -> do not edit by hand -// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -#include -#include - -using namespace Rcpp; - -// gaussian_approx_model -Rcpp::List gaussian_approx_model(const Rcpp::List model_, const int model_type); -RcppExport SEXP _bssm_gaussian_approx_model(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_approx_model(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_approx_model_nlg -Rcpp::List gaussian_approx_model_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter); -RcppExport SEXP _bssm_gaussian_approx_model_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_approx_model_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter)); - return rcpp_result_gen; -END_RCPP -} -// bsf -Rcpp::List bsf(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, bool gaussian, const int model_type); -RcppExport SEXP _bssm_bsf(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP gaussianSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< bool >::type gaussian(gaussianSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(bsf(model_, nsim, seed, gaussian, model_type)); - return rcpp_result_gen; -END_RCPP -} -// bsf_smoother -Rcpp::List bsf_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, bool gaussian, const int model_type); -RcppExport SEXP _bssm_bsf_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP gaussianSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< bool >::type gaussian(gaussianSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(bsf_smoother(model_, nsim, seed, gaussian, model_type)); - return rcpp_result_gen; -END_RCPP -} -// bsf_nlg -Rcpp::List bsf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); -RcppExport SEXP _bssm_bsf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(bsf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); - return rcpp_result_gen; -END_RCPP -} -// bsf_smoother_nlg -Rcpp::List bsf_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); -RcppExport SEXP _bssm_bsf_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(bsf_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); - return rcpp_result_gen; -END_RCPP -} -// ekf_nlg -Rcpp::List ekf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); -RcppExport SEXP _bssm_ekf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - rcpp_result_gen = Rcpp::wrap(ekf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); - return rcpp_result_gen; -END_RCPP -} -// ekf_smoother_nlg -Rcpp::List ekf_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); -RcppExport SEXP _bssm_ekf_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - rcpp_result_gen = Rcpp::wrap(ekf_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); - return rcpp_result_gen; -END_RCPP -} -// ekf_fast_smoother_nlg -Rcpp::List ekf_fast_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); -RcppExport SEXP _bssm_ekf_fast_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - rcpp_result_gen = Rcpp::wrap(ekf_fast_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); - return rcpp_result_gen; -END_RCPP -} -// ekpf -Rcpp::List ekpf(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); -RcppExport SEXP _bssm_ekpf(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(ekpf(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); - return rcpp_result_gen; -END_RCPP -} -// ekpf_smoother -Rcpp::List ekpf_smoother(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); -RcppExport SEXP _bssm_ekpf_smoother(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(ekpf_smoother(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); - return rcpp_result_gen; -END_RCPP -} -// importance_sample_ng -Rcpp::List importance_sample_ng(const Rcpp::List model_, unsigned int nsim, bool use_antithetic, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_importance_sample_ng(SEXP model_SEXP, SEXP nsimSEXP, SEXP use_antitheticSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< bool >::type use_antithetic(use_antitheticSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(importance_sample_ng(model_, nsim, use_antithetic, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_kfilter -Rcpp::List gaussian_kfilter(const Rcpp::List model_, const unsigned int model_type); -RcppExport SEXP _bssm_gaussian_kfilter(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_kfilter(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_loglik -double gaussian_loglik(const Rcpp::List model_, const int model_type); -RcppExport SEXP _bssm_gaussian_loglik(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_loglik(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_loglik -double nongaussian_loglik(const Rcpp::List model_, const unsigned int nsim, const unsigned int sampling_method, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_nongaussian_loglik(SEXP model_SEXP, SEXP nsimSEXP, SEXP sampling_methodSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_loglik(model_, nsim, sampling_method, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_loglik -double nonlinear_loglik(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int method); -RcppExport SEXP _bssm_nonlinear_loglik(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP methodSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type method(methodSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_loglik(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_mcmc -Rcpp::List gaussian_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const int model_type); -RcppExport SEXP _bssm_gaussian_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_mcmc(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_pm_mcmc -Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int model_type); -RcppExport SEXP _bssm_nongaussian_pm_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_pm_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_da_mcmc -Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const int model_type); -RcppExport SEXP _bssm_nongaussian_da_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_da_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_is_mcmc -Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int is_type, const int model_type, const bool approx); -RcppExport SEXP _bssm_nongaussian_is_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP is_typeSEXP, SEXP model_typeSEXP, SEXP approxSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_is_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_pm_mcmc -Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_pm_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_pm_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_da_mcmc -Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_da_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_da_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_ekf_mcmc -Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_ekf_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_ekf_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_is_mcmc -Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int is_type, const unsigned int sampling_method, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int output_type, const bool approx); -RcppExport SEXP _bssm_nonlinear_is_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP sampling_methodSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP approxSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_is_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx)); - return rcpp_result_gen; -END_RCPP -} -// R_milstein -double R_milstein(const double x0, const unsigned int L, const double t, const arma::vec& theta, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, bool positive, const unsigned int seed); -RcppExport SEXP _bssm_R_milstein(SEXP x0SEXP, SEXP LSEXP, SEXP tSEXP, SEXP thetaSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP positiveSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); - Rcpp::traits::input_parameter< const double >::type t(tSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(R_milstein(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)); - return rcpp_result_gen; -END_RCPP -} -// suggest_n_nongaussian -arma::vec suggest_n_nongaussian(const Rcpp::List model_, const arma::vec theta, const arma::vec candidates, const unsigned int replications, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_suggest_n_nongaussian(SEXP model_SEXP, SEXP thetaSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const arma::vec >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::vec >::type candidates(candidatesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type replications(replicationsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(suggest_n_nongaussian(model_, theta, candidates, replications, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// suggest_n_nonlinear -arma::vec suggest_n_nonlinear(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const arma::vec theta_map, const arma::vec candidates, const unsigned int replications, const unsigned int seed); -RcppExport SEXP _bssm_suggest_n_nonlinear(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP theta_mapSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const arma::vec >::type theta_map(theta_mapSEXP); - Rcpp::traits::input_parameter< const arma::vec >::type candidates(candidatesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type replications(replicationsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(suggest_n_nonlinear(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed)); - return rcpp_result_gen; -END_RCPP -} -// postcorrection_nongaussian -Rcpp::List postcorrection_nongaussian(const Rcpp::List model_, const int model_type, const unsigned int output_type, const unsigned int nsim, const unsigned int seed, const unsigned int n_threads, const unsigned int is_type, const arma::uvec counts, const arma::mat theta, const arma::cube modes); -RcppExport SEXP _bssm_postcorrection_nongaussian(SEXP model_SEXP, SEXP model_typeSEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP countsSEXP, SEXP thetaSEXP, SEXP modesSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); - Rcpp::traits::input_parameter< const arma::uvec >::type counts(countsSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::cube >::type modes(modesSEXP); - rcpp_result_gen = Rcpp::wrap(postcorrection_nongaussian(model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)); - return rcpp_result_gen; -END_RCPP -} -// postcorrection_nonlinear -Rcpp::List postcorrection_nonlinear(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta_init, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int output_type, const unsigned int nsim, const unsigned int seed, const unsigned int n_threads, const unsigned int is_type, const arma::uvec counts, const arma::mat theta, const arma::cube modes); -RcppExport SEXP _bssm_postcorrection_nonlinear(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP theta_initSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP countsSEXP, SEXP thetaSEXP, SEXP modesSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta_init(theta_initSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); - Rcpp::traits::input_parameter< const arma::uvec >::type counts(countsSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::cube >::type modes(modesSEXP); - rcpp_result_gen = Rcpp::wrap(postcorrection_nonlinear(y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_predict -arma::cube gaussian_predict(const Rcpp::List model_, const arma::mat theta, const arma::mat alpha, const unsigned int predict_type, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_gaussian_predict(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_predict(model_, theta, alpha, predict_type, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_predict -arma::cube nongaussian_predict(const Rcpp::List model_, const arma::mat& theta, const arma::mat& alpha, const unsigned int predict_type, const unsigned int seed, const unsigned int model_type); -RcppExport SEXP _bssm_nongaussian_predict(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_predict(model_, theta, alpha, predict_type, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_predict -arma::cube nonlinear_predict(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const arma::mat& theta, const arma::mat& alpha, const unsigned int predict_type, const unsigned int seed); -RcppExport SEXP _bssm_nonlinear_predict(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_predict(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_predict_past -arma::cube gaussian_predict_past(const Rcpp::List model_, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_gaussian_predict_past(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_predict_past(model_, theta, alpha, predict_type, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nongaussian_predict_past -arma::cube nongaussian_predict_past(const Rcpp::List model_, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed, const unsigned int model_type); -RcppExport SEXP _bssm_nongaussian_predict_past(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_predict_past(model_, theta, alpha, predict_type, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// nonlinear_predict_past -arma::cube nonlinear_predict_past(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed); -RcppExport SEXP _bssm_nonlinear_predict_past(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_predict_past(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_psi_smoother -arma::cube gaussian_psi_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_gaussian_psi_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_psi_smoother(model_, nsim, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// psi_smoother -Rcpp::List psi_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_psi_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(psi_smoother(model_, nsim, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// psi_smoother_nlg -Rcpp::List psi_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter); -RcppExport SEXP _bssm_psi_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); - Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); - rcpp_result_gen = Rcpp::wrap(psi_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter)); - return rcpp_result_gen; -END_RCPP -} -// loglik_sde -double loglik_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); -RcppExport SEXP _bssm_loglik_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(loglik_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); - return rcpp_result_gen; -END_RCPP -} -// bsf_sde -Rcpp::List bsf_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); -RcppExport SEXP _bssm_bsf_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(bsf_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); - return rcpp_result_gen; -END_RCPP -} -// bsf_smoother_sde -Rcpp::List bsf_smoother_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); -RcppExport SEXP _bssm_bsf_smoother_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(bsf_smoother_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); - return rcpp_result_gen; -END_RCPP -} -// sde_pm_mcmc -Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); -RcppExport SEXP _bssm_sde_pm_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_pm_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); - return rcpp_result_gen; -END_RCPP -} -// sde_da_mcmc -Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); -RcppExport SEXP _bssm_sde_da_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_da_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); - return rcpp_result_gen; -END_RCPP -} -// sde_is_mcmc -Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int is_type, const unsigned int n_threads, const unsigned int type); -RcppExport SEXP _bssm_sde_is_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP is_typeSEXP, SEXP n_threadsSEXP, SEXP typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); - Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); - Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); - Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); - Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_is_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type)); - return rcpp_result_gen; -END_RCPP -} -// sde_state_sampler_bsf_is2 -Rcpp::List sde_state_sampler_bsf_is2(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const unsigned int nsim, const unsigned int L_f, const unsigned int seed, const arma::vec& approx_loglik_storage, const arma::mat& theta); -RcppExport SEXP _bssm_sde_state_sampler_bsf_is2(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP nsimSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP approx_loglik_storageSEXP, SEXP thetaSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); - Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); - Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); - Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type approx_loglik_storage(approx_loglik_storageSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); - rcpp_result_gen = Rcpp::wrap(sde_state_sampler_bsf_is2(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_smoother -Rcpp::List gaussian_smoother(const Rcpp::List model_, const int model_type); -RcppExport SEXP _bssm_gaussian_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_smoother(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_ccov_smoother -Rcpp::List gaussian_ccov_smoother(const Rcpp::List model_, const int model_type); -RcppExport SEXP _bssm_gaussian_ccov_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_ccov_smoother(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_fast_smoother -arma::mat gaussian_fast_smoother(const Rcpp::List model_, const int model_type); -RcppExport SEXP _bssm_gaussian_fast_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_fast_smoother(model_, model_type)); - return rcpp_result_gen; -END_RCPP -} -// gaussian_sim_smoother -arma::cube gaussian_sim_smoother(const Rcpp::List model_, const unsigned int nsim, bool use_antithetic, const unsigned int seed, const int model_type); -RcppExport SEXP _bssm_gaussian_sim_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP use_antitheticSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); - Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); - Rcpp::traits::input_parameter< bool >::type use_antithetic(use_antitheticSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_sim_smoother(model_, nsim, use_antithetic, seed, model_type)); - return rcpp_result_gen; -END_RCPP -} -// ukf_nlg -Rcpp::List ukf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const double alpha, const double beta, const double kappa); -RcppExport SEXP _bssm_ukf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP alphaSEXP, SEXP betaSEXP, SEXP kappaSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); - Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); - Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); - Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); - Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); - Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); - Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); - Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); - Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); - Rcpp::traits::input_parameter< const double >::type alpha(alphaSEXP); - Rcpp::traits::input_parameter< const double >::type beta(betaSEXP); - Rcpp::traits::input_parameter< const double >::type kappa(kappaSEXP); - rcpp_result_gen = Rcpp::wrap(ukf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa)); - return rcpp_result_gen; -END_RCPP -} -// conditional_cov -void conditional_cov(arma::cube& Vt, arma::cube& Ct, const bool use_svd); -RcppExport SEXP _bssm_conditional_cov(SEXP VtSEXP, SEXP CtSEXP, SEXP use_svdSEXP) { -BEGIN_RCPP - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< arma::cube& >::type Vt(VtSEXP); - Rcpp::traits::input_parameter< arma::cube& >::type Ct(CtSEXP); - Rcpp::traits::input_parameter< const bool >::type use_svd(use_svdSEXP); - conditional_cov(Vt, Ct, use_svd); - return R_NilValue; -END_RCPP -} -// dmvnorm -double dmvnorm(const arma::vec& x, const arma::vec& mean, const arma::mat& sigma, bool lwr, bool logd); -RcppExport SEXP _bssm_dmvnorm(SEXP xSEXP, SEXP meanSEXP, SEXP sigmaSEXP, SEXP lwrSEXP, SEXP logdSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type mean(meanSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type sigma(sigmaSEXP); - Rcpp::traits::input_parameter< bool >::type lwr(lwrSEXP); - Rcpp::traits::input_parameter< bool >::type logd(logdSEXP); - rcpp_result_gen = Rcpp::wrap(dmvnorm(x, mean, sigma, lwr, logd)); - return rcpp_result_gen; -END_RCPP -} -// precompute_dmvnorm -double precompute_dmvnorm(const arma::mat& sigma, arma::mat& Linv, const arma::uvec& nonzero); -RcppExport SEXP _bssm_precompute_dmvnorm(SEXP sigmaSEXP, SEXP LinvSEXP, SEXP nonzeroSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type sigma(sigmaSEXP); - Rcpp::traits::input_parameter< arma::mat& >::type Linv(LinvSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type nonzero(nonzeroSEXP); - rcpp_result_gen = Rcpp::wrap(precompute_dmvnorm(sigma, Linv, nonzero)); - return rcpp_result_gen; -END_RCPP -} -// fast_dmvnorm -double fast_dmvnorm(const arma::vec& x, const arma::vec& mean, const arma::mat& Linv, const arma::uvec& nonzero, const double constant); -RcppExport SEXP _bssm_fast_dmvnorm(SEXP xSEXP, SEXP meanSEXP, SEXP LinvSEXP, SEXP nonzeroSEXP, SEXP constantSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type mean(meanSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type Linv(LinvSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type nonzero(nonzeroSEXP); - Rcpp::traits::input_parameter< const double >::type constant(constantSEXP); - rcpp_result_gen = Rcpp::wrap(fast_dmvnorm(x, mean, Linv, nonzero, constant)); - return rcpp_result_gen; -END_RCPP -} -// psd_chol -arma::mat psd_chol(const arma::mat& x); -RcppExport SEXP _bssm_psd_chol(SEXP xSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::mat& >::type x(xSEXP); - rcpp_result_gen = Rcpp::wrap(psd_chol(x)); - return rcpp_result_gen; -END_RCPP -} -// stratified_sample -arma::uvec stratified_sample(arma::vec& p, const arma::vec& r, const unsigned int N); -RcppExport SEXP _bssm_stratified_sample(SEXP pSEXP, SEXP rSEXP, SEXP NSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< arma::vec& >::type p(pSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type r(rSEXP); - Rcpp::traits::input_parameter< const unsigned int >::type N(NSEXP); - rcpp_result_gen = Rcpp::wrap(stratified_sample(p, r, N)); - return rcpp_result_gen; -END_RCPP -} - -static const R_CallMethodDef CallEntries[] = { - {"_bssm_gaussian_approx_model", (DL_FUNC) &_bssm_gaussian_approx_model, 2}, - {"_bssm_gaussian_approx_model_nlg", (DL_FUNC) &_bssm_gaussian_approx_model_nlg, 19}, - {"_bssm_bsf", (DL_FUNC) &_bssm_bsf, 5}, - {"_bssm_bsf_smoother", (DL_FUNC) &_bssm_bsf_smoother, 5}, - {"_bssm_bsf_nlg", (DL_FUNC) &_bssm_bsf_nlg, 18}, - {"_bssm_bsf_smoother_nlg", (DL_FUNC) &_bssm_bsf_smoother_nlg, 18}, - {"_bssm_ekf_nlg", (DL_FUNC) &_bssm_ekf_nlg, 17}, - {"_bssm_ekf_smoother_nlg", (DL_FUNC) &_bssm_ekf_smoother_nlg, 17}, - {"_bssm_ekf_fast_smoother_nlg", (DL_FUNC) &_bssm_ekf_fast_smoother_nlg, 17}, - {"_bssm_ekpf", (DL_FUNC) &_bssm_ekpf, 18}, - {"_bssm_ekpf_smoother", (DL_FUNC) &_bssm_ekpf_smoother, 18}, - {"_bssm_importance_sample_ng", (DL_FUNC) &_bssm_importance_sample_ng, 5}, - {"_bssm_gaussian_kfilter", (DL_FUNC) &_bssm_gaussian_kfilter, 2}, - {"_bssm_gaussian_loglik", (DL_FUNC) &_bssm_gaussian_loglik, 2}, - {"_bssm_nongaussian_loglik", (DL_FUNC) &_bssm_nongaussian_loglik, 5}, - {"_bssm_nonlinear_loglik", (DL_FUNC) &_bssm_nonlinear_loglik, 22}, - {"_bssm_gaussian_mcmc", (DL_FUNC) &_bssm_gaussian_mcmc, 12}, - {"_bssm_nongaussian_pm_mcmc", (DL_FUNC) &_bssm_nongaussian_pm_mcmc, 14}, - {"_bssm_nongaussian_da_mcmc", (DL_FUNC) &_bssm_nongaussian_da_mcmc, 14}, - {"_bssm_nongaussian_is_mcmc", (DL_FUNC) &_bssm_nongaussian_is_mcmc, 16}, - {"_bssm_nonlinear_pm_mcmc", (DL_FUNC) &_bssm_nonlinear_pm_mcmc, 31}, - {"_bssm_nonlinear_da_mcmc", (DL_FUNC) &_bssm_nonlinear_da_mcmc, 31}, - {"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 27}, - {"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 33}, - {"_bssm_R_milstein", (DL_FUNC) &_bssm_R_milstein, 9}, - {"_bssm_suggest_n_nongaussian", (DL_FUNC) &_bssm_suggest_n_nongaussian, 6}, - {"_bssm_suggest_n_nonlinear", (DL_FUNC) &_bssm_suggest_n_nonlinear, 20}, - {"_bssm_postcorrection_nongaussian", (DL_FUNC) &_bssm_postcorrection_nongaussian, 10}, - {"_bssm_postcorrection_nonlinear", (DL_FUNC) &_bssm_postcorrection_nonlinear, 24}, - {"_bssm_gaussian_predict", (DL_FUNC) &_bssm_gaussian_predict, 6}, - {"_bssm_nongaussian_predict", (DL_FUNC) &_bssm_nongaussian_predict, 6}, - {"_bssm_nonlinear_predict", (DL_FUNC) &_bssm_nonlinear_predict, 19}, - {"_bssm_gaussian_predict_past", (DL_FUNC) &_bssm_gaussian_predict_past, 6}, - {"_bssm_nongaussian_predict_past", (DL_FUNC) &_bssm_nongaussian_predict_past, 6}, - {"_bssm_nonlinear_predict_past", (DL_FUNC) &_bssm_nonlinear_predict_past, 19}, - {"_bssm_gaussian_psi_smoother", (DL_FUNC) &_bssm_gaussian_psi_smoother, 4}, - {"_bssm_psi_smoother", (DL_FUNC) &_bssm_psi_smoother, 4}, - {"_bssm_psi_smoother_nlg", (DL_FUNC) &_bssm_psi_smoother_nlg, 21}, - {"_bssm_loglik_sde", (DL_FUNC) &_bssm_loglik_sde, 12}, - {"_bssm_bsf_sde", (DL_FUNC) &_bssm_bsf_sde, 12}, - {"_bssm_bsf_smoother_sde", (DL_FUNC) &_bssm_bsf_smoother_sde, 12}, - {"_bssm_sde_pm_mcmc", (DL_FUNC) &_bssm_sde_pm_mcmc, 20}, - {"_bssm_sde_da_mcmc", (DL_FUNC) &_bssm_sde_da_mcmc, 21}, - {"_bssm_sde_is_mcmc", (DL_FUNC) &_bssm_sde_is_mcmc, 23}, - {"_bssm_sde_state_sampler_bsf_is2", (DL_FUNC) &_bssm_sde_state_sampler_bsf_is2, 13}, - {"_bssm_gaussian_smoother", (DL_FUNC) &_bssm_gaussian_smoother, 2}, - {"_bssm_gaussian_ccov_smoother", (DL_FUNC) &_bssm_gaussian_ccov_smoother, 2}, - {"_bssm_gaussian_fast_smoother", (DL_FUNC) &_bssm_gaussian_fast_smoother, 2}, - {"_bssm_gaussian_sim_smoother", (DL_FUNC) &_bssm_gaussian_sim_smoother, 5}, - {"_bssm_ukf_nlg", (DL_FUNC) &_bssm_ukf_nlg, 19}, - {"_bssm_conditional_cov", (DL_FUNC) &_bssm_conditional_cov, 3}, - {"_bssm_dmvnorm", (DL_FUNC) &_bssm_dmvnorm, 5}, - {"_bssm_precompute_dmvnorm", (DL_FUNC) &_bssm_precompute_dmvnorm, 3}, - {"_bssm_fast_dmvnorm", (DL_FUNC) &_bssm_fast_dmvnorm, 5}, - {"_bssm_psd_chol", (DL_FUNC) &_bssm_psd_chol, 1}, - {"_bssm_stratified_sample", (DL_FUNC) &_bssm_stratified_sample, 3}, - {NULL, NULL, 0} -}; - -RcppExport void R_init_bssm(DllInfo *dll) { - R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); - R_useDynamicSymbols(dll, FALSE); -} +// Generated by using Rcpp::compileAttributes() -> do not edit by hand +// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#include +#include + +using namespace Rcpp; + +#ifdef RCPP_USE_GLOBAL_ROSTREAM +Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); +Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); +#endif + +// gaussian_approx_model +Rcpp::List gaussian_approx_model(const Rcpp::List model_, const int model_type); +RcppExport SEXP _bssm_gaussian_approx_model(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_approx_model(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_approx_model_nlg +Rcpp::List gaussian_approx_model_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter); +RcppExport SEXP _bssm_gaussian_approx_model_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_approx_model_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter)); + return rcpp_result_gen; +END_RCPP +} +// bsf +Rcpp::List bsf(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, bool gaussian, const int model_type); +RcppExport SEXP _bssm_bsf(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP gaussianSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< bool >::type gaussian(gaussianSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(bsf(model_, nsim, seed, gaussian, model_type)); + return rcpp_result_gen; +END_RCPP +} +// bsf_smoother +Rcpp::List bsf_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, bool gaussian, const int model_type); +RcppExport SEXP _bssm_bsf_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP gaussianSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< bool >::type gaussian(gaussianSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(bsf_smoother(model_, nsim, seed, gaussian, model_type)); + return rcpp_result_gen; +END_RCPP +} +// bsf_nlg +Rcpp::List bsf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); +RcppExport SEXP _bssm_bsf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(bsf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); + return rcpp_result_gen; +END_RCPP +} +// bsf_smoother_nlg +Rcpp::List bsf_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); +RcppExport SEXP _bssm_bsf_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(bsf_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); + return rcpp_result_gen; +END_RCPP +} +// ekf_nlg +Rcpp::List ekf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); +RcppExport SEXP _bssm_ekf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + rcpp_result_gen = Rcpp::wrap(ekf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); + return rcpp_result_gen; +END_RCPP +} +// ekf_smoother_nlg +Rcpp::List ekf_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); +RcppExport SEXP _bssm_ekf_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + rcpp_result_gen = Rcpp::wrap(ekf_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); + return rcpp_result_gen; +END_RCPP +} +// ekf_fast_smoother_nlg +Rcpp::List ekf_fast_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int iekf_iter); +RcppExport SEXP _bssm_ekf_fast_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP iekf_iterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + rcpp_result_gen = Rcpp::wrap(ekf_fast_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)); + return rcpp_result_gen; +END_RCPP +} +// ekpf +Rcpp::List ekpf(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); +RcppExport SEXP _bssm_ekpf(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(ekpf(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); + return rcpp_result_gen; +END_RCPP +} +// ekpf_smoother +Rcpp::List ekpf_smoother(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed); +RcppExport SEXP _bssm_ekpf_smoother(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(ekpf_smoother(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)); + return rcpp_result_gen; +END_RCPP +} +// importance_sample_ng +Rcpp::List importance_sample_ng(const Rcpp::List model_, unsigned int nsim, bool use_antithetic, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_importance_sample_ng(SEXP model_SEXP, SEXP nsimSEXP, SEXP use_antitheticSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< bool >::type use_antithetic(use_antitheticSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(importance_sample_ng(model_, nsim, use_antithetic, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_kfilter +Rcpp::List gaussian_kfilter(const Rcpp::List model_, const unsigned int model_type); +RcppExport SEXP _bssm_gaussian_kfilter(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_kfilter(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_loglik +double gaussian_loglik(const Rcpp::List model_, const int model_type); +RcppExport SEXP _bssm_gaussian_loglik(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_loglik(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_loglik +double nongaussian_loglik(const Rcpp::List model_, const unsigned int nsim, const unsigned int sampling_method, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_nongaussian_loglik(SEXP model_SEXP, SEXP nsimSEXP, SEXP sampling_methodSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_loglik(model_, nsim, sampling_method, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_loglik +double nonlinear_loglik(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int method); +RcppExport SEXP _bssm_nonlinear_loglik(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP methodSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type method(methodSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_loglik(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_mcmc +Rcpp::List gaussian_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const int model_type); +RcppExport SEXP _bssm_gaussian_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_mcmc(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_pm_mcmc +Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int model_type); +RcppExport SEXP _bssm_nongaussian_pm_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_pm_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_da_mcmc +Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const int model_type); +RcppExport SEXP _bssm_nongaussian_da_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_da_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_is_mcmc +Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int is_type, const int model_type, const bool approx); +RcppExport SEXP _bssm_nongaussian_is_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP is_typeSEXP, SEXP model_typeSEXP, SEXP approxSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_is_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_pm_mcmc +Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); +RcppExport SEXP _bssm_nonlinear_pm_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_pm_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_da_mcmc +Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); +RcppExport SEXP _bssm_nonlinear_da_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_da_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_ekf_mcmc +Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int iekf_iter, const unsigned int output_type); +RcppExport SEXP _bssm_nonlinear_ekf_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_ekf_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_is_mcmc +Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int is_type, const unsigned int sampling_method, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int output_type, const bool approx); +RcppExport SEXP _bssm_nonlinear_is_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP sampling_methodSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP approxSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_is_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx)); + return rcpp_result_gen; +END_RCPP +} +// R_milstein +double R_milstein(const double x0, const unsigned int L, const double t, const arma::vec& theta, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, bool positive, const unsigned int seed); +RcppExport SEXP _bssm_R_milstein(SEXP x0SEXP, SEXP LSEXP, SEXP tSEXP, SEXP thetaSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP positiveSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); + Rcpp::traits::input_parameter< const double >::type t(tSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(R_milstein(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)); + return rcpp_result_gen; +END_RCPP +} +// suggest_n_nongaussian +arma::vec suggest_n_nongaussian(const Rcpp::List model_, const arma::vec theta, const arma::vec candidates, const unsigned int replications, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_suggest_n_nongaussian(SEXP model_SEXP, SEXP thetaSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const arma::vec >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::vec >::type candidates(candidatesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type replications(replicationsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(suggest_n_nongaussian(model_, theta, candidates, replications, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// suggest_n_nonlinear +arma::vec suggest_n_nonlinear(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const arma::vec theta_map, const arma::vec candidates, const unsigned int replications, const unsigned int seed); +RcppExport SEXP _bssm_suggest_n_nonlinear(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP theta_mapSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const arma::vec >::type theta_map(theta_mapSEXP); + Rcpp::traits::input_parameter< const arma::vec >::type candidates(candidatesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type replications(replicationsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(suggest_n_nonlinear(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed)); + return rcpp_result_gen; +END_RCPP +} +// postcorrection_nongaussian +Rcpp::List postcorrection_nongaussian(const Rcpp::List model_, const int model_type, const unsigned int output_type, const unsigned int nsim, const unsigned int seed, const unsigned int n_threads, const unsigned int is_type, const arma::uvec counts, const arma::mat theta, const arma::cube modes); +RcppExport SEXP _bssm_postcorrection_nongaussian(SEXP model_SEXP, SEXP model_typeSEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP countsSEXP, SEXP thetaSEXP, SEXP modesSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); + Rcpp::traits::input_parameter< const arma::uvec >::type counts(countsSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::cube >::type modes(modesSEXP); + rcpp_result_gen = Rcpp::wrap(postcorrection_nongaussian(model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)); + return rcpp_result_gen; +END_RCPP +} +// postcorrection_nonlinear +Rcpp::List postcorrection_nonlinear(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta_init, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int output_type, const unsigned int nsim, const unsigned int seed, const unsigned int n_threads, const unsigned int is_type, const arma::uvec counts, const arma::mat theta, const arma::cube modes); +RcppExport SEXP _bssm_postcorrection_nonlinear(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP theta_initSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP countsSEXP, SEXP thetaSEXP, SEXP modesSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta_init(theta_initSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); + Rcpp::traits::input_parameter< const arma::uvec >::type counts(countsSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::cube >::type modes(modesSEXP); + rcpp_result_gen = Rcpp::wrap(postcorrection_nonlinear(y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_predict +arma::cube gaussian_predict(const Rcpp::List model_, const arma::mat theta, const arma::mat alpha, const unsigned int predict_type, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_gaussian_predict(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const arma::mat >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_predict(model_, theta, alpha, predict_type, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_predict +arma::cube nongaussian_predict(const Rcpp::List model_, const arma::mat& theta, const arma::mat& alpha, const unsigned int predict_type, const unsigned int seed, const unsigned int model_type); +RcppExport SEXP _bssm_nongaussian_predict(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_predict(model_, theta, alpha, predict_type, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_predict +arma::cube nonlinear_predict(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const arma::mat& theta, const arma::mat& alpha, const unsigned int predict_type, const unsigned int seed); +RcppExport SEXP _bssm_nonlinear_predict(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_predict(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_predict_past +arma::cube gaussian_predict_past(const Rcpp::List model_, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_gaussian_predict_past(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_predict_past(model_, theta, alpha, predict_type, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nongaussian_predict_past +arma::cube nongaussian_predict_past(const Rcpp::List model_, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed, const unsigned int model_type); +RcppExport SEXP _bssm_nongaussian_predict_past(SEXP model_SEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_predict_past(model_, theta, alpha, predict_type, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// nonlinear_predict_past +arma::cube nonlinear_predict_past(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const arma::mat& theta, const arma::cube& alpha, const unsigned int predict_type, const unsigned int seed); +RcppExport SEXP _bssm_nonlinear_predict_past(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP thetaSEXP, SEXP alphaSEXP, SEXP predict_typeSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const arma::cube& >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type predict_type(predict_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_predict_past(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_psi_smoother +arma::cube gaussian_psi_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_gaussian_psi_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_psi_smoother(model_, nsim, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// psi_smoother +Rcpp::List psi_smoother(const Rcpp::List model_, const unsigned int nsim, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_psi_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(psi_smoother(model_, nsim, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// psi_smoother_nlg +Rcpp::List psi_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const unsigned int nsim, const unsigned int seed, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter); +RcppExport SEXP _bssm_psi_smoother_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP nsimSEXP, SEXP seedSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type max_iter(max_iterSEXP); + Rcpp::traits::input_parameter< const double >::type conv_tol(conv_tolSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); + rcpp_result_gen = Rcpp::wrap(psi_smoother_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter)); + return rcpp_result_gen; +END_RCPP +} +// loglik_sde +double loglik_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); +RcppExport SEXP _bssm_loglik_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(loglik_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); + return rcpp_result_gen; +END_RCPP +} +// bsf_sde +Rcpp::List bsf_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); +RcppExport SEXP _bssm_bsf_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(bsf_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); + return rcpp_result_gen; +END_RCPP +} +// bsf_smoother_sde +Rcpp::List bsf_smoother_sde(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed); +RcppExport SEXP _bssm_bsf_smoother_sde(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(bsf_smoother_sde(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)); + return rcpp_result_gen; +END_RCPP +} +// sde_pm_mcmc +Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); +RcppExport SEXP _bssm_sde_pm_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L(LSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); + rcpp_result_gen = Rcpp::wrap(sde_pm_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); + return rcpp_result_gen; +END_RCPP +} +// sde_da_mcmc +Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); +RcppExport SEXP _bssm_sde_da_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); + rcpp_result_gen = Rcpp::wrap(sde_da_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); + return rcpp_result_gen; +END_RCPP +} +// sde_is_mcmc +Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int is_type, const unsigned int n_threads, const unsigned int type); +RcppExport SEXP _bssm_sde_is_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP is_typeSEXP, SEXP n_threadsSEXP, SEXP typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type burnin(burninSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type thin(thinSEXP); + Rcpp::traits::input_parameter< const double >::type gamma(gammaSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); + Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); + rcpp_result_gen = Rcpp::wrap(sde_is_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type)); + return rcpp_result_gen; +END_RCPP +} +// sde_state_sampler_bsf_is2 +Rcpp::List sde_state_sampler_bsf_is2(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const unsigned int nsim, const unsigned int L_f, const unsigned int seed, const arma::vec& approx_loglik_storage, const arma::mat& theta); +RcppExport SEXP _bssm_sde_state_sampler_bsf_is2(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP nsimSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP approx_loglik_storageSEXP, SEXP thetaSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type y(ySEXP); + Rcpp::traits::input_parameter< const double >::type x0(x0SEXP); + Rcpp::traits::input_parameter< const bool >::type positive(positiveSEXP); + Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf_pntr(log_prior_pdf_pntrSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_obs_density_pntr(log_obs_density_pntrSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type approx_loglik_storage(approx_loglik_storageSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type theta(thetaSEXP); + rcpp_result_gen = Rcpp::wrap(sde_state_sampler_bsf_is2(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_smoother +Rcpp::List gaussian_smoother(const Rcpp::List model_, const int model_type); +RcppExport SEXP _bssm_gaussian_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_smoother(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_ccov_smoother +Rcpp::List gaussian_ccov_smoother(const Rcpp::List model_, const int model_type); +RcppExport SEXP _bssm_gaussian_ccov_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_ccov_smoother(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_fast_smoother +arma::mat gaussian_fast_smoother(const Rcpp::List model_, const int model_type); +RcppExport SEXP _bssm_gaussian_fast_smoother(SEXP model_SEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_fast_smoother(model_, model_type)); + return rcpp_result_gen; +END_RCPP +} +// gaussian_sim_smoother +arma::cube gaussian_sim_smoother(const Rcpp::List model_, const unsigned int nsim, bool use_antithetic, const unsigned int seed, const int model_type); +RcppExport SEXP _bssm_gaussian_sim_smoother(SEXP model_SEXP, SEXP nsimSEXP, SEXP use_antitheticSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List >::type model_(model_SEXP); + Rcpp::traits::input_parameter< const unsigned int >::type nsim(nsimSEXP); + Rcpp::traits::input_parameter< bool >::type use_antithetic(use_antitheticSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_sim_smoother(model_, nsim, use_antithetic, seed, model_type)); + return rcpp_result_gen; +END_RCPP +} +// ukf_nlg +Rcpp::List ukf_nlg(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const unsigned int n_states, const unsigned int n_etas, const arma::uvec& time_varying, const double alpha, const double beta, const double kappa); +RcppExport SEXP _bssm_ukf_nlg(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP time_varyingSEXP, SEXP alphaSEXP, SEXP betaSEXP, SEXP kappaSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type y(ySEXP); + Rcpp::traits::input_parameter< SEXP >::type Z(ZSEXP); + Rcpp::traits::input_parameter< SEXP >::type H(HSEXP); + Rcpp::traits::input_parameter< SEXP >::type T(TSEXP); + Rcpp::traits::input_parameter< SEXP >::type R(RSEXP); + Rcpp::traits::input_parameter< SEXP >::type Zg(ZgSEXP); + Rcpp::traits::input_parameter< SEXP >::type Tg(TgSEXP); + Rcpp::traits::input_parameter< SEXP >::type a1(a1SEXP); + Rcpp::traits::input_parameter< SEXP >::type P1(P1SEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< SEXP >::type log_prior_pdf(log_prior_pdfSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type known_params(known_paramsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type known_tv_params(known_tv_paramsSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_states(n_statesSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type n_etas(n_etasSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type time_varying(time_varyingSEXP); + Rcpp::traits::input_parameter< const double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< const double >::type beta(betaSEXP); + Rcpp::traits::input_parameter< const double >::type kappa(kappaSEXP); + rcpp_result_gen = Rcpp::wrap(ukf_nlg(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa)); + return rcpp_result_gen; +END_RCPP +} +// conditional_cov +void conditional_cov(arma::cube& Vt, arma::cube& Ct, const bool use_svd); +RcppExport SEXP _bssm_conditional_cov(SEXP VtSEXP, SEXP CtSEXP, SEXP use_svdSEXP) { +BEGIN_RCPP + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::cube& >::type Vt(VtSEXP); + Rcpp::traits::input_parameter< arma::cube& >::type Ct(CtSEXP); + Rcpp::traits::input_parameter< const bool >::type use_svd(use_svdSEXP); + conditional_cov(Vt, Ct, use_svd); + return R_NilValue; +END_RCPP +} +// dmvnorm +double dmvnorm(const arma::vec& x, const arma::vec& mean, const arma::mat& sigma, bool lwr, bool logd); +RcppExport SEXP _bssm_dmvnorm(SEXP xSEXP, SEXP meanSEXP, SEXP sigmaSEXP, SEXP lwrSEXP, SEXP logdSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type mean(meanSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type sigma(sigmaSEXP); + Rcpp::traits::input_parameter< bool >::type lwr(lwrSEXP); + Rcpp::traits::input_parameter< bool >::type logd(logdSEXP); + rcpp_result_gen = Rcpp::wrap(dmvnorm(x, mean, sigma, lwr, logd)); + return rcpp_result_gen; +END_RCPP +} +// precompute_dmvnorm +double precompute_dmvnorm(const arma::mat& sigma, arma::mat& Linv, const arma::uvec& nonzero); +RcppExport SEXP _bssm_precompute_dmvnorm(SEXP sigmaSEXP, SEXP LinvSEXP, SEXP nonzeroSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type sigma(sigmaSEXP); + Rcpp::traits::input_parameter< arma::mat& >::type Linv(LinvSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type nonzero(nonzeroSEXP); + rcpp_result_gen = Rcpp::wrap(precompute_dmvnorm(sigma, Linv, nonzero)); + return rcpp_result_gen; +END_RCPP +} +// fast_dmvnorm +double fast_dmvnorm(const arma::vec& x, const arma::vec& mean, const arma::mat& Linv, const arma::uvec& nonzero, const double constant); +RcppExport SEXP _bssm_fast_dmvnorm(SEXP xSEXP, SEXP meanSEXP, SEXP LinvSEXP, SEXP nonzeroSEXP, SEXP constantSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec& >::type x(xSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type mean(meanSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Linv(LinvSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type nonzero(nonzeroSEXP); + Rcpp::traits::input_parameter< const double >::type constant(constantSEXP); + rcpp_result_gen = Rcpp::wrap(fast_dmvnorm(x, mean, Linv, nonzero, constant)); + return rcpp_result_gen; +END_RCPP +} +// psd_chol +arma::mat psd_chol(const arma::mat& x); +RcppExport SEXP _bssm_psd_chol(SEXP xSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type x(xSEXP); + rcpp_result_gen = Rcpp::wrap(psd_chol(x)); + return rcpp_result_gen; +END_RCPP +} +// stratified_sample +arma::uvec stratified_sample(arma::vec& p, const arma::vec& r, const unsigned int N); +RcppExport SEXP _bssm_stratified_sample(SEXP pSEXP, SEXP rSEXP, SEXP NSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::vec& >::type p(pSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type r(rSEXP); + Rcpp::traits::input_parameter< const unsigned int >::type N(NSEXP); + rcpp_result_gen = Rcpp::wrap(stratified_sample(p, r, N)); + return rcpp_result_gen; +END_RCPP +} + +static const R_CallMethodDef CallEntries[] = { + {"_bssm_gaussian_approx_model", (DL_FUNC) &_bssm_gaussian_approx_model, 2}, + {"_bssm_gaussian_approx_model_nlg", (DL_FUNC) &_bssm_gaussian_approx_model_nlg, 19}, + {"_bssm_bsf", (DL_FUNC) &_bssm_bsf, 5}, + {"_bssm_bsf_smoother", (DL_FUNC) &_bssm_bsf_smoother, 5}, + {"_bssm_bsf_nlg", (DL_FUNC) &_bssm_bsf_nlg, 18}, + {"_bssm_bsf_smoother_nlg", (DL_FUNC) &_bssm_bsf_smoother_nlg, 18}, + {"_bssm_ekf_nlg", (DL_FUNC) &_bssm_ekf_nlg, 17}, + {"_bssm_ekf_smoother_nlg", (DL_FUNC) &_bssm_ekf_smoother_nlg, 17}, + {"_bssm_ekf_fast_smoother_nlg", (DL_FUNC) &_bssm_ekf_fast_smoother_nlg, 17}, + {"_bssm_ekpf", (DL_FUNC) &_bssm_ekpf, 18}, + {"_bssm_ekpf_smoother", (DL_FUNC) &_bssm_ekpf_smoother, 18}, + {"_bssm_importance_sample_ng", (DL_FUNC) &_bssm_importance_sample_ng, 5}, + {"_bssm_gaussian_kfilter", (DL_FUNC) &_bssm_gaussian_kfilter, 2}, + {"_bssm_gaussian_loglik", (DL_FUNC) &_bssm_gaussian_loglik, 2}, + {"_bssm_nongaussian_loglik", (DL_FUNC) &_bssm_nongaussian_loglik, 5}, + {"_bssm_nonlinear_loglik", (DL_FUNC) &_bssm_nonlinear_loglik, 22}, + {"_bssm_gaussian_mcmc", (DL_FUNC) &_bssm_gaussian_mcmc, 12}, + {"_bssm_nongaussian_pm_mcmc", (DL_FUNC) &_bssm_nongaussian_pm_mcmc, 14}, + {"_bssm_nongaussian_da_mcmc", (DL_FUNC) &_bssm_nongaussian_da_mcmc, 14}, + {"_bssm_nongaussian_is_mcmc", (DL_FUNC) &_bssm_nongaussian_is_mcmc, 16}, + {"_bssm_nonlinear_pm_mcmc", (DL_FUNC) &_bssm_nonlinear_pm_mcmc, 31}, + {"_bssm_nonlinear_da_mcmc", (DL_FUNC) &_bssm_nonlinear_da_mcmc, 31}, + {"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 27}, + {"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 33}, + {"_bssm_R_milstein", (DL_FUNC) &_bssm_R_milstein, 9}, + {"_bssm_suggest_n_nongaussian", (DL_FUNC) &_bssm_suggest_n_nongaussian, 6}, + {"_bssm_suggest_n_nonlinear", (DL_FUNC) &_bssm_suggest_n_nonlinear, 20}, + {"_bssm_postcorrection_nongaussian", (DL_FUNC) &_bssm_postcorrection_nongaussian, 10}, + {"_bssm_postcorrection_nonlinear", (DL_FUNC) &_bssm_postcorrection_nonlinear, 24}, + {"_bssm_gaussian_predict", (DL_FUNC) &_bssm_gaussian_predict, 6}, + {"_bssm_nongaussian_predict", (DL_FUNC) &_bssm_nongaussian_predict, 6}, + {"_bssm_nonlinear_predict", (DL_FUNC) &_bssm_nonlinear_predict, 19}, + {"_bssm_gaussian_predict_past", (DL_FUNC) &_bssm_gaussian_predict_past, 6}, + {"_bssm_nongaussian_predict_past", (DL_FUNC) &_bssm_nongaussian_predict_past, 6}, + {"_bssm_nonlinear_predict_past", (DL_FUNC) &_bssm_nonlinear_predict_past, 19}, + {"_bssm_gaussian_psi_smoother", (DL_FUNC) &_bssm_gaussian_psi_smoother, 4}, + {"_bssm_psi_smoother", (DL_FUNC) &_bssm_psi_smoother, 4}, + {"_bssm_psi_smoother_nlg", (DL_FUNC) &_bssm_psi_smoother_nlg, 21}, + {"_bssm_loglik_sde", (DL_FUNC) &_bssm_loglik_sde, 12}, + {"_bssm_bsf_sde", (DL_FUNC) &_bssm_bsf_sde, 12}, + {"_bssm_bsf_smoother_sde", (DL_FUNC) &_bssm_bsf_smoother_sde, 12}, + {"_bssm_sde_pm_mcmc", (DL_FUNC) &_bssm_sde_pm_mcmc, 20}, + {"_bssm_sde_da_mcmc", (DL_FUNC) &_bssm_sde_da_mcmc, 21}, + {"_bssm_sde_is_mcmc", (DL_FUNC) &_bssm_sde_is_mcmc, 23}, + {"_bssm_sde_state_sampler_bsf_is2", (DL_FUNC) &_bssm_sde_state_sampler_bsf_is2, 13}, + {"_bssm_gaussian_smoother", (DL_FUNC) &_bssm_gaussian_smoother, 2}, + {"_bssm_gaussian_ccov_smoother", (DL_FUNC) &_bssm_gaussian_ccov_smoother, 2}, + {"_bssm_gaussian_fast_smoother", (DL_FUNC) &_bssm_gaussian_fast_smoother, 2}, + {"_bssm_gaussian_sim_smoother", (DL_FUNC) &_bssm_gaussian_sim_smoother, 5}, + {"_bssm_ukf_nlg", (DL_FUNC) &_bssm_ukf_nlg, 19}, + {"_bssm_conditional_cov", (DL_FUNC) &_bssm_conditional_cov, 3}, + {"_bssm_dmvnorm", (DL_FUNC) &_bssm_dmvnorm, 5}, + {"_bssm_precompute_dmvnorm", (DL_FUNC) &_bssm_precompute_dmvnorm, 3}, + {"_bssm_fast_dmvnorm", (DL_FUNC) &_bssm_fast_dmvnorm, 5}, + {"_bssm_psd_chol", (DL_FUNC) &_bssm_psd_chol, 1}, + {"_bssm_stratified_sample", (DL_FUNC) &_bssm_stratified_sample, 3}, + {NULL, NULL, 0} +}; + +RcppExport void R_init_bssm(DllInfo *dll) { + R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); + R_useDynamicSymbols(dll, FALSE); +} diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index 17dccb22..d6729dab 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -19,32 +19,42 @@ test_that("Gaussian approximation results of bssm and KFAS coincide",{ test_that("Gaussian approximation works for SV model",{ set.seed(123) - expect_error(model_bssm <- svm(rnorm(5), sigma = uniform(1,0,10), rho = uniform(0.950, 0, 1), + expect_error(model_bssm <- svm(rnorm(5), sigma = uniform(1,0,10), + rho = uniform(0.950, 0, 1), sd_ar = uniform(0.1,0,1)), NA) - expect_error(approx_bssm <- gaussian_approx(model_bssm, max_iter = 2, conv_tol = 1e-8), NA) + expect_error(approx_bssm <- gaussian_approx(model_bssm, max_iter = 2, + conv_tol = 1e-8), NA) expect_equivalent(c(-1.47548927809174, -11.2190916117862, - 0.263154138901814, -121.519769682058, -36.0386937004332), approx_bssm$y[1:5]) + 0.263154138901814, -121.519769682058, -36.0386937004332), + approx_bssm$y[1:5]) expect_equivalent(c(2.01061310553144, 4.84658294043645, 0.712674409714633, 15.6217737012134, 8.54936618861792), approx_bssm$H[1:5]) expect_equivalent(c(-0.0999179077423753, -0.101594935319188, - -0.0985572218431492, -0.103275329248674, -0.103028083292436), fast_smoother(approx_bssm)[1:5]) + -0.0985572218431492, -0.103275329248674, -0.103028083292436), + fast_smoother(approx_bssm)[1:5]) }) test_that("results for poisson GLM are equal to glm function",{ - d <- data.frame(treatment = gl(3,3), outcome = gl(3,1,9), counts = c(18,17,15,20,10,20,25,13,12)) - glm_poisson <- glm(counts ~ outcome + treatment, data = d, family = poisson()) + d <- data.frame(treatment = gl(3,3), outcome = gl(3,1,9), + counts = c(18,17,15,20,10,20,25,13,12)) + glm_poisson <- glm(counts ~ outcome + treatment, data = d, + family = poisson()) xreg <- model.matrix(~ outcome + treatment, data = d) - expect_error(model_poisson1 <- ssm_ung(d$counts, Z = t(xreg), T = diag(5), R = diag(0, 5), - P1 = diag(1e7, 5), distribution = 'poisson', state_names = colnames(xreg)), NA) + expect_error(model_poisson1 <- ssm_ung(d$counts, Z = t(xreg), T = diag(5), + R = diag(0, 5), + P1 = diag(1e7, 5), distribution = 'poisson', + state_names = colnames(xreg)), NA) expect_error(sm <- smoother(model_poisson1), NA) expect_equal(sm$alphahat[1,], coef(glm_poisson)) expect_equal(sm$V[,,1], vcov(glm_poisson)) xreg <- model.matrix(~ outcome + treatment, data = d)[, -1] - expect_error(model_poisson2 <- bsm_ng(d$counts, sd_level = 0, xreg = xreg, P1=matrix(1e7), + expect_error(model_poisson2 <- bsm_ng(d$counts, sd_level = 0, xreg = xreg, + P1 = matrix(1e7), beta = normal(coef(glm_poisson)[-1], 0, 10), distribution = 'poisson'), NA) - expect_equivalent(smoother(model_poisson2)$alphahat[1,], coef(glm_poisson)[1]) + expect_equivalent(smoother(model_poisson2)$alphahat[1,], + coef(glm_poisson)[1]) model_poisson3 <- model_poisson1 model_poisson3$P1[] <- 0 @@ -94,7 +104,7 @@ test_that("results for binomial GLM are equal to glm function",{ }) -test_that("state estimates for negative binomial GLM are equal to glm function",{ +test_that("state estimates for NB GLM are equal to glm function",{ library(MASS) set.seed(123) offs <- quine$Days + sample(10:20, size = nrow(quine), replace = TRUE) @@ -108,7 +118,7 @@ test_that("state estimates for negative binomial GLM are equal to glm function", expect_equivalent(sm$alphahat[1], unname(coef(glm_nb)[1])) }) -test_that("multivariate iid model gives same results as two univariate models", { +test_that("Two iid model gives same results as two univariate models", { set.seed(1) y <- matrix(rbinom(20, size = 10, prob = plogis(rnorm(20, sd = 0.5))), 10, 2) expect_error(model <- ssm_mng(y, Z = diag(2), phi = 2, @@ -125,3 +135,141 @@ test_that("multivariate iid model gives same results as two univariate models", cbind(gaussian_approx(model1, conv_tol = 1e-12)$y, gaussian_approx(model2, conv_tol = 1e-12)$y), tol = 1e-6) }) + +test_that("Gaussian approximation works for nonlinear models", { + + Rcpp::sourceCpp(code = ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = 0; + return a1; + } + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1, 1); + P1(0,0) = 1; + return P1; + } + + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat R(1, 1); + R(0, 0) = 1; + return R; + } + + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = 1.0; + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = 1.0; + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0); //jacobian term + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation + // returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear function returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + }') + pntrs <- create_xptrs() + set.seed(1) + y <- cumsum(rnorm(10)) + rnorm(10) + model_nlg <- ssm_nlg(y = y, a1=pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c("log_H" = 0), log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = c("state")) + model_gaussian <- bsm_lg(y, sd_y=1, sd_level = 1, P1 = 1) + expect_equal( + logLik(model_nlg, method = "ekf", particles = 0), + logLik(gaussian_approx(model_nlg))) + expect_equal(logLik(model_gaussian), logLik(gaussian_approx(model_nlg))) +}) diff --git a/tests/testthat/test_as_bssm.R b/tests/testthat/test_as_bssm.R index a59ba022..afbec26f 100644 --- a/tests/testthat/test_as_bssm.R +++ b/tests/testthat/test_as_bssm.R @@ -5,10 +5,13 @@ test_that("Test conversion from SSModel to ssm_ulg",{ library(KFAS) model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(2, 2), P1 = diag(2e3, 2)), H = 2) - expect_error(model_bssm <- ssm_ulg(y = ts(1:10), Z = matrix(c(1, 0), 2, 1), H = sqrt(2), - T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(sqrt(2), 2), c(2, 2, 1)), + expect_error(model_bssm <- ssm_ulg(y = ts(1:10), Z = matrix(c(1, 0), 2, 1), + H = sqrt(2), + T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(sqrt(2), 2), c(2, 2, 1)), a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), init_theta = c(0,0)), NA) - expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0, 0)), NA) + expect_error(conv_model_bssm <- as_bssm(model_KFAS, + init_theta = c(0, 0)), NA) expect_equivalent(model_bssm, conv_model_bssm) expect_equivalent(logLik(conv_model_bssm), logLik(model_KFAS)) @@ -18,9 +21,11 @@ test_that("Test conversion from SSModel to ssm_ung",{ library(KFAS) model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(2, 2), P1 = diag(2e3, 2)), u = 2, distribution = "negative binomial") - expect_error(model_bssm <- ssm_ung(y = ts(1:10), Z = matrix(c(1, 0), 2, 1), phi = 2, - T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(sqrt(2), 2), c(2, 2, 1)), - a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), distribution = "negative binomial", + expect_error(model_bssm <- ssm_ung(y = ts(1:10), Z = matrix(c(1, 0), 2, 1), + phi = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(sqrt(2), 2), c(2, 2, 1)), + a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), + distribution = "negative binomial", state_names = c("level", "slope"), init_theta = c(0,0)), NA) expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0,0)), NA) expect_equivalent(model_bssm, conv_model_bssm) diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index f9a565ff..a19c993b 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -33,14 +33,15 @@ test_that("results for multivariate Gaussian model are comparable to KFAS",{ diag(kfas_model$P1) <- 50 diag(kfas_model$P1inf) <- 0 - kfas_model$H <- structure(c(0.00544500509177812, 0.00437558178720609, 0.00437558178720609, - 0.00885692410165593), .Dim = c(2L, 2L, 1L)) - kfas_model$R <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0152150188066314, 0.0144897116711475 + kfas_model$H <- structure(c(0.00544500509177812, 0.00437558178720609, + 0.00437558178720609, 0.00885692410165593), .Dim = c(2L, 2L, 1L)) + kfas_model$R <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0152150188066314, 0.0144897116711475 ), .Dim = c(29L, 1L, 1L), .Dimnames = list(c("log(PetrolPrice).front", "log(kms).front", "log(PetrolPrice).rear", "log(kms).rear", "law.front", - "sea_trig1.front", "sea_trig*1.front", "sea_trig2.front", "sea_trig*2.front", - "sea_trig3.front", "sea_trig*3.front", "sea_trig4.front", "sea_trig*4.front", + "sea_trig1.front", "sea_trig*1.front", "sea_trig2.front", + "sea_trig*2.front", "sea_trig3.front", "sea_trig*3.front", + "sea_trig4.front", "sea_trig*4.front", "sea_trig5.front", "sea_trig*5.front", "sea_trig6.front", "sea_trig1.rear", "sea_trig*1.rear", "sea_trig2.rear", "sea_trig*2.rear", "sea_trig3.rear", "sea_trig*3.rear", "sea_trig4.rear", "sea_trig*4.rear", "sea_trig5.rear", @@ -67,7 +68,8 @@ test_that("different smoothers give identical results",{ test_that("results for Poisson model are comparable to KFAS",{ library("KFAS") set.seed(1) - model_KFAS <- SSModel(rpois(10, exp(0.2) * (2:11)) ~ SSMtrend(2, Q = list(0.01^2, 0)), + model_KFAS <- SSModel(rpois(10, exp(0.2) * (2:11)) ~ + SSMtrend(2, Q = list(0.01^2, 0)), distribution = "poisson", u = 2:11) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 @@ -89,7 +91,8 @@ test_that("results for Poisson model are comparable to KFAS",{ test_that("results for binomial model are comparable to KFAS", { library("KFAS") set.seed(1) - model_KFAS <- SSModel(rbinom(10, 2:11, 0.4) ~ SSMtrend(2, Q = list(0.01^2, 0)), + model_KFAS <- SSModel(rbinom(10, 2:11, 0.4) ~ + SSMtrend(2, Q = list(0.01^2, 0)), distribution = "binomial", u = 2:11) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 @@ -107,7 +110,7 @@ test_that("results for binomial model are comparable to KFAS", { expect_equivalent(out_KFAS$V, out_bssm$Vt) }) -test_that("results for multivariate non-Gaussian model are comparable to KFAS", { +test_that("results for bivariate non-Gaussian model are comparable to KFAS", { library("KFAS") set.seed(1) n <- 10 @@ -133,16 +136,19 @@ test_that("results for multivariate non-Gaussian model are comparable to KFAS", expect_equivalent(logLik(model_KFAS, nsim = 0), logLik(model_bssm, particles = 0), tol = 1e-8) expect_equivalent(logLik(model_KFAS, nsim = 100, seed = 1), - logLik(model_bssm, particles = 100, method = "spdk", seed = 1), tolerance = 1) + logLik(model_bssm, particles = 100, method = "spdk", seed = 1), + tolerance = 1) expect_equivalent( logLik(model_bssm, particles = 100, method = "psi", seed = 1), - logLik(model_bssm, particles = 100, method = "spdk", seed = 1), tolerance = 1) + logLik(model_bssm, particles = 100, method = "spdk", seed = 1), + tolerance = 1) # note large tolerance due to the sd of bsf expect_equivalent( logLik(model_bssm, particles = 100, method = "psi", seed = 1), - logLik(model_bssm, particles = 100, method = "bsf", seed = 1), tolerance = 10) + logLik(model_bssm, particles = 100, method = "bsf", seed = 1), + tolerance = 10) out_KFAS <- KFS(model_KFAS) expect_error(out_bssm <- smoother(model_bssm), NA) @@ -171,12 +177,14 @@ test_that("multivariate normal pdf works", { expect_equivalent(logp1, -14.0607446337904, tolerance = 1e-6) chola <- t(chol(a)) - logp2 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), chola, TRUE, TRUE), NA) + logp2 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), chola, TRUE, TRUE), + NA) expect_equivalent(logp2, logp1, tolerance = 1e-8) b <- matrix(0, 3, 3) constant <- bssm:::precompute_dmvnorm(a, b, 0:2) - expect_equivalent(logp1, bssm:::fast_dmvnorm(1:3, -0.1*(3:1), b, 0:2, constant), tolerance = 1e-8) + expect_equivalent(logp1, + bssm:::fast_dmvnorm(1:3, -0.1*(3:1), b, 0:2, constant), tolerance = 1e-8) a[2,] <- a[, 2] <- 0 logp3 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) diff --git a/tests/testthat/test_bootstrap_filter.R b/tests/testthat/test_bootstrap_filter.R index bc00920b..c8ec6c45 100644 --- a/tests/testthat/test_bootstrap_filter.R +++ b/tests/testthat/test_bootstrap_filter.R @@ -3,10 +3,13 @@ context("Test that bootstrap_filter works") test_that("Test that bsm_lg gives identical results with ssm_ulg",{ - expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, - T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(2, 2), c(2, 2, 1)), - a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), NA) - expect_error(bsf_ssm_ulg <- bootstrap_filter(model_ssm_ulg, 10, seed = 1), NA) + expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), + H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(2, 2), c(2, 2, 1)), + a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), + NA) + expect_error(bsf_ssm_ulg <- bootstrap_filter(model_ssm_ulg, 10, seed = 1), + NA) expect_error(model_bsm <- bsm_lg(1:10, sd_level = 2, sd_slope = 2, sd_y = 2, P1 = diag(2, 2)), NA) expect_error(bsf_bsm <- bootstrap_filter(model_bsm, 10, seed = 1), NA) @@ -17,10 +20,13 @@ test_that("Test that bsm_lg gives identical results with ssm_ulg",{ tol <- 1e-8 test_that("Test that gaussian bsf still works",{ - expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, - T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(2, 2), c(2, 2, 1)), - a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), NA) - expect_error(bsf_ssm_ulg <- bootstrap_filter(model_ssm_ulg, 10, seed = 1), NA) + expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), + H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(2, 2), c(2, 2, 1)), + a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), + NA) + expect_error(bsf_ssm_ulg <- bootstrap_filter(model_ssm_ulg, 10, seed = 1), + NA) expect_gte(min(bsf_ssm_ulg$weights), 0) expect_lt(max(bsf_ssm_ulg$weights), Inf) expect_true(is.finite(bsf_ssm_ulg$logLik)) @@ -30,8 +36,8 @@ test_that("Test that gaussian bsf still works",{ test_that("Test that poisson bsm_ng still works",{ - expect_error(model <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, P1 = diag(2, 2), - distribution = "poisson"), NA) + expect_error(model <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, + P1 = diag(2, 2), distribution = "poisson"), NA) expect_error(bsf_poisson <- bootstrap_filter(model, 10, seed = 1), NA) expect_gte(min(bsf_poisson$weights), 0) @@ -43,7 +49,8 @@ test_that("Test that poisson bsm_ng still works",{ test_that("Test that binomial bsm_ng still works",{ - expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, sd_slope = 2, P1 = diag(2, 2), + expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, + sd_slope = 2, P1 = diag(2, 2), distribution = "binomial"), NA) expect_error(bsf_binomial <- bootstrap_filter(model, 10, seed = 1), NA) @@ -59,7 +66,8 @@ test_that("Test that binomial bsm_ng still works",{ test_that("Test that negative binomial bsm_ng still works",{ - expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, sd_slope = 2, P1 = diag(2, 2), + expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, + sd_slope = 2, P1 = diag(2, 2), distribution = "negative binomial", phi = 0.1, u = 2), NA) expect_error(bsf_nbinomial <- bootstrap_filter(model, 10, seed = 1), NA) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 25ca6cf4..dbcc3cf3 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -11,14 +11,19 @@ test_that("MCMC results for Gaussian model are correct",{ expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1)[-14], run_mcmc(model_bssm, iter = 100, seed = 1)[-14]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")[-15], + expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "summary")[-15], run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")[-15]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")[-13], + expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "theta")[-13], run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")[-13]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")$theta, + expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "theta")$theta, run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")$theta) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")$acceptance_rate, - run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")$acceptance_rate) + expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "theta")$acceptance_rate, + run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "summary")$acceptance_rate) expect_gt(mcmc_bsm$acceptance_rate, 0) expect_gte(min(mcmc_bsm$theta), 0) @@ -30,18 +35,25 @@ test_that("MCMC results for Gaussian model are correct",{ test_that("DA-MCMC results for Poisson model are correct",{ set.seed(123) - model_bssm <- bsm_ng(rpois(10, exp(0.2) * (2:11)), P1 = diag(2, 2), sd_slope = 0, - sd_level = uniform(2, 0, 10), u = 2:11, distribution = "poisson") + model_bssm <- bsm_ng(rpois(10, exp(0.2) * (2:11)), P1 = diag(2, 2), + sd_slope = 0, sd_level = uniform(2, 0, 10), u = 2:11, + distribution = "poisson") expect_error(mcmc_poisson <- run_mcmc(model_bssm, mcmc_type = "da", iter = 100, particles = 5, seed = 42), NA) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, particles = 5)[-14], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, particles = 5)[-14]) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, output_type = "summary", particles = 5)[-15], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, output_type = "summary", particles = 5)[-15]) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, output_type = "theta", particles = 5)[-13], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, output_type = "theta", particles = 5)[-13]) + expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + particles = 5)[-14], + run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + particles = 5)[-14]) + expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + output_type = "summary", particles = 5)[-15], + run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + output_type = "summary", particles = 5)[-15]) + expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + output_type = "theta", particles = 5)[-13], + run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, + output_type = "theta", particles = 5)[-13]) expect_gt(mcmc_poisson$acceptance_rate, 0) expect_gte(min(mcmc_poisson$theta), 0) @@ -58,15 +70,18 @@ test_that("MCMC results for SV model using IS-correction are correct",{ expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", seed = 1)[-16], - run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", seed = 1)[-16]) + run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", + seed = 1)[-16]) expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1)[-16], - run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1)[-16]) + run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", + seed = 1)[-16]) expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is3", seed = 1)[-16], - run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is3", seed = 1)[-16]) + run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is3", + seed = 1)[-16]) expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi")[-16], diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index 0517da5d..fe8677cb 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -20,7 +20,8 @@ test_that("proper arguments for bsm don't throw an error",{ expect_error(bsm_lg(1:10, uniform(0, 0, 1), 1), NA) expect_error(bsm_lg(1:10, 1, 1, uniform(0, 0, 1)), NA) expect_error(bsm_lg(1:10, 1, 1, 1, 1, period = 3), NA) - expect_error(bsm_lg(1:10, 1, 1, 1, 1, period = 3, xreg = matrix(1:10, 10), beta = normal(0,0,10)), NA) + expect_error(bsm_lg(1:10, 1, 1, 1, 1, period = 3, xreg = matrix(1:10, 10), + beta = normal(0,0,10)), NA) }) @@ -32,7 +33,8 @@ test_that("bad argument values for bsm_ng throws an error",{ expect_error(bsm_ng(1:10, sd_level = "character", distribution = "poisson")) expect_error(bsm_ng(1:10, sd_y = Inf, distribution = "poisson")) expect_error(bsm_ng(1:10, no_argument = 5, distribution = "poisson")) - expect_error(bsm_ng(1:10, xreg = matrix(1:20), beta = uniform(0, 0, 1), distribution = "poisson")) + expect_error(bsm_ng(1:10, xreg = matrix(1:20), beta = uniform(0, 0, 1), + distribution = "poisson")) expect_error(bsm_ng(1:10, xreg = 1:10, beta = NA, distribution = "poisson")) expect_error(bsm_ng(1:10, 1, 1, a1 = 1, distribution = "poisson")) expect_error(bsm_ng(1:10, 1, 1, 1, 1, distribution = "poisson")) diff --git a/vignettes/ssm_nlg_template.cpp b/vignettes/ssm_nlg_template.cpp index de257d44..2badca6d 100644 --- a/vignettes/ssm_nlg_template.cpp +++ b/vignettes/ssm_nlg_template.cpp @@ -104,7 +104,7 @@ arma::mat T_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& th return Tg; } -// # log-prior pdf for theta +// log-prior pdf for theta // [[Rcpp::export]] double log_prior_pdf(const arma::vec& theta) { From 2f61c1be4c42449dcecf79171e679dab9522abc4 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 15:16:39 +0300 Subject: [PATCH 011/180] linting --- R/approx.R | 4 +- R/as.data.frame.mcmc_output.R | 9 +-- R/as_bssm.R | 26 +++--- R/bootstrap_filter.R | 10 +-- R/check_arguments.R | 38 ++++----- R/ekpf_filter.R | 2 +- R/init_mode.R | 4 +- R/loglik.R | 12 ++- R/model_type.R | 2 +- R/models.R | 41 +++++----- R/particle_smoother.R | 20 ++--- R/post_correction.R | 4 +- R/predict.R | 69 ++++++++-------- R/print_mcmc.R | 49 ++++++------ R/priors.R | 36 ++++----- R/run_mcmc.R | 106 ++++++++++++++----------- R/zzz.R | 2 +- tests/testthat/test_approx.R | 48 +++++------ tests/testthat/test_as_bssm.R | 16 ++-- tests/testthat/test_basics.R | 28 +++---- tests/testthat/test_bootstrap_filter.R | 18 ++--- tests/testthat/test_is.R | 11 ++- tests/testthat/test_mcmc.R | 10 +-- tests/testthat/test_models.R | 20 ++--- 24 files changed, 293 insertions(+), 292 deletions(-) diff --git a/R/approx.R b/R/approx.R index 6fe0401b..5471652b 100644 --- a/R/approx.R +++ b/R/approx.R @@ -41,11 +41,11 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, duplicates.ok = TRUE) - 1 out <- gaussian_approx_model(model, model_type(model)) - if(ncol(out$y) == 1) { + if (ncol(out$y) == 1) { out$y <- ts(c(out$y), start = start(model$y), end = end(model$y), frequency = frequency(model$y)) D <- model$D - if(length(model$beta) > 0) D <- as.numeric(D) + t(model$xreg %*% model$beta) + if (length(model$beta) > 0) D <- as.numeric(D) + t(model$xreg %*% model$beta) approx_model <- ssm_ulg(y = out$y, Z = model$Z, H = out$H, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, init_theta = model$theta, D = D, C = model$C, state_names = names(model$a1), diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index 46749161..e856d8d0 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -48,14 +48,14 @@ as.data.frame.mcmc_output <- function(x, if (expand) { values <- suppressWarnings(expand_sample(x, "theta")) iters <- seq(x$burnin + 1, x$iter, by = x$thin) - weights <- if(x$mcmc_type %in% paste0("is", 1:3)) { + weights <- if (x$mcmc_type %in% paste0("is", 1:3)) { rep(x$weights, times = x$counts) } else 1 } else { values <- x$theta iters <- x$burnin + cumsum(x$counts) weights <- - x$counts * (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) + x$counts * (if (x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } d <- data.frame(iter = iters, value = as.numeric(values), @@ -68,14 +68,14 @@ as.data.frame.mcmc_output <- function(x, values <- aperm(x$alpha[times, states, rep(seq_len(nrow(x$theta)), times = x$counts), drop = FALSE], 3:1) iters <- seq(x$burnin + 1, x$iter, by = x$thin) - weights <- if(x$mcmc_type %in% paste0("is", 1:3)) { + weights <- if (x$mcmc_type %in% paste0("is", 1:3)) { rep(x$weights, times = x$counts) } else 1 } else { values <- aperm(x$alpha[times, states, , drop = FALSE], 3:1) iters <- x$burnin + cumsum(x$counts) weights <- x$counts * - (if(x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) + (if (x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } times <- time(ts(seq_len(nrow(x$alpha)), start = attr(x, "ts")$start, @@ -88,4 +88,3 @@ as.data.frame.mcmc_output <- function(x, } d } - diff --git a/R/as_bssm.R b/R/as_bssm.R index a94a6be9..5eaf5714 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -53,7 +53,6 @@ as_bssm <- function(model, kappa = 100, ...) { if (any(model$distribution != "gaussian")) { if (attr(model, "p") == 1) { - if (model$distribution == "negative binomial" && length(unique(model$u)) > 1) { stop(paste("Time-varying dispersion parameter for negative binomial", @@ -88,37 +87,37 @@ as_bssm <- function(model, kappa = 100, ...) { } else { phi <- numeric(attr(model, "p")) u <- model$u - for(i in 1:attr(model, "p")) { + for (i in 1:attr(model, "p")) { switch(model$distribution[i], poisson = { phi[i] <- 1 - u[,i] <- model$u[,i] + u[, i] <- model$u[, i] }, binomial = { phi[i] <- 1 - u[,i] <- model$u[,i] + u[, i] <- model$u[, i] }, gamma = { - if(length(unique(model$u[,i])) > 1) + if (length(unique(model$u[, i])) > 1) stop(paste0("Time-varying shape parameter for gamma is not", "(yet) supported in 'bssm'.", sep = " ")) - phi[i] <- model$u[1,i] - u[,i] <- 1 # no exposure for Gamma in KFAS + phi[i] <- model$u[1, i] + u[, i] <- 1 # no exposure for Gamma in KFAS }, "negative binomial" = { - if(length(unique(model$u[,i])) > 1) + if (length(unique(model$u[, i])) > 1) stop(paste("Time-varying dispersion parameter for negative", "binomial is not (yet) supported in 'bssm'.", sep = " ")) - phi[i] <- model$u[1,i] - u[,i] <- 1 # no exposure for NB in KFAS + phi[i] <- model$u[1, i] + u[, i] <- 1 # no exposure for NB in KFAS }, gaussian = { - if(length(unique(model$u[,i])) > 1) + if (length(unique(model$u[, i])) > 1) stop(paste("Time-varying standard deviation for gaussian", "distribution with non-gaussian series is not supported", "in 'bssm'.", sep = " ")) - phi[i] <- sqrt(model$u[1,i]) - u[,i] <- 1 + phi[i] <- sqrt(model$u[1, i]) + u[, i] <- 1 }) } @@ -149,4 +148,3 @@ as_bssm <- function(model, kappa = 100, ...) { out } - diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index ae168432..90f0473c 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -35,7 +35,7 @@ bootstrap_filter <- function(model, particles, ...) { bootstrap_filter.gaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { particles <- match.call(expand.dots = TRUE)$particles if (!is.null(particles)) particles <- particles } @@ -66,7 +66,7 @@ bootstrap_filter.gaussian <- function(model, particles, bootstrap_filter.nongaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste0("Argument `nsim` is deprecated. Use argument `particles`", @@ -96,7 +96,7 @@ bootstrap_filter.nongaussian <- function(model, particles, bootstrap_filter.ssm_nlg <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -126,9 +126,9 @@ bootstrap_filter.ssm_nlg <- function(model, particles, bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { - if(L < 1) stop("Discretization level L must be larger than 0.") + if (L < 1) stop("Discretization level L must be larger than 0.") - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", diff --git a/R/check_arguments.R b/R/check_arguments.R index 4e3ab96a..1441f582 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -1,7 +1,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(!is.na(x))) { - if(multivariate) { + if (multivariate) { if (!is.matrix(x)) { stop("Argument y must be a numeric matrix or multivariate ts object.") } @@ -9,11 +9,11 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { stop("Argument y must be a numeric vector or ts object.") } - if(distribution != "gaussian" && any(na.omit(x) < 0)) { + if (distribution != "gaussian" && any(na.omit(x) < 0)) { stop(paste0("Negative values not allowed for ", distribution, " distribution. ")) } else { - if(distribution %in% + if (distribution %in% c("negative binomial", "binomial", "poisson") && any(na.omit(x != as.integer(x)))) { stop(paste0("Non-integer values not allowed for ", distribution, @@ -32,14 +32,14 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } check_distribution <- function(x, distribution) { - for(i in seq_len(ncol(x))) { - if(distribution[i] != "gaussian" && any(na.omit(x[,i]) < 0)) { + for (i in seq_len(ncol(x))) { + if (distribution[i] != "gaussian" && any(na.omit(x[, i]) < 0)) { stop(paste0("Negative values not allowed for ", distribution[i], " distribution. ")) } else { - if(distribution[i] %in% + if (distribution[i] %in% c("negative binomial", "binomial", "poisson") && - any(na.omit(x[,i] != as.integer(x[,i])))) { + any(na.omit(x[, i] != as.integer(x[, i])))) { stop(paste0("Non-integer values not allowed for ", distribution[i], " distribution. ")) } @@ -120,7 +120,7 @@ check_u <- function(x, multivariate = FALSE) { if (any(x < 0)) { stop("All values of 'u' must be non-negative.") } - if(multivariate) { + if (multivariate) { if (!is.matrix(x) && !is.numeric(x)) { stop("Argument 'u' must be a numeric matrix or multivariate ts object.") } @@ -140,17 +140,17 @@ check_prior <- function(x, name) { } check_target <- function(target) { - if(length(target) > 1 || target >= 1 || target <= 0) { + if (length(target) > 1 || target >= 1 || target <= 0) { stop("Argument 'target' must be on interval (0, 1).") } } check_D <- function(x, p, n) { - if(missing(x)) { - if(p == 1) 0 else matrix(0, p, 1) + if (missing(x)) { + if (p == 1) 0 else matrix(0, p, 1) } else { - if(p == 1) { + if (p == 1) { if (!(length(x) %in% c(1, n))) { stop(paste("'D' must be a scalar or length n, where n is the number of", "observations.", sep = " ")) @@ -167,10 +167,10 @@ check_D <- function(x, p, n) { } check_C <- function(x, m, n) { - if(missing(x)) { + if (missing(x)) { matrix(0, m, 1) } else { - if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1,n))) { + if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1, n))) { stop(paste("'C' must be m x 1 or m x n matrix, where m is", "the number of states.", sep = " ")) } @@ -179,13 +179,13 @@ check_C <- function(x, m, n) { } create_regression <- function(beta, xreg, n) { - if(is.null(xreg)) { + if (is.null(xreg)) { list(xreg = matrix(0, 0, 0), coefs = numeric(0), beta = NULL) } else { if (missing(beta) || is.null(beta)) { stop("No prior defined for beta. ") } else { - if(!is_prior(beta) && !is_prior_list(beta)) { + if (!is_prior(beta) && !is_prior_list(beta)) { stop(paste("Prior for beta must be of class 'bssm_prior' or", "'bssm_prior_list.", sep = " " )) } else { @@ -195,7 +195,7 @@ create_regression <- function(beta, xreg, n) { check_xreg(xreg, n) nx <- ncol(xreg) if (nx == 1 && is_prior_list(beta)) beta <- beta[[1]] - if(nx > 1) { + if (nx > 1) { coefs <- vapply(beta, "[[", "init", FUN.VALUE = 1) } else { coefs <- beta$init @@ -212,7 +212,7 @@ create_regression <- function(beta, xreg, n) { } check_Z <- function(x, p, n) { - if(p == 1) { + if (p == 1) { if (length(x) == 1) { dim(x) <- c(1, 1) } else { @@ -298,7 +298,7 @@ check_P1 <- function(x, m) { } check_H <- function(x, p, n) { - if(p == 1) { + if (p == 1) { if (!(length(x) %in% c(1, n))) { stop(paste("'H' must be a scalar or length n, where n is the length of", "the time series y", sep = " ")) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 3a22ce40..9063c0c4 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -23,7 +23,7 @@ ekpf_filter <- function(object, particles, ...) { ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument", diff --git a/R/init_mode.R b/R/init_mode.R index 0d3d209b..e85a4fc9 100644 --- a/R/init_mode.R +++ b/R/init_mode.R @@ -9,7 +9,7 @@ init_mode <- function(y, u, distribution) { y <- log(y) }, binomial = { - y <- qlogis((ifelse(is.na(y), 0.5, y) + 0.5)/(u + 1)) + y <- qlogis((ifelse(is.na(y), 0.5, y) + 0.5) / (u + 1)) }, gamma = { y <- y / u @@ -18,7 +18,7 @@ init_mode <- function(y, u, distribution) { }, "negative binomial" = { y <- y / u - y[is.na(y) | y < 1/6] <- 1/6 + y[is.na(y) | y < 1 / 6] <- 1 / 6 y <- log(y) }, gaussian = { diff --git a/R/loglik.R b/R/loglik.R index 32edc3b9..d848bd88 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -54,12 +54,12 @@ logLik.gaussian <- function(object, ...) { #' logLik(model2, particles = 10, seed = 1) logLik.nongaussian <- function(object, particles, method = "psi", max_iter = 100, conv_tol = 1e-8, - seed = sample(.Machine$integer.max, size = 1),...) { + seed = sample(.Machine$integer.max, size = 1), ...) { object$max_iter <- max_iter object$conv_tol <- conv_tol - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -107,7 +107,7 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -142,8 +142,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", #' @export logLik.ssm_sde <- function(object, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { - if(L <= 0) stop("Discretization level L must be larger than 0.") - if(missing(particles)) { + if (L <= 0) stop("Discretization level L must be larger than 0.") + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -156,5 +156,3 @@ logLik.ssm_sde <- function(object, particles, L, object$prior_pdf, object$obs_pdf, object$theta, particles, L, seed) } - - diff --git a/R/model_type.R b/R/model_type.R index 240b3ce5..3140b170 100644 --- a/R/model_type.R +++ b/R/model_type.R @@ -1,5 +1,5 @@ model_type <- function(model) { - if(inherits(model, "gaussian")) { + if (inherits(model, "gaussian")) { switch(class(model)[1], "ssm_mlg" = 0L, "ssm_ulg" = 1L, diff --git a/R/models.R b/R/models.R index 47eea35d..10233248 100644 --- a/R/models.R +++ b/R/models.R @@ -1,6 +1,10 @@ ## placeholder functions for fixed models -default_prior_fn <- function(theta) {0} -default_update_fn <- function(theta) {} +default_prior_fn <- function(theta) { + 0 +} +default_update_fn <- function(theta) { + +} #' #' General univariate linear-Gaussian state space models #' @@ -81,10 +85,10 @@ default_update_fn <- function(theta) {} #' } #' # prior for standard deviations as half-normal(1) #' prior_fn <- function(theta) { -#' if(any(theta < 0)){ -#' log_p <- -Inf +#' if(any(theta < 0)) { +#' log_p <- -Inf #' } else { -#' log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) +#' log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) #' } #' log_p #' } @@ -118,7 +122,7 @@ default_update_fn <- function(theta) {} #' # "manually" by constructing only necessary matrices, #' # i.e., in this case a list with H and Q) #' -#' updatefn <- function(theta){ +#' updatefn <- function(theta) { #' #' model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ #' SSMseasonal(period = 12, @@ -190,11 +194,10 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), # create R R <- check_R(R) - k <- dim(R)[2] a1 <- check_a1(a1, m) - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) D <- check_D(D, 1L, n) C <- check_C(C, m, n) @@ -207,7 +210,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), rownames(Z) <- colnames(T) <- rownames(T) <- rownames(R) <- names(a1) <- rownames(P1) <- colnames(P1) <- state_names - if(is.null(names(init_theta)) && length(init_theta) > 0) + if (is.null(names(init_theta)) && length(init_theta) > 0) names(init_theta) <- paste0("theta_", seq_along(init_theta)) @@ -215,7 +218,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, a1 = a1, P1 = P1, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, - xreg = matrix(0,0,0), beta = numeric(0)), class = c("ssm_ulg", "gaussian")) + xreg = matrix(0, 0, 0), beta = numeric(0)), class = c("ssm_ulg", "gaussian")) } #' General univariate non-Gaussian state space model #' @@ -308,11 +311,10 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, # create R R <- check_R(R) - k <- dim(R)[2] a1 <- check_a1(a1, m) - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) D <- check_D(D, 1L, n) C <- check_C(C, m, n) @@ -341,7 +343,7 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, initial_mode = initial_mode, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE, - xreg = matrix(0,0,0), beta = numeric(0)), + xreg = matrix(0, 0, 0), beta = numeric(0)), class = c("ssm_ung", "nongaussian")) } @@ -426,10 +428,9 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), # create R R <- check_R(R) - k <- dim(R)[2] a1 <- check_a1(a1, m) - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) D <- check_D(D, p, n) C <- check_C(C, m, n) @@ -535,10 +536,9 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, # create R R <- check_R(R) - k <- dim(R)[2] a1 <- check_a1(a1, m) - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) D <- check_D(D, p, n) C <- check_C(C, m, n) @@ -687,7 +687,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, if (missing(P1)) { P1 <- diag(100, m) } else { - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) } @@ -915,7 +915,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, if (missing(P1)) { P1 <- diag(100, m) } else { - P1 <- check_P1(P1, m, m) + P1 <- check_P1(P1, m) } if (slope) { @@ -1379,7 +1379,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, known_params = NA, known_tv_params = matrix(NA), n_states, n_etas, log_prior_pdf, time_varying = rep(TRUE, 4), - state_names = paste0("state",1:n_states)) { + state_names = paste0("state", 1:n_states)) { if (is.null(dim(y))) { dim(y) <- c(length(y), 1) @@ -1465,7 +1465,6 @@ ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive) { check_y(y) - n <- length(y) structure(list(y = as.ts(y), drift = drift, diffusion = diffusion, diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 32c069d3..473d89e9 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -75,7 +75,7 @@ particle_smoother <- function(model, particles, ...) { particle_smoother.gaussian <- function(model, particles, method = "psi", seed = sample(.Machine$integer.max, size = 1), ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -84,19 +84,19 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", } } - if(method == "psi") { + if (method == "psi") { out <- list() out$alpha <- gaussian_psi_smoother(model, particles, seed, model_type(model)) out$alphahat <- t(apply(out$alpha, 1:2, mean)) - if(ncol(out$alphahat) == 1L) { + if (ncol(out$alphahat) == 1L) { out$Vt <- array(apply(out$alpha[1, , ], 1, var), c(1, 1, nrow(out$alphahat))) } else { out$Vt <- array(NA, c(ncol(out$alphahat), ncol(out$alphahat), nrow(out$alphahat))) - for(i in seq_len(nrow(out$alphahat))) { - out$Vt[,, i] <- cov(t(out$alpha[,i,])) + for (i in seq_len(nrow(out$alphahat))) { + out$Vt[, , i] <- cov(t(out$alpha[, i, ])) } } } else { @@ -120,7 +120,7 @@ particle_smoother.nongaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -137,7 +137,7 @@ particle_smoother.nongaussian <- function(model, particles, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 - if(method == "psi") { + if (method == "psi") { out <- psi_smoother(model, particles, seed, model_type(model)) } else { out <- bsf_smoother(model, particles, seed, FALSE, model_type(model)) @@ -160,7 +160,7 @@ particle_smoother.ssm_nlg <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -208,9 +208,9 @@ particle_smoother.ssm_nlg <- function(model, particles, particle_smoother.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { - if(L < 1) stop("Discretization level L must be larger than 0.") + if (L < 1) stop("Discretization level L must be larger than 0.") - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", diff --git a/R/post_correction.R b/R/post_correction.R index b787273d..a7063d66 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -1,6 +1,6 @@ # Get MAP estimate of theta get_map <- function(x) { - x$theta[which.max(x$posterior),] + x$theta[which.max(x$posterior), ] } #' Suggest Number of Particles for \eqn{\psi}-APF Post-correction @@ -255,7 +255,7 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, } } mcmc_output$time <- - rbind("approx" = mcmc_output$time, "postcorrection" = proc.time() - a)[,1:3] + rbind("approx" = mcmc_output$time, "postcorrection" = proc.time() - a)[, 1:3] mcmc_output$mcmc_type <- paste0("is", is_type) mcmc_output$seed <- c(mcmc_output$seed, seed) mcmc_output$call <- c(mcmc_output$call, match.call()) diff --git a/R/predict.R b/R/predict.R index 6cd2c02b..b6370929 100644 --- a/R/predict.R +++ b/R/predict.R @@ -122,34 +122,33 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, if (object$output_type != 1) stop("MCMC output must contain posterior samples of the states.") - - if(!identical(attr(object, "model_type"), class(model)[1])) { + if (!identical(attr(object, "model_type"), class(model)[1])) { stop("Model class does not correspond to the MCMC output. ") } - if(!identical(ncol(object$theta), length(model$theta))) { + if (!identical(ncol(object$theta), length(model$theta))) { stop(paste("Number of unknown parameters 'theta' does not correspond to", "the MCMC output. ", sep = " ")) } - if(nsim < 1) stop("Number of samples 'nsim' should be at least one.") + if (nsim < 1) stop("Number of samples 'nsim' should be at least one.") - if(future) { + if (future) { if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { - object$theta[,1:(ncol(object$theta) - length(model$beta))] <- - log(object$theta[,1:(ncol(object$theta) - length(model$beta))]) + object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- + log(object$theta[, 1:(ncol(object$theta) - length(model$beta))]) } w <- object$counts * - (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) + (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) theta <- t(object$theta[idx, ]) - alpha <- matrix(object$alpha[nrow(object$alpha),,idx], + alpha <- matrix(object$alpha[nrow(object$alpha), , idx], nrow = ncol(object$alpha)) switch(attr(object, "model_type"), - ssm_mlg = , - ssm_ulg = , - bsm_lg = , + ssm_mlg =, + ssm_ulg =, + bsm_lg =, ar1_lg = { if (!identical(length(model$a1), ncol(object$alpha))) { stop(paste("Model does not correspond to the MCMC output:", @@ -162,10 +161,10 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, c("ssm_mng", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) }, - ssm_mng = , - ssm_ung = , - bsm_ng = , - svm = , + ssm_mng =, + ssm_ung =, + bsm_ng =, + svm =, ar1_ng = { if (!identical(length(model$a1), ncol(object$alpha))) { stop(paste("Model does not correspond to the MCMC output:", @@ -180,7 +179,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, pmatch(attr(object, "model_type"), c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) - if(anyNA(pred)) + if (anyNA(pred)) warning("NA or NaN values in predictions, possible under/overflow?") }, ssm_nlg = { @@ -198,15 +197,15 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } , stop("Not yet implemented for ssm_sde. ")) - if(type == "state") { - if(attr(object, "model_type") == "ssm_nlg") { + if (type == "state") { + if (attr(object, "model_type") == "ssm_nlg") { variables <- model$state_names } else { variables <- names(model$a1) } } else { variables <- colnames(model$y) - if(is.null(variables)) variables <- "Series 1" + if (is.null(variables)) variables <- "Series 1" } d <- data.frame(value = as.numeric(pred), variable = variables, @@ -215,12 +214,12 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } else { - if(!identical(nrow(object$alpha) - 1L, length(model$y))) { + if (!identical(nrow(object$alpha) - 1L, length(model$y))) { stop("Number of observations of the model and MCMC output do not match.") } w <- object$counts * - (if(object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) + (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) n <- nrow(object$alpha) - 1L @@ -228,8 +227,8 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, states <- object$alpha[1:n, , idx, drop = FALSE] - if(type == "state") { - if(attr(object, "model_type") == "ssm_nlg") { + if (type == "state") { + if (attr(object, "model_type") == "ssm_nlg") { variables <- model$state_names } else { variables <- names(model$a1) @@ -241,20 +240,20 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } else { variables <- colnames(model$y) - if(is.null(variables)) variables <- "Series 1" + if (is.null(variables)) variables <- "Series 1" if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { - object$theta[,1:(ncol(object$theta) - length(model$beta))] <- - log(object$theta[,1:(ncol(object$theta) - length(model$beta))]) + object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- + log(object$theta[, 1:(ncol(object$theta) - length(model$beta))]) } theta <- t(object$theta[idx, ]) states <- aperm(states, c(2, 1, 3)) switch(attr(object, "model_type"), - ssm_mlg = , - ssm_ulg = , - bsm_lg = , + ssm_mlg =, + ssm_ulg =, + bsm_lg =, ar1_lg = { if (!identical(length(model$a1), m)) { stop(paste("Model does not correspond to the MCMC output:", @@ -267,10 +266,10 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, c("ssm_mng", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) }, - ssm_mng = , - ssm_ung = , - bsm_ng = , - svm = , + ssm_mng =, + ssm_ung =, + bsm_ng =, + svm =, ar1_ng = { if (!identical(length(model$a1), m)) { stop(paste("Model does not correspond to the MCMC output:", @@ -285,7 +284,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, pmatch(attr(object, "model_type"), c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) - if(anyNA(pred)) + if (anyNA(pred)) warning("NA or NaN values in predictions, possible under/overflow?") }, ssm_nlg = { diff --git a/R/print_mcmc.R b/R/print_mcmc.R index c9d2f319..6cdf7716 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -5,9 +5,9 @@ iact <- function(x) { # Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms C <- max(5.0, log10(n)) tau <- 1 - for(k in 1:(n-1)) { + for (k in 1:(n - 1)) { tau <- tau + 2.0 * (x_[1:(n-k)] %*% x_[(1+k):n]) / (n - k) - if(k > C * tau) break + if (k > C * tau) break } max(0.0, tau) } @@ -41,13 +41,13 @@ print.mcmc_output <- function(x, ...) { if (x$mcmc_type %in% paste0("is", 1:3)) { theta <- mcmc(x$theta) - if(x$output_type == 1) - alpha <- mcmc(matrix(x$alpha[nrow(x$alpha),,], ncol = ncol(x$alpha), + if (x$output_type == 1) + alpha <- mcmc(matrix(x$alpha[nrow(x$alpha), , ], ncol = ncol(x$alpha), byrow = TRUE, dimnames = list(NULL, colnames(x$alpha)))) w <- x$counts * x$weights } else { theta <- expand_sample(x, "theta") - if(x$output_type == 1) + if (x$output_type == 1) alpha <- expand_sample(x, "state", times = nrow(x$alpha), by_states = FALSE)[[1]] } @@ -56,10 +56,10 @@ print.mcmc_output <- function(x, ...) { "\n", sep = "") cat("\n", "Iterations = ", x$burnin + 1, ":", x$iter, "\n", sep = "") - cat("Thinning interval = ",x$thin, "\n", sep = "") + cat("Thinning interval = ", x$thin, "\n", sep = "") cat("Length of the final jump chain = ", length(x$counts), "\n", sep = "") cat("\nAcceptance rate after the burn-in period: ", - paste(round(x$acceptance_rate,3),"\n", sep = "")) + paste(round(x$acceptance_rate, 3), "\n", sep = "")) cat("\nSummary for theta:\n\n") if (x$mcmc_type %in% paste0("is", 1:3)) { @@ -73,7 +73,7 @@ print.mcmc_output <- function(x, ...) { } else { mean_theta <- colMeans(theta) sd_theta <- apply(theta, 2, sd) - se_theta <- sqrt(spectrum0.ar(theta)$spec/nrow(theta)) + se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) stats <- matrix(c(mean_theta, sd_theta, se_theta), ncol = 3, dimnames = list(colnames(x$theta), c("Mean", "SD", "SE"))) } @@ -84,7 +84,7 @@ print.mcmc_output <- function(x, ...) { esss <- matrix((sd_theta / se_theta)^2, ncol = 1, dimnames = list(colnames(x$theta), c("ESS"))) print(esss) - if(x$output_type != 3) { + if (x$output_type != 3) { n <- nrow(x$alpha) cat(paste0("\nSummary for alpha_", n), ":\n\n", sep = "") @@ -95,7 +95,7 @@ print.mcmc_output <- function(x, ...) { sd_alpha <- sqrt(diag(weighted_var(alpha, w, method = "moment"))) se_alpha_is <- weighted_se(alpha, w) se_alpha <- sqrt(apply(alpha, 2, function(x) asymptotic_var(x, w))) - stats <- matrix(c(mean_alpha, sd_alpha, se_alpha,se_alpha_is), + stats <- matrix(c(mean_alpha, sd_alpha, se_alpha, se_alpha_is), ncol = 4, dimnames = list(colnames(x$alpha), c("Mean", "SD", "SE", "SE-IS"))) } else { @@ -116,9 +116,9 @@ print.mcmc_output <- function(x, ...) { } else { if (ncol(x$alphahat) == 1) { - print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(x$Vt[,,n]))) + print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(x$Vt[, , n]))) } else { - print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(diag(x$Vt[,,n])))) + print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(diag(x$Vt[, , n])))) } } } else cat("\nNo posterior samples for states available.\n") @@ -154,20 +154,20 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) { if (only_theta) { - parameters <- "theta" + variable <- "theta" warning(paste("Argument 'only_theta' is deprecated. Use argument", "'variable' instead. ", sep = " ")) } variable <- match.arg(variable, c("theta", "states", "both")) - if(variable %in% c("theta", "both")) { + if (variable %in% c("theta", "both")) { if (object$mcmc_type %in% paste0("is", 1:3)) { theta <- mcmc(object$theta) w <- object$counts * object$weights mean_theta <- weighted_mean(theta, w) sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) - if(return_se) { + if (return_se) { mean_theta <- weighted_mean(theta, w) sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) se_theta_is <- weighted_se(theta, w) @@ -187,10 +187,10 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", mean_theta <- colMeans(theta) sd_theta <- apply(theta, 2, sd) - if(return_se) { + if (return_se) { mean_theta <- colMeans(theta) sd_theta <- apply(theta, 2, sd) - se_theta <- sqrt(spectrum0.ar(theta)$spec/nrow(theta)) + se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) ess_theta <- (sd_theta / se_theta)^2 summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), ncol = 4, @@ -215,12 +215,12 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", frequency = attr(object, "ts")$frequency, names = colnames(object$alpha)) sd_alpha <- weighted_var(object$alpha, w, method = "moment") - sd_alpha <- if(m > 1) { + sd_alpha <- if (m > 1) { sqrt(t(apply(sd_alpha, 3, diag))) } else matrix(sqrt(sd_alpha), ncol = 1) - if(return_se) { + if (return_se) { se_alpha_is <- apply(object$alpha, 2, function(x) weighted_se(t(x), w)) @@ -246,8 +246,7 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sd_alpha <- vapply(alpha, function(x) apply(x, 2, sd), numeric(nrow(object$alpha))) - if(return_se) { - + if (return_se) { se_alpha <- vapply(alpha, function(x) apply(x, 2, function(z) sqrt(spectrum0.ar(z)$spec / length(z))), @@ -294,14 +293,14 @@ expand_sample <- function(x, variable = "theta", times, states, if (x$mcmc_type %in% paste0("is", 1:3)) warning(paste("Input is based on a IS-weighted MCMC, the results", "correspond to the approximate posteriors.", sep = " ")) - if(variable == "theta") { + if (variable == "theta") { out <- apply(x$theta, 2, rep, times = x$counts) } else { if (x$output_type == 1) { - if(missing(times)) times <- seq_len(nrow(x$alpha)) - if(missing(states)) states <- seq_len(ncol(x$alpha)) + if (missing(times)) times <- seq_len(nrow(x$alpha)) + if (missing(states)) states <- seq_len(ncol(x$alpha)) - if(by_states) { + if (by_states) { out <- lapply(states, function(i) { z <- apply(x$alpha[times, i, , drop = FALSE], 1, rep, x$counts) colnames(z) <- times diff --git a/R/priors.R b/R/priors.R index 1e8126b7..892ee21b 100644 --- a/R/priors.R +++ b/R/priors.R @@ -30,21 +30,21 @@ #' uniform(0.2, -1, 1) #' # two normal priors at once i.e. for coefficients beta: #' normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) -uniform <- function(init, min, max){ - if(any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { +uniform <- function(init, min, max) { + if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") } - if (any(min > max)){ + if (any(min > max)) { stop(paste("Lower bound of uniform distribution must be smaller than", "upper bound.", sep = " ")) } - if(any(init < min) || any(init > max)) { + if (any(init < min) || any(init > max)) { stop(paste("Initial value for parameter with uniform prior is not", "in the support of the prior.", sep = " ")) } n <- max(length(init), length(min), length(max)) - if(n > 1) { + if (n > 1) { structure(lapply(1:n, function(i) structure(list(prior_distribution = "uniform", init = safe_pick(init, i), min = safe_pick(min, i), max = safe_pick(max, i)), @@ -58,9 +58,9 @@ uniform <- function(init, min, max){ #' @rdname priors #' @export -halfnormal <- function(init, sd){ +halfnormal <- function(init, sd) { - if(any(!is.numeric(init), !is.numeric(sd))) { + if (any(!is.numeric(init), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { @@ -87,9 +87,9 @@ halfnormal <- function(init, sd){ #' @rdname priors #' @export -normal <- function(init, mean, sd){ +normal <- function(init, mean, sd) { - if(any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { + if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { @@ -112,9 +112,9 @@ normal <- function(init, mean, sd){ } #' @rdname priors #' @export -tnormal <- function(init, mean, sd, min = -Inf, max = Inf){ +tnormal <- function(init, mean, sd, min = -Inf, max = Inf) { - if(any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { + if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } if (any(sd < 0)) { @@ -142,19 +142,19 @@ combine_priors <- function(x) { prior_distributions <- vapply(x, "[[", "prior_distribution", character(1)) parameters <- matrix(NA, 4, length(prior_distributions)) - for(i in seq_along(prior_distributions)) { - parameters[1:(length(x[[i]])-2), i] <- as.numeric(x[[i]][-(1:2)]) + for (i in seq_along(prior_distributions)) { + parameters[1:(length(x[[i]]) - 2), i] <- as.numeric(x[[i]][-(1:2)]) } list(prior_distributions = pmatch(prior_distributions, c("uniform", "halfnormal", "normal", - "tnormal", "gamma"), duplicates.ok = TRUE)-1, + "tnormal", "gamma"), duplicates.ok = TRUE) - 1, parameters = parameters) } #' @rdname priors #' @export -gamma <- function(init, shape, rate){ +gamma <- function(init, shape, rate) { - if(any(!is.numeric(init), !is.numeric(shape), !is.numeric(rate))) { + if (any(!is.numeric(init), !is.numeric(shape), !is.numeric(rate))) { stop("Parameters for priors must be numeric.") } if (any(shape < 0)) { @@ -177,10 +177,10 @@ gamma <- function(init, shape, rate){ class = "bssm_prior") } } -is_prior <- function(x){ +is_prior <- function(x) { inherits(x, "bssm_prior") } -is_prior_list <- function(x){ +is_prior_list <- function(x) { inherits(x, "bssm_prior_list") } safe_pick <- function(x, i) { diff --git a/R/run_mcmc.R b/R/run_mcmc.R index aaecc7ea..3f86f5d0 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -81,12 +81,12 @@ run_mcmc <- function(model, iter, ...) { #' geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), #' col = 2) run_mcmc.gaussian <- function(model, iter, output_type = "full", - burnin = floor(iter / 2), thin = 1, gamma = 2/3, + burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { - if(length(model$theta) == 0) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() @@ -316,27 +316,29 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' ggplot(sumr, aes(time, mean)) + #' geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + #' geom_line() + -#' geom_line(data = data.frame(mean = y[, 1], time = 1:20), colour = "tomato") + -#' geom_line(data = data.frame(mean = y[, 2], time = 1:20), colour = "tomato") + +#' geom_line(data = data.frame(mean = y[, 1], time = 1:20), +#' colour = "tomato") + +#' geom_line(data = data.frame(mean = y[, 2], time = 1:20), +#' colour = "tomato") + #' theme_bw() #' run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", - mcmc_type = "is2", sampling_method = "psi", burnin = floor(iter/2), - thin = 1, gamma = 2/3, target_acceptance = 0.234, S, + mcmc_type = "is2", sampling_method = "psi", burnin = floor(iter / 2), + thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, local_approx = TRUE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", - "instead.", sep = " ")) + "instead.", sep = " ")) particles <- nsim } } - if(length(model$theta) == 0) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) @@ -346,16 +348,17 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", if (mcmc_type == "approx") particles <- 0 if (particles < 2 && mcmc_type != "approx") stop(paste("Number of state samples less than 2, use 'mcmc_type' 'approx'", - "instead.", sep = " ")) + "instead.", sep = " ")) - sampling_method <- pmatch(match.arg(sampling_method, c("psi", "bsf", "spdk")), - c("psi", "bsf", "spdk")) + sampling_method <- + pmatch(match.arg(sampling_method, c("psi", "bsf", "spdk")), + c("psi", "bsf", "spdk")) model$max_iter <- max_iter model$conv_tol <- conv_tol model$local_approx <- local_approx - if(inherits(model, "bsm_ng")) { + if (inherits(model, "bsm_ng")) { names_ind <- c(!model$fixed & c(TRUE, model$slope, model$seasonal), model$noise) transformed <- c( @@ -413,7 +416,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) - if(inherits(model, "bsm_ng")) { + if (inherits(model, "bsm_ng")) { out$theta[, transformed] <- exp(out$theta[, transformed]) } out$iter <- iter @@ -499,12 +502,12 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "bsf", - burnin = floor(iter/2), thin = 1, - gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, + burnin = floor(iter / 2), thin = 1, + gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", @@ -513,7 +516,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } } - if(length(model$theta) == 0) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) @@ -521,7 +524,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), "ekf", "approx")) - if(mcmc_type %in% c("ekf", "approx")) particles <- 0 + if (mcmc_type %in% c("ekf", "approx")) particles <- 0 sampling_method <- pmatch(match.arg(sampling_method, c("psi", "bsf", "ekf")), c("psi", "bsf", NA, "ekf")) @@ -530,9 +533,9 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } if (particles < 2 && !(mcmc_type %in% c("ekf", "approx"))) - stop(paste("Number of state samples less than 2, use 'mcmc_type'", - "'approx' or 'ekf' instead.", sep = " ")) - + stop(paste("Number of state samples less than 2, use 'mcmc_type'", + "'approx' or 'ekf' instead.", sep = " ")) + out <- switch(mcmc_type, "da" = { @@ -543,7 +546,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, max_iter, conv_tol, - sampling_method,iekf_iter, output_type) + sampling_method, iekf_iter, output_type) }, "pm" = { nonlinear_pm_mcmc(t(model$y), model$Z, model$H, model$T, @@ -553,7 +556,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, max_iter, conv_tol, - sampling_method,iekf_iter, output_type) + sampling_method, iekf_iter, output_type) }, "is1" =, "is2" =, @@ -683,26 +686,28 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { - if(any(c(model$drift, model$diffusion, model$ddiffusion,model$prior_pdf, + if (any(c(model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf) %in% c("", ""))) { stop(paste("NULL pointer detected, please recompile the pointer file", - "and reconstruct the model.", sep = " ")) + "and reconstruct the model.", sep = " ")) } - if(missing(particles)) { + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", "instead.", sep = " ")) particles <- nsim } + } else { + if (particles <= 0) stop("particles should be positive integer.") } - if(length(model$theta) == 0) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) - if(particles <= 0) stop("particles should be positive integer.") + output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3))) @@ -711,31 +716,36 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - if (mcmc_type == "da"){ + if (mcmc_type != "pm") { if (L_f <= L_c) stop("L_f should be larger than L_c.") - if(L_c < 1) stop("L_c should be at least 1") - out <- sde_da_mcmc(model$y, model$x0, model$positive, - model$drift, model$diffusion, model$ddiffusion, - model$prior_pdf, model$obs_pdf, model$theta, - particles, L_c, L_f, seed, - iter, burnin, thin, gamma, target_acceptance, S, - end_adaptive_phase, output_type) + if (L_c < 1) stop("L_c should be at least 1") } else { - if(mcmc_type == "pm") { - if (missing(L_c)) L_c <- 0 - if (missing(L_f)) L_f <- 0 - L <- max(L_c, L_f) - if(L <= 0) stop("L should be positive.") + if (missing(L_c)) L_c <- 0 + if (missing(L_f)) L_f <- 0 + L <- max(L_c, L_f) + if (L <= 0) stop("L should be positive.") + } + out <- switch(mcmc_type, + "da" = { + out <- sde_da_mcmc(model$y, model$x0, model$positive, + model$drift, model$diffusion, model$ddiffusion, + model$prior_pdf, model$obs_pdf, model$theta, + particles, L_c, L_f, seed, + iter, burnin, thin, gamma, target_acceptance, S, + end_adaptive_phase, output_type) + }, + "pm" = { + out <- sde_pm_mcmc(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, particles, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, output_type) - } else { - if (L_f <= L_c) stop("L_f should be larger than L_c.") - if(L_c < 1) stop("L_c should be at least 1") - + }, + "is1" =, + "is2" =, + "is3" = { out <- sde_is_mcmc(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, @@ -743,8 +753,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, pmatch(mcmc_type, paste0("is", 1:3)), threads, output_type) - } - } + }) + colnames(out$alpha) <- model$state_names colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- diff --git a/R/zzz.R b/R/zzz.R index 12d0d826..6762d661 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -1,3 +1,3 @@ -.onUnload <- function (libpath) { +.onUnload <- function(libpath) { library.dynam.unload("bssm", libpath) } diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index d6729dab..161920aa 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -1,7 +1,7 @@ context("Test Gaussian approximation") -test_that("Gaussian approximation results of bssm and KFAS coincide",{ +test_that("Gaussian approximation results of bssm and KFAS coincide", { library(KFAS) set.seed(123) model_KFAS <- SSModel(rpois(10, exp(2)) ~ SSMtrend(2, Q = list(1, 1), @@ -10,18 +10,18 @@ test_that("Gaussian approximation results of bssm and KFAS coincide",{ sd_slope = 1, distribution = "poisson"), NA) approx_KFAS <- approxSSM(model_KFAS) expect_error(approx_bssm <- gaussian_approx(model_bssm), NA) - all.equal(c(approx_bssm$H^2),c(approx_KFAS$H)) + all.equal(c(approx_bssm$H^2), c(approx_KFAS$H)) expect_error(alphahat <- fast_smoother(approx_bssm), NA) expect_equivalent(KFS(approx_KFAS)$alphahat, alphahat) expect_equivalent(logLik(approx_KFAS), logLik(approx_bssm)) }) -test_that("Gaussian approximation works for SV model",{ +test_that("Gaussian approximation works for SV model", { set.seed(123) - expect_error(model_bssm <- svm(rnorm(5), sigma = uniform(1,0,10), + expect_error(model_bssm <- svm(rnorm(5), sigma = uniform(1, 0, 10), rho = uniform(0.950, 0, 1), - sd_ar = uniform(0.1,0,1)), NA) + sd_ar = uniform(0.1, 0, 1)), NA) expect_error(approx_bssm <- gaussian_approx(model_bssm, max_iter = 2, conv_tol = 1e-8), NA) @@ -35,25 +35,25 @@ test_that("Gaussian approximation works for SV model",{ fast_smoother(approx_bssm)[1:5]) }) -test_that("results for poisson GLM are equal to glm function",{ - d <- data.frame(treatment = gl(3,3), outcome = gl(3,1,9), - counts = c(18,17,15,20,10,20,25,13,12)) +test_that("results for poisson GLM are equal to glm function", { + d <- data.frame(treatment = gl(3, 3), outcome = gl(3, 1, 9), + counts = c(18, 17, 15, 20, 10, 20, 25, 13, 12)) glm_poisson <- glm(counts ~ outcome + treatment, data = d, family = poisson()) xreg <- model.matrix(~ outcome + treatment, data = d) expect_error(model_poisson1 <- ssm_ung(d$counts, Z = t(xreg), T = diag(5), R = diag(0, 5), - P1 = diag(1e7, 5), distribution = 'poisson', + P1 = diag(1e7, 5), distribution = "poisson", state_names = colnames(xreg)), NA) expect_error(sm <- smoother(model_poisson1), NA) - expect_equal(sm$alphahat[1,], coef(glm_poisson)) - expect_equal(sm$V[,,1], vcov(glm_poisson)) + expect_equal(sm$alphahat[1, ], coef(glm_poisson)) + expect_equal(sm$V[, , 1], vcov(glm_poisson)) xreg <- model.matrix(~ outcome + treatment, data = d)[, -1] expect_error(model_poisson2 <- bsm_ng(d$counts, sd_level = 0, xreg = xreg, P1 = matrix(1e7), - beta = normal(coef(glm_poisson)[-1], 0, 10), distribution = 'poisson'), NA) - expect_equivalent(smoother(model_poisson2)$alphahat[1,], + beta = normal(coef(glm_poisson)[-1], 0, 10), distribution = "poisson"), NA) + expect_equivalent(smoother(model_poisson2)$alphahat[1, ], coef(glm_poisson)[1]) model_poisson3 <- model_poisson1 @@ -63,7 +63,7 @@ test_that("results for poisson GLM are equal to glm function",{ model_poisson4 <- ssm_ung(d$counts, Z = 1, T = 1, R = 0, D = t(model_poisson2$xreg %*% model_poisson2$beta), - P1 = 1e7, distribution = 'poisson') + P1 = 1e7, distribution = "poisson") expect_equivalent(gaussian_approx(model_poisson1)$y, gaussian_approx(model_poisson2)$y) @@ -85,17 +85,17 @@ test_that("results for poisson GLM are equal to glm function",{ }) -test_that("results for binomial GLM are equal to glm function",{ +test_that("results for binomial GLM are equal to glm function", { ldose <- rep(0:5, 2) numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) sex <- factor(rep(c("M", "F"), c(6, 6))) - SF <- cbind(numdead, numalive = 20-numdead) + SF <- cbind(numdead, numalive = 20 - numdead) glm_binomial <- glm(SF ~ sex * ldose, family = binomial) xreg <- model.matrix(~ sex * ldose) expect_error(model_binomial <- ssm_ung(numdead, Z = t(xreg), T = diag(4), R = diag(0, 4), P1 = diag(1e5, 4), - distribution = 'binomial', u = 20, state_names = colnames(xreg)), NA) + distribution = "binomial", u = 20, state_names = colnames(xreg)), NA) expect_error(sm <- smoother(model_binomial), NA) # non-exact diffuse initialization is numerically difficult... expect_equal(sm$alphahat[1, ], coef(glm_binomial), tolerance = 1e-5) @@ -104,14 +104,14 @@ test_that("results for binomial GLM are equal to glm function",{ }) -test_that("state estimates for NB GLM are equal to glm function",{ +test_that("state estimates for NB GLM are equal to glm function", { library(MASS) set.seed(123) offs <- quine$Days + sample(10:20, size = nrow(quine), replace = TRUE) glm_nb <- glm.nb(Days ~ 1 + offset(log(offs)), data = quine) expect_error(model_nb <- bsm_ng(quine$Days, u = offs, sd_level = 0, P1 = matrix(1e7), phi = glm_nb$theta, - distribution = 'negative binomial'), NA) + distribution = "negative binomial"), NA) approx_model <- gaussian_approx(model_nb, conv_tol = 1e-12) expect_error(sm <- smoother(approx_model), NA) @@ -124,11 +124,11 @@ test_that("Two iid model gives same results as two univariate models", { expect_error(model <- ssm_mng(y, Z = diag(2), phi = 2, T = diag(2), R = array(diag(0.5, 2), c(2, 2, 1)), a1 = matrix(0, 2, 1), P1 = diag(2), distribution = "negative binomial", - init_theta = c(0,0)), NA) - expect_error(model1 <- ssm_ung(y[,1], Z = 1, phi = 2, + init_theta = c(0, 0)), NA) + expect_error(model1 <- ssm_ung(y[, 1], Z = 1, phi = 2, T = 1, R = 0.5, P1 = 1, distribution = "negative binomial", init_theta = 0), NA) - expect_error(model2 <- ssm_ung(y[,2], Z = 1, phi = 2, + expect_error(model2 <- ssm_ung(y[, 2], Z = 1, phi = 2, T = 1, R = 0.5, P1 = 1, distribution = "negative binomial", init_theta = 0), NA) expect_equivalent(gaussian_approx(model, conv_tol = 1e-12)$y, @@ -262,12 +262,12 @@ test_that("Gaussian approximation works for nonlinear models", { pntrs <- create_xptrs() set.seed(1) y <- cumsum(rnorm(10)) + rnorm(10) - model_nlg <- ssm_nlg(y = y, a1=pntrs$a1, P1 = pntrs$P1, + model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, theta = c("log_H" = 0), log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = c("state")) - model_gaussian <- bsm_lg(y, sd_y=1, sd_level = 1, P1 = 1) + model_gaussian <- bsm_lg(y, sd_y = 1, sd_level = 1, P1 = 1) expect_equal( logLik(model_nlg, method = "ekf", particles = 0), logLik(gaussian_approx(model_nlg))) diff --git a/tests/testthat/test_as_bssm.R b/tests/testthat/test_as_bssm.R index afbec26f..cc17ffff 100644 --- a/tests/testthat/test_as_bssm.R +++ b/tests/testthat/test_as_bssm.R @@ -1,7 +1,7 @@ context("Test as_bssm") -test_that("Test conversion from SSModel to ssm_ulg",{ +test_that("Test conversion from SSModel to ssm_ulg", { library(KFAS) model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(2, 2), P1 = diag(2e3, 2)), H = 2) @@ -9,7 +9,7 @@ test_that("Test conversion from SSModel to ssm_ulg",{ H = sqrt(2), T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(sqrt(2), 2), c(2, 2, 1)), - a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), init_theta = c(0,0)), NA) + a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), init_theta = c(0, 0)), NA) expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0, 0)), NA) expect_equivalent(model_bssm, conv_model_bssm) @@ -17,7 +17,7 @@ test_that("Test conversion from SSModel to ssm_ulg",{ }) -test_that("Test conversion from SSModel to ssm_ung",{ +test_that("Test conversion from SSModel to ssm_ung", { library(KFAS) model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(2, 2), P1 = diag(2e3, 2)), u = 2, distribution = "negative binomial") @@ -26,12 +26,12 @@ test_that("Test conversion from SSModel to ssm_ung",{ R = array(diag(sqrt(2), 2), c(2, 2, 1)), a1 = matrix(0, 2, 1), P1 = diag(2e3, 2), distribution = "negative binomial", - state_names = c("level", "slope"), init_theta = c(0,0)), NA) - expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0,0)), NA) + state_names = c("level", "slope"), init_theta = c(0, 0)), NA) + expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0, 0)), NA) expect_equivalent(model_bssm, conv_model_bssm) }) -test_that("Test conversion from SSModel to ssm_mng",{ +test_that("Test conversion from SSModel to ssm_mng", { library(KFAS) set.seed(1) y <- matrix(rbinom(20, size = 10, prob = plogis(rnorm(20, sd = 0.2))), 10, 2) @@ -40,10 +40,10 @@ test_that("Test conversion from SSModel to ssm_mng",{ expect_error(model_bssm <- ssm_mng(y, Z = diag(2), u = 10, T = diag(2), R = array(diag(0.2, 2), c(2, 2, 1)), a1 = matrix(0, 2, 1), P1 = diag(2), distribution = "binomial", - init_theta = c(0,0)), NA) + init_theta = c(0, 0)), NA) # to make the attributes match model_bssm$u <- as.ts(model_bssm$u) model_bssm$initial_mode <- as.ts(model_bssm$initial_mode) - expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0,0)), NA) + expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0, 0)), NA) expect_equivalent(model_bssm, conv_model_bssm) }) diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index a19c993b..52a572ee 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -1,15 +1,15 @@ context("Test basics") -test_that("results for Gaussian models are comparable to KFAS",{ +test_that("results for Gaussian models are comparable to KFAS", { library("KFAS") model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(0.01^2, 0)), H = 2) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - model_bssm <- bsm_lg(1:10, P1 = diag(1e2,2), sd_slope = 0, + model_bssm <- bsm_lg(1:10, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, sd_y = sqrt(2)) - expect_equal(logLik(model_KFAS,convtol = 1e-12), logLik(model_bssm,0)) + expect_equal(logLik(model_KFAS, convtol = 1e-12), logLik(model_bssm, 0)) out_KFAS <- KFS(model_KFAS, filtering = "state", convtol = 1e-12) expect_error(out_bssm <- kfilter(model_bssm), NA) expect_equivalent(out_KFAS$a, out_bssm$at) @@ -19,7 +19,7 @@ test_that("results for Gaussian models are comparable to KFAS",{ expect_equivalent(out_KFAS$V, out_bssm$Vt) }) -test_that("results for multivariate Gaussian model are comparable to KFAS",{ +test_that("results for multivariate Gaussian model are comparable to KFAS", { library("KFAS") # From the help page of ?KFAS data("Seatbelts", package = "datasets") @@ -49,13 +49,13 @@ test_that("results for multivariate Gaussian model are comparable to KFAS",{ NULL)) bssm_model <- as_bssm(kfas_model) - expect_equivalent(logLik(kfas_model),logLik(bssm_model)) + expect_equivalent(logLik(kfas_model), logLik(bssm_model)) expect_equivalent(KFS(kfas_model)$alphahat, smoother(bssm_model)$alphahat) }) -test_that("different smoothers give identical results",{ - model_bssm <- bsm_lg(log10(AirPassengers), P1 = diag(1e2,13), sd_slope = 0, +test_that("different smoothers give identical results", { + model_bssm <- bsm_lg(log10(AirPassengers), P1 = diag(1e2, 13), sd_slope = 0, sd_y = uniform(0.005, 0, 10), sd_level = uniform(0.01, 0, 10), sd_seasonal = uniform(0.005, 0, 1)) @@ -65,7 +65,7 @@ test_that("different smoothers give identical results",{ }) -test_that("results for Poisson model are comparable to KFAS",{ +test_that("results for Poisson model are comparable to KFAS", { library("KFAS") set.seed(1) model_KFAS <- SSModel(rpois(10, exp(0.2) * (2:11)) ~ @@ -74,7 +74,7 @@ test_that("results for Poisson model are comparable to KFAS",{ model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2,2), sd_slope = 0, + model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, u = 2:11, distribution = "poisson") expect_equal(logLik(model_KFAS), logLik(model_bssm, 0)) @@ -97,7 +97,7 @@ test_that("results for binomial model are comparable to KFAS", { model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2,2), sd_slope = 0, + model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, u = 2:11, distribution = "binomial") expect_equal(logLik(model_KFAS), logLik(model_bssm, 0)) @@ -156,9 +156,9 @@ test_that("results for bivariate non-Gaussian model are comparable to KFAS", { expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = 1e-8) is_KFAS <- importanceSSM(model_KFAS, nsim = 1e4) expect_error(is_bssm <- importance_sample(model_bssm, nsim = 1e4), NA) - expect_equivalent(apply(is_bssm$alpha, 1:2, mean)[1:n,], + expect_equivalent(apply(is_bssm$alpha, 1:2, mean)[1:n, ], apply(is_KFAS$samples, 1:2, mean), tolerance = 0.1) - expect_equivalent(apply(is_bssm$alpha, 1:2, sd)[1:n,], + expect_equivalent(apply(is_bssm$alpha, 1:2, sd)[1:n, ], apply(is_KFAS$samples, 1:2, sd), tolerance = 0.1) }) @@ -184,9 +184,9 @@ test_that("multivariate normal pdf works", { b <- matrix(0, 3, 3) constant <- bssm:::precompute_dmvnorm(a, b, 0:2) expect_equivalent(logp1, - bssm:::fast_dmvnorm(1:3, -0.1*(3:1), b, 0:2, constant), tolerance = 1e-8) + bssm:::fast_dmvnorm(1:3, -0.1 * (3:1), b, 0:2, constant), tolerance = 1e-8) - a[2,] <- a[, 2] <- 0 + a[2, ] <- a[, 2] <- 0 logp3 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) expect_equivalent(logp3, -12.5587625856078, tolerance = 1e-6) }) diff --git a/tests/testthat/test_bootstrap_filter.R b/tests/testthat/test_bootstrap_filter.R index c8ec6c45..a1a44bc3 100644 --- a/tests/testthat/test_bootstrap_filter.R +++ b/tests/testthat/test_bootstrap_filter.R @@ -2,7 +2,7 @@ context("Test that bootstrap_filter works") -test_that("Test that bsm_lg gives identical results with ssm_ulg",{ +test_that("Test that bsm_lg gives identical results with ssm_ulg", { expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(2, 2), c(2, 2, 1)), @@ -18,7 +18,7 @@ test_that("Test that bsm_lg gives identical results with ssm_ulg",{ tol <- 1e-8 -test_that("Test that gaussian bsf still works",{ +test_that("Test that gaussian bsf still works", { expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), @@ -34,7 +34,7 @@ test_that("Test that gaussian bsf still works",{ expect_true(is.finite(sum(bsf_ssm_ulg$Ptt))) }) -test_that("Test that poisson bsm_ng still works",{ +test_that("Test that poisson bsm_ng still works", { expect_error(model <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, P1 = diag(2, 2), distribution = "poisson"), NA) @@ -47,9 +47,9 @@ test_that("Test that poisson bsm_ng still works",{ expect_true(is.finite(sum(bsf_poisson$Ptt))) }) -test_that("Test that binomial bsm_ng still works",{ +test_that("Test that binomial bsm_ng still works", { - expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, + expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, sd_slope = 2, P1 = diag(2, 2), distribution = "binomial"), NA) expect_error(bsf_binomial <- bootstrap_filter(model, 10, seed = 1), NA) @@ -64,9 +64,9 @@ test_that("Test that binomial bsm_ng still works",{ -test_that("Test that negative binomial bsm_ng still works",{ +test_that("Test that negative binomial bsm_ng still works", { - expect_error(model <- bsm_ng(c(1,0,1,1,1,0,0,0), sd_level = 2, + expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, sd_slope = 2, P1 = diag(2, 2), distribution = "negative binomial", phi = 0.1, u = 2), NA) expect_error(bsf_nbinomial <- bootstrap_filter(model, 10, seed = 1), NA) @@ -79,9 +79,9 @@ test_that("Test that negative binomial bsm_ng still works",{ }) -test_that("Test that still svm works",{ +test_that("Test that still svm works", { data("exchange") - model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), + model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), sd_ar = halfnormal(0.2, 5), sigma = halfnormal(1, 2)) expect_error(bsf_svm <- bootstrap_filter(model, 10, seed = 1), NA) diff --git a/tests/testthat/test_is.R b/tests/testthat/test_is.R index daf74318..dab171d7 100644 --- a/tests/testthat/test_is.R +++ b/tests/testthat/test_is.R @@ -1,21 +1,21 @@ context("Test importance_sample") -test_that("Test that poisson bsm_ng give identical results with ssm_ung",{ +test_that("Test that poisson bsm_ng give identical results with ssm_ung", { expect_error(model_ssm_ung <- ssm_ung(y = 1:10, Z = matrix(c(1, 0), 2, 1), T = array(c(1, 0, 1, 1), c(2, 2, 1)), R = array(diag(2, 2), c(2, 2, 1)), a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope"), distribution = "poisson"), NA) expect_error(sim_ssm_ung <- importance_sample(model_ssm_ung, 4, seed = 2), NA) - expect_error(model_bsm_ng <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, P1 = diag(2, 2), - distribution = "poisson"), NA) + expect_error(model_bsm_ng <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, + P1 = diag(2, 2), distribution = "poisson"), NA) expect_error(sim_bsm_ng <- importance_sample(model_bsm_ng, 4, seed = 2), NA) expect_equal(sim_bsm_ng, sim_ssm_ung) }) -test_that("Test that svm still works",{ +test_that("Test that svm still works", { data("exchange") - model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), + model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), sd_ar = halfnormal(0.2, 5), sigma = halfnormal(1, 2)) expect_error(sim <- importance_sample(model, 10, seed = 2), NA) @@ -25,4 +25,3 @@ test_that("Test that svm still works",{ expect_true(is.finite(sum(sim$states))) expect_true(is.finite(sum(sim$weights))) }) - diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index dbcc3cf3..76f93abc 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -1,9 +1,9 @@ context("Test MCMC") tol <- 1e-8 -test_that("MCMC results for Gaussian model are correct",{ +test_that("MCMC results for Gaussian model are correct", { set.seed(123) - model_bssm <- bsm_lg(rnorm(10,3), P1 = diag(2,2), sd_slope = 0, + model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, sd_y = uniform(1, 0, 10), sd_level = uniform(1, 0, 10)) @@ -33,7 +33,7 @@ test_that("MCMC results for Gaussian model are correct",{ }) -test_that("DA-MCMC results for Poisson model are correct",{ +test_that("DA-MCMC results for Poisson model are correct", { set.seed(123) model_bssm <- bsm_ng(rpois(10, exp(0.2) * (2:11)), P1 = diag(2, 2), sd_slope = 0, sd_level = uniform(2, 0, 10), u = 2:11, @@ -63,9 +63,9 @@ test_that("DA-MCMC results for Poisson model are correct",{ }) -test_that("MCMC results for SV model using IS-correction are correct",{ +test_that("MCMC results for SV model using IS-correction are correct", { set.seed(123) - expect_error(model_bssm <- svm(rnorm(10), rho = uniform(0.95,-0.999,0.999), + expect_error(model_bssm <- svm(rnorm(10), rho = uniform(0.95, -0.999, 0.999), sd_ar = halfnormal(1, 5), sigma = halfnormal(1, 2)), NA) expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index fe8677cb..30dad830 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -1,6 +1,6 @@ context("Test models") -test_that("bad argument values for bsm throws an error",{ +test_that("bad argument values for bsm throws an error", { expect_error(bsm_lg("character vector")) expect_error(bsm_lg(matrix(0, 2, 2))) expect_error(bsm_lg(1)) @@ -15,17 +15,17 @@ test_that("bad argument values for bsm throws an error",{ expect_error(bsm_lg(1:10, 1, 1, 1, 1)) }) -test_that("proper arguments for bsm don't throw an error",{ +test_that("proper arguments for bsm don't throw an error", { expect_error(bsm_lg(1:10, 1, 1), NA) expect_error(bsm_lg(1:10, uniform(0, 0, 1), 1), NA) expect_error(bsm_lg(1:10, 1, 1, uniform(0, 0, 1)), NA) expect_error(bsm_lg(1:10, 1, 1, 1, 1, period = 3), NA) expect_error(bsm_lg(1:10, 1, 1, 1, 1, period = 3, xreg = matrix(1:10, 10), - beta = normal(0,0,10)), NA) + beta = normal(0, 0, 10)), NA) }) -test_that("bad argument values for bsm_ng throws an error",{ +test_that("bad argument values for bsm_ng throws an error", { expect_error(bsm_ng("character vector", distribution = "poisson")) expect_error(bsm_ng(matrix(0, 2, 2), distribution = "poisson")) expect_error(bsm_ng(1, distribution = "poisson")) @@ -40,16 +40,16 @@ test_that("bad argument values for bsm_ng throws an error",{ expect_error(bsm_ng(1:10, 1, 1, 1, 1, distribution = "poisson")) }) -test_that("proper arguments for ng_bsm don't throw an error",{ +test_that("proper arguments for ng_bsm don't throw an error", { expect_error(bsm_ng(1:10, 1, 1, distribution = "poisson"), NA) expect_error(bsm_ng(1:10, uniform(0, 0, 1), 1, distribution = "poisson"), NA) expect_error(bsm_ng(1:10, 1, uniform(0, 0, 1), distribution = "poisson"), NA) expect_error(bsm_ng(1:10, 1, 1, 1, period = 3, distribution = "poisson"), NA) expect_error(bsm_ng(1:10, 1, 1, 1, period = 3, xreg = matrix(1:10, 10), - beta = normal(0,0,10), distribution = "poisson"), NA) + beta = normal(0, 0, 10), distribution = "poisson"), NA) }) -test_that("bad argument values for svm throws an error",{ +test_that("bad argument values for svm throws an error", { expect_error(svm("character vector")) expect_error(svm(matrix(0, 2, 2))) expect_error(svm(1)) @@ -62,9 +62,9 @@ test_that("bad argument values for svm throws an error",{ expect_error(svm(1:10, 1, 1, a1 = 1)) }) -test_that("proper arguments for svm don't throw an error",{ - expect_error(svm(1:10, rho = uniform(0.9,-0.9, 0.99), - mu = normal(0, 0,2), sd_ar = halfnormal(1, 2)), NA) +test_that("proper arguments for svm don't throw an error", { + expect_error(svm(1:10, rho = uniform(0.9, -0.9, 0.99), + mu = normal(0, 0, 2), sd_ar = halfnormal(1, 2)), NA) }) test_that("multivariate non-gaussian model", { From 8af1bc978d8e8f9d3fdc17e706d9b449796ad282 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 15:36:04 +0300 Subject: [PATCH 012/180] add contribution guidelines etc and coverage checks --- .Rbuildignore | 33 ++-- .github/CONTRIBUTING.md | 37 ++++ .github/issue_template.md | 10 + .github/pull_request_template.md | 20 ++ DESCRIPTION | 87 +++++---- README.md | 4 +- codecov.yml | 14 ++ codemeta.json | 301 +++++++++++++++++++++++++++++++ 8 files changed, 451 insertions(+), 55 deletions(-) create mode 100644 .github/CONTRIBUTING.md create mode 100644 .github/issue_template.md create mode 100644 .github/pull_request_template.md create mode 100644 codecov.yml create mode 100644 codemeta.json diff --git a/.Rbuildignore b/.Rbuildignore index fb6972db..273ca2ed 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,16 +1,17 @@ -^\.git$ -^.*\.Rproj$ -^\.Rproj\.user$ -^README\.md$ -^\.gitignore$ -^\.travis\.yml$ -^TODO$ -^\.Rhistory$ -^bssm.pdf$ -^growth_model.pdf$ -^vignettes/psi_pf_experiments/.*result.*\.rds$ -^vignettes/psi_pf_experiments/.*truth.*\.rds$ -^vignettes/psi_pf_experiments/.*\.sh$ -^vignettes/psi_pf_experiments/.*\.R$ -^\.github$ -slides_UseR2021 +^\.git$ +^.*\.Rproj$ +^\.Rproj\.user$ +^README\.md$ +^\.gitignore$ +^\.travis\.yml$ +^TODO$ +^\.Rhistory$ +^bssm.pdf$ +^growth_model.pdf$ +^vignettes/psi_pf_experiments/.*result.*\.rds$ +^vignettes/psi_pf_experiments/.*truth.*\.rds$ +^vignettes/psi_pf_experiments/.*\.sh$ +^vignettes/psi_pf_experiments/.*\.R$ +^\.github$ +slides_UseR2021 +^codecov\.yml$ diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 00000000..610031a5 --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,37 @@ +# CONTRIBUTING # + +### Fixing typos + +Small typos or grammatical errors in documentation may be edited directly using +the GitHub web interface, so long as the changes are made in the _source_ file. + +* YES: you edit a roxygen comment in a `.R` file below `R/`. +* NO: you edit an `.Rd` file below `man/`. + +### Prerequisites + +Before you make a substantial pull request, you should always file an issue and +make sure someone from the team agrees that it’s a problem. If you’ve found a +bug, create an associated issue and illustrate the bug with a minimal +[reprex](https://www.tidyverse.org/help/#reprex). + +### Pull request process + +* We recommend that you create a Git branch for each pull request (PR). +* Look at the Github Actions build status before and after making changes. +The `README` should contain status badge "R-CMD-check" which acts a link to +Github Actions. +* We recommend the tidyverse [style guide](http://style.tidyverse.org). +You can use the [styler](https://CRAN.R-project.org/package=styler) package to +apply these styles, but please don't restyle code that has nothing to do with +your PR. +* We use [roxygen2](https://cran.r-project.org/package=roxygen2). +* We use [testthat](https://cran.r-project.org/package=testthat). +Contributions with test cases included are easier to accept. +* For user-facing changes, add a bullet to the top of `NEWS.md` below the +current development version header describing the changes made followed by your +GitHub username, and links to relevant issue(s)/PR(s). + +### Thanks for contributing! + +This contributing guide is adapted from the tidyverse contributing guide available at https://raw.githubusercontent.com/r-lib/usethis/master/inst/templates/tidy-contributing.md \ No newline at end of file diff --git a/.github/issue_template.md b/.github/issue_template.md new file mode 100644 index 00000000..836f284c --- /dev/null +++ b/.github/issue_template.md @@ -0,0 +1,10 @@ + + + + +
Session Info + +```r + +``` +
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..e1beac81 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,20 @@ + + + + + + +## Description + + +## Related Issue + + +## Example + + + diff --git a/DESCRIPTION b/DESCRIPTION index 3c548a2e..34ff12f9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,38 +1,49 @@ -Package: bssm -Type: Package -Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space - Models -Version: 1.1.6 -Authors@R: - c(person(given = "Jouni", - family = "Helske", - role = c("aut", "cre"), - email = "jouni.helske@iki.fi", - comment = c(ORCID = "0000-0001-7130-793X")), - person(given = "Matti", - family = "Vihola", - role = "aut", - comment = c(ORCID = "0000-0002-8041-7222"))) -Description: Efficient methods for Bayesian inference of state space models - via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel - importance sampling type weighted estimators - (Vihola, Helske, and Franks, 2020, ). - Gaussian, Poisson, binomial, negative binomial, and Gamma - observation densities and basic stochastic volatility models - with linear-Gaussian state dynamics, - as well as general non-linear Gaussian models and discretised - diffusion models are supported. -License: GPL (>= 2) -Depends: R (>= 3.5.0) -Suggests: dplyr, ggplot2 (>= 2.0.0), Hmisc, KFAS (>= 1.2.1), knitr (>= - 1.11), MASS, ramcmc, rmarkdown (>= 0.8.1), sde, sitmo, testthat -Imports: coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) -LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo -SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -RoxygenNote: 7.1.1 -VignetteBuilder: knitr -BugReports: https://github.com/helske/bssm/issues -URL: https://github.com/helske/bssm -ByteCompile: true -Encoding: UTF-8 -NeedsCompilation: yes +Package: bssm +Type: Package +Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space + Models +Version: 1.1.6 +Authors@R: + c(person(given = "Jouni", + family = "Helske", + role = c("aut", "cre"), + email = "jouni.helske@iki.fi", + comment = c(ORCID = "0000-0001-7130-793X")), + person(given = "Matti", + family = "Vihola", + role = "aut", + comment = c(ORCID = "0000-0002-8041-7222"))) +Description: Efficient methods for Bayesian inference of state space models + via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel + importance sampling type weighted estimators + (Vihola, Helske, and Franks, 2020, ). + Gaussian, Poisson, binomial, negative binomial, and Gamma + observation densities and basic stochastic volatility models + with linear-Gaussian state dynamics, + as well as general non-linear Gaussian models and discretised + diffusion models are supported. +License: GPL (>= 2) +Depends: R (>= 3.5.0) +Suggests: + covr, + dplyr, + ggplot2 (>= 2.0.0), + Hmisc, + KFAS (>= 1.2.1), + knitr (>= 1.11), + MASS, + ramcmc, + rmarkdown (>= 0.8.1), + sde, + sitmo, + testthat +Imports: coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) +LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo +SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) +RoxygenNote: 7.1.1 +VignetteBuilder: knitr +BugReports: https://github.com/helske/bssm/issues +URL: https://github.com/helske/bssm +ByteCompile: true +Encoding: UTF-8 +NeedsCompilation: yes diff --git a/README.md b/README.md index 7bd6f54c..d7744f86 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ - [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) [![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) [![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 00000000..e5a1ab93 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: false + +coverage: + status: + project: + default: + target: auto + threshold: 1% + informational: true + patch: + default: + target: auto + threshold: 1% + informational: true diff --git a/codemeta.json b/codemeta.json new file mode 100644 index 00000000..282a1987 --- /dev/null +++ b/codemeta.json @@ -0,0 +1,301 @@ +{ + "@context": ["https://doi.org/10.5063/schema/codemeta-2.0", "http://schema.org"], + "@type": "SoftwareSourceCode", + "identifier": "bssm", + "description": "Efficient methods for Bayesian inference of state space models \n via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel \n importance sampling type weighted estimators \n (Vihola, Helske, and Franks, 2020, ). \n Gaussian, Poisson, binomial, negative binomial, and Gamma\n observation densities and basic stochastic volatility models \n with linear-Gaussian state dynamics, \n as well as general non-linear Gaussian models and discretised \n diffusion models are supported.", + "name": "bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space\n Models", + "codeRepository": "https://github.com/helske/bssm", + "issueTracker": "https://github.com/helske/bssm/issues", + "license": "https://spdx.org/licenses/GPL-2.0", + "version": "1.1.6", + "programmingLanguage": { + "@type": "ComputerLanguage", + "name": "R", + "url": "https://r-project.org" + }, + "runtimePlatform": "R Under development (unstable) (2021-04-12 r80161)", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": 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"cpp"], + "citation": [ + { + "@type": "CreativeWork", + "datePublished": "2021", + "author": [ + { + "@type": "Person", + "givenName": "Jouni", + "familyName": "Helske" + }, + { + "@type": "Person", + "givenName": "Matti", + "familyName": "Vihola" + } + ], + "name": "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", + "url": "https://arxiv.org/abs/2101.08492" + }, + { + "@type": "ScholarlyArticle", + "datePublished": "2020", + "author": [ + { + "@type": "Person", + "givenName": "Matti", + "familyName": "Vihola" + }, + { + "@type": "Person", + "givenName": "Jouni", + "familyName": "Helske" + }, + { + "@type": "Person", + "givenName": "Jordan", + "familyName": "Franks" + } + ], + "name": "Importance Sampling Type Estimators Based on Approximate Marginal {MCMC}", + "identifier": "10.1111/sjos.12492", + "url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492", + "@id": "https://doi.org/10.1111/sjos.12492", + "sameAs": "https://doi.org/10.1111/sjos.12492", + "isPartOf": { + "@type": "PublicationIssue", + "datePublished": "2020", + "isPartOf": { + "@type": ["PublicationVolume", "Periodical"], + "name": "Scandinavian Journal of Statistics" + } + } + } + ] +} From 75cfac00e435463a4d7ed2cc9c321c09fb83aad4 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 15:50:26 +0300 Subject: [PATCH 013/180] xreg from a list --- R/models.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/models.R b/R/models.R index 10233248..d3472711 100644 --- a/R/models.R +++ b/R/models.R @@ -743,7 +743,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, rownames(T) <- colnames(T) <- rownames(R) <- state_names - if(ncol(xreg) > 1) { + if(ncol(regression_part$xreg) > 1) { priors <- c(list(sd_y, sd_level, sd_slope, sd_seasonal), regression_part$beta) } else { @@ -997,7 +997,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, names(a1) <- rownames(P1) <- colnames(P1) <- rownames(Z) <- rownames(T) <- colnames(T) <- rownames(R) <- state_names - if(ncol(xreg) > 1) { + if(ncol(regression_part$xreg) > 1) { priors <- c(list(sd_level, sd_slope, sd_seasonal, sd_noise, phi), regression_part$beta) } else { @@ -1209,7 +1209,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, rownames(T) <- colnames(T) <- rownames(R) <- "signal" - if(ncol(xreg) > 1) { + if(ncol(regression_part$xreg) > 1) { priors <- c(list(rho, sigma, mu, phi), beta) } else { priors <- list(rho, sigma, mu, phi, beta) @@ -1303,7 +1303,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { rownames(T) <- colnames(T) <- rownames(R) <- "signal" - if(ncol(xreg) > 1) { + if(ncol(regression_part$xreg) > 1) { priors <- c(list(rho, sigma, mu, sd_y), regression_part$beta) } else { priors <- list(rho, sigma, mu, sd_y, regression_part$beta) From 2b2bd4c6fcfec399404743e45de48a2487c266f1 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 16:39:10 +0300 Subject: [PATCH 014/180] typo --- R/models.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/models.R b/R/models.R index d3472711..a0b25ac6 100644 --- a/R/models.R +++ b/R/models.R @@ -748,7 +748,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, regression_part$beta) } else { priors <- list(sd_y, sd_level, sd_slope, sd_seasonal, - regresssion_part$beta) + regression_part$beta) } names(priors) <- c("sd_y", "sd_level", "sd_slope", "sd_seasonal", names(regression_part$coefs)) From 6593d114a97359fc24a7ac6c232aa45ab071628c Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 16:47:11 +0300 Subject: [PATCH 015/180] fix check_R calls --- R/models.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/models.R b/R/models.R index a0b25ac6..ef66b655 100644 --- a/R/models.R +++ b/R/models.R @@ -193,7 +193,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), T <- check_T(T, m, n) # create R - R <- check_R(R) + R <- check_R(R, m, n) a1 <- check_a1(a1, m) @@ -310,7 +310,7 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, T <- check_T(T, m, n) # create R - R <- check_R(R) + R <- check_R(R, m, n) a1 <- check_a1(a1, m) @@ -427,7 +427,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), T <- check_T(T, m, n) # create R - R <- check_R(R) + R <- check_R(R, m, n) a1 <- check_a1(a1, m) P1 <- check_P1(P1, m) @@ -535,7 +535,7 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, T <- check_T(T, m, n) # create R - R <- check_R(R) + R <- check_R(R, m, n) a1 <- check_a1(a1, m) P1 <- check_P1(P1, m) From 24bf5e0d5480a6936c2fae84f6b2b03fda71028c Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 18:33:48 +0300 Subject: [PATCH 016/180] fix arg checks --- R/check_arguments.R | 39 ++++++++++++++++++++++-------------- R/models.R | 34 ++++++++++++++++--------------- R/priors.R | 3 ++- tests/testthat/test_models.R | 4 ++-- 4 files changed, 46 insertions(+), 34 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 1441f582..a3335592 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -6,7 +6,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { stop("Argument y must be a numeric matrix or multivariate ts object.") } } else { - if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { + if (!is.numeric(unclass(x))) { stop("Argument y must be a numeric vector or ts object.") } if (distribution != "gaussian" && any(na.omit(x) < 0)) { @@ -15,7 +15,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } else { if (distribution %in% c("negative binomial", "binomial", "poisson") && - any(na.omit(x != as.integer(x)))) { + any(na.omit(x[is.finite(x)] != as.integer(x[is.finite(x)])))) { stop(paste0("Non-integer values not allowed for ", distribution, " distribution. ")) } @@ -28,7 +28,6 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { stop("Length of argument y must be at least two.") } } - } check_distribution <- function(x, distribution) { @@ -80,7 +79,7 @@ check_xreg <- function(x, n) { } check_beta <- function(x, k) { - + if(!is.numeric(x)) stop("'beta' must be numeric. ") if (length(x) != k) { stop(paste("Number of coefficients in beta is not equal to the number", "of columns of xreg.", sep = " ")) @@ -147,9 +146,10 @@ check_target <- function(target) { check_D <- function(x, p, n) { - if (missing(x)) { - if (p == 1) 0 else matrix(0, p, 1) + if (missing(x) || is.null(x)) { + x <- if (p == 1) 0 else matrix(0, p, 1) } else { + if(!is.numeric(x)) stop("'D' must be numeric. ") if (p == 1) { if (!(length(x) %in% c(1, n))) { stop(paste("'D' must be a scalar or length n, where n is the number of", @@ -167,9 +167,10 @@ check_D <- function(x, p, n) { } check_C <- function(x, m, n) { - if (missing(x)) { - matrix(0, m, 1) + if (missing(x) || is.null(x)) { + x <- matrix(0, m, 1) } else { + if(!is.numeric(x)) stop("'C' must be numeric. ") if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1, n))) { stop(paste("'C' must be m x 1 or m x n matrix, where m is", "the number of states.", sep = " ")) @@ -179,7 +180,7 @@ check_C <- function(x, m, n) { } create_regression <- function(beta, xreg, n) { - if (is.null(xreg)) { + if (missing(xreg) || is.null(xreg)) { list(xreg = matrix(0, 0, 0), coefs = numeric(0), beta = NULL) } else { if (missing(beta) || is.null(beta)) { @@ -211,8 +212,9 @@ create_regression <- function(beta, xreg, n) { } } -check_Z <- function(x, p, n) { - if (p == 1) { +check_Z <- function(x, p, n, multivariate = FALSE) { + if(!is.numeric(x)) stop("'Z' must be numeric. ") + if (!multivariate) { if (length(x) == 1) { dim(x) <- c(1, 1) } else { @@ -239,6 +241,7 @@ check_Z <- function(x, p, n) { } check_T <- function(x, m, n) { + if(!is.numeric(x)) stop("'T' must be numeric. ") if (length(x) == 1 && m == 1) { dim(x) <- c(1, 1, 1) } else { @@ -256,6 +259,7 @@ check_R <- function(x, m, n) { if (length(x) == m) { dim(x) <- c(m, 1, 1) } else { + if(!is.numeric(x)) stop("'R' must be numeric. ") if (!(dim(x)[1] == m) || dim(x)[2] > m || !dim(x)[3] %in% c(1, NA, n)) { stop(paste("'R' must be a (m x k) matrix, (m x k x 1) or", "(m x k x n) array, where k<=m is the number of disturbances eta,", @@ -269,9 +273,10 @@ check_R <- function(x, m, n) { } check_a1 <- function(x, m) { - if (missing(x)) { + if (missing(x) || is.null(x)) { x <- numeric(m) } else { + if(!is.numeric(x)) stop("'a1' must be numeric. ") if (length(x) == 1 || length(x) == m) { x <- rep(x, length.out = m) } else { @@ -283,9 +288,10 @@ check_a1 <- function(x, m) { } check_P1 <- function(x, m) { - if (missing(x)) { + if (missing(x) || is.null(x)) { x <- matrix(0, m, m) } else { + if(!is.numeric(x)) stop("'P1' must be numeric. ") if (length(x) == 1 && m == 1) { dim(x) <- c(1, 1) } else { @@ -297,8 +303,11 @@ check_P1 <- function(x, m) { x } -check_H <- function(x, p, n) { - if (p == 1) { +check_H <- function(x, p, n, multivariate = FALSE) { + + if(!is.numeric(x)) stop("'H' must be numeric. ") + + if (!multivariate) { if (!(length(x) %in% c(1, n))) { stop(paste("'H' must be a scalar or length n, where n is the length of", "the time series y", sep = " ")) diff --git a/R/models.R b/R/models.R index ef66b655..c5eee1ed 100644 --- a/R/models.R +++ b/R/models.R @@ -178,8 +178,8 @@ default_update_fn <- function(theta) { #' out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) #' out2 #' } -ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), - D, C, state_names, update_fn = default_update_fn, +ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, init_theta = numeric(0), + D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { check_y(y) @@ -294,8 +294,9 @@ ssm_ulg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), #' # approximate results based on Gaussian approximation #' out <- smoother(model) #' ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) -ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, - init_theta = numeric(0), D, C, state_names, update_fn = default_update_fn, +ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, + u = 1, init_theta = numeric(0), D = NULL, C = NULL, state_names, + update_fn = default_update_fn, prior_fn = default_prior_fn) { @@ -411,9 +412,9 @@ ssm_ung <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) #' ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) #' -ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), - D, C, state_names, update_fn = default_update_fn, - prior_fn = default_prior_fn) { +ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, + init_theta = numeric(0), D = NULL, C = NULL, state_names, + update_fn = default_update_fn, prior_fn = default_prior_fn) { # create y check_y(y, multivariate = TRUE) @@ -421,7 +422,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), p <- ncol(y) # create Z - Z <- check_Z(Z, p, n) + Z <- check_Z(Z, p, n, multivariate = TRUE) m <- dim(Z)[2] T <- check_T(T, m, n) @@ -435,7 +436,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), D <- check_D(D, p, n) C <- check_C(C, m, n) - H <- check_H(H, p, n) + H <- check_H(H, p, n, multivariate = TRUE) if (missing(state_names)) { state_names <- paste("state", 1:m) @@ -510,9 +511,9 @@ ssm_mlg <- function(y, Z, H, T, R, a1, P1, init_theta = numeric(0), #' @param state_names Names for the states. #' @return Object of class \code{ssm_mng}. #' @export -ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, - init_theta = numeric(0), D, C, state_names, update_fn = default_update_fn, - prior_fn = default_prior_fn) { +ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, + phi = 1, u = 1, init_theta = numeric(0), D = NULL, C = NULL, state_names, + update_fn = default_update_fn, prior_fn = default_prior_fn) { # create y @@ -529,7 +530,7 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, } # create Z - Z <- check_Z(Z, p, n) + Z <- check_Z(Z, p, n, multivariate = TRUE) m <- dim(Z)[2] T <- check_T(T, m, n) @@ -611,7 +612,8 @@ ssm_mng <- function(y, Z, T, R, a1, P1, distribution, phi = 1, u = 1, #' sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] #' bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, - beta, xreg = NULL, period = frequency(y), a1, P1, D, C) { + beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, D = NULL, + C = NULL) { check_y(y) n <- length(y) @@ -840,8 +842,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' ggplot(aes(y = value, x = iter)) + geom_line() #' } bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, - distribution, phi, u = 1, beta, xreg = NULL, period = frequency(y), a1, P1, - C) { + distribution, phi, u = 1, beta, xreg = NULL, period = frequency(y), + a1 = NULL, P1 = NULL, C = NULL) { distribution <- match.arg(distribution, diff --git a/R/priors.R b/R/priors.R index 892ee21b..6603a077 100644 --- a/R/priors.R +++ b/R/priors.R @@ -140,7 +140,8 @@ combine_priors <- function(x) { if (length(x) == 0) return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) - prior_distributions <- vapply(x, "[[", "prior_distribution", character(1)) + prior_distributions <- vapply(x, "[[", "prior_distribution", + FUN.VALUE = character(1)) parameters <- matrix(NA, 4, length(prior_distributions)) for (i in seq_along(prior_distributions)) { parameters[1:(length(x[[i]]) - 2), i] <- as.numeric(x[[i]][-(1:2)]) diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index 30dad830..189d8d10 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -11,7 +11,7 @@ test_that("bad argument values for bsm throws an error", { expect_error(bsm_lg(1:10, xreg = matrix(NA))) expect_error(bsm_lg(1:10, xreg = matrix(1:20), beta = uniform(0, 0, 1))) expect_error(bsm_lg(1:10, xreg = 1:10, beta = NA)) - expect_error(bsm_lg(1:10, 1, 1, 1, a1 = 1)) + expect_error(bsm_lg(1:10, 1, 1, 1, a1 = 1:4)) expect_error(bsm_lg(1:10, 1, 1, 1, 1)) }) @@ -36,7 +36,7 @@ test_that("bad argument values for bsm_ng throws an error", { expect_error(bsm_ng(1:10, xreg = matrix(1:20), beta = uniform(0, 0, 1), distribution = "poisson")) expect_error(bsm_ng(1:10, xreg = 1:10, beta = NA, distribution = "poisson")) - expect_error(bsm_ng(1:10, 1, 1, a1 = 1, distribution = "poisson")) + expect_error(bsm_ng(1:10, 1, 1, a1 = "a", distribution = "poisson")) expect_error(bsm_ng(1:10, 1, 1, 1, 1, distribution = "poisson")) }) From e7aa137cc6a6b87967df169ccd8ebee96b45a7c5 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 19:50:22 +0300 Subject: [PATCH 017/180] small tweaks, modify github actions for covr --- .github/CONTRIBUTING.md | 2 +- .github/workflows/R-CMD-check.yaml | 71 ++++++++++++------------------ R/approx.R | 3 +- R/models.R | 6 ++- R/post_correction.R | 3 +- 5 files changed, 37 insertions(+), 48 deletions(-) diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 610031a5..c50d646f 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -34,4 +34,4 @@ GitHub username, and links to relevant issue(s)/PR(s). ### Thanks for contributing! -This contributing guide is adapted from the tidyverse contributing guide available at https://raw.githubusercontent.com/r-lib/usethis/master/inst/templates/tidy-contributing.md \ No newline at end of file +This contributing guide is adapted from the tidyverse contributing guide available at https://raw.githubusercontent.com/r-lib/usethis/master/inst/templates/tidy-contributing.md diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index a2dbc238..07afb6ff 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -1,14 +1,10 @@ -# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag. -# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: - - main - - master + branches: [main, master] pull_request: - branches: - - main - - master + branches: [main, master] name: R-CMD-check @@ -22,62 +18,51 @@ jobs: fail-fast: false matrix: config: + - {os: macOS-latest, r: 'release'} - {os: windows-latest, r: 'release'} - - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} - - {os: ubuntu-20.04, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel/1'} env: - R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - RSPM: ${{ matrix.config.rspm }} GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes steps: - uses: actions/checkout@v2 + - uses: r-lib/actions/setup-pandoc@v1 + - uses: r-lib/actions/setup-r@v1 with: r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - if: runner.os != 'Windows' - uses: actions/cache@v2 + - uses: r-lib/actions/setup-r-dependencies@v1 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install system dependencies - if: runner.os == 'Linux' - run: | - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))') - - - name: Install dependencies - run: | - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - shell: Rscript {0} + extra-packages: rcmdcheck, covr - name: Check env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") + _R_CHECK_CRAN_INCOMING_: false + run: | + options(crayon.enabled = TRUE) + rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check") shell: Rscript {0} + - name: Show testthat output + if: always() + run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + - name: Upload check results if: failure() uses: actions/upload-artifact@main with: name: ${{ runner.os }}-r${{ matrix.config.r }}-results path: check + + - name: Test coverage + run: covr::codecov() + shell: Rscript {0} \ No newline at end of file diff --git a/R/approx.R b/R/approx.R index 5471652b..2f0c6cb9 100644 --- a/R/approx.R +++ b/R/approx.R @@ -45,7 +45,8 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, out$y <- ts(c(out$y), start = start(model$y), end = end(model$y), frequency = frequency(model$y)) D <- model$D - if (length(model$beta) > 0) D <- as.numeric(D) + t(model$xreg %*% model$beta) + if (length(model$beta) > 0) + D <- as.numeric(D) + t(model$xreg %*% model$beta) approx_model <- ssm_ulg(y = out$y, Z = model$Z, H = out$H, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, init_theta = model$theta, D = D, C = model$C, state_names = names(model$a1), diff --git a/R/models.R b/R/models.R index c5eee1ed..2569f708 100644 --- a/R/models.R +++ b/R/models.R @@ -178,7 +178,8 @@ default_update_fn <- function(theta) { #' out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) #' out2 #' } -ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, init_theta = numeric(0), +ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, + init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { @@ -218,7 +219,8 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, init_theta = numeric(0) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, a1 = a1, P1 = P1, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, - xreg = matrix(0, 0, 0), beta = numeric(0)), class = c("ssm_ulg", "gaussian")) + xreg = matrix(0, 0, 0), beta = numeric(0)), + class = c("ssm_ulg", "gaussian")) } #' General univariate non-Gaussian state space model #' diff --git a/R/post_correction.R b/R/post_correction.R index a7063d66..f8eec817 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -255,7 +255,8 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, } } mcmc_output$time <- - rbind("approx" = mcmc_output$time, "postcorrection" = proc.time() - a)[, 1:3] + rbind("approx" = mcmc_output$time, + "postcorrection" = proc.time() - a)[, 1:3] mcmc_output$mcmc_type <- paste0("is", is_type) mcmc_output$seed <- c(mcmc_output$seed, seed) mcmc_output$call <- c(mcmc_output$call, match.call()) From 9ccc1935dc1f9f874ce790cd038a9620827eec37 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 21:15:49 +0300 Subject: [PATCH 018/180] return one step predictions from EKPF --- src/R_ekpf.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/R_ekpf.cpp b/src/R_ekpf.cpp index 09ef18b2..9df1f4d4 100644 --- a/src/R_ekpf.cpp +++ b/src/R_ekpf.cpp @@ -45,8 +45,8 @@ Rcpp::List ekpf(const arma::mat& y, SEXP Z, SEXP H, arma::inplace_trans(att); return Rcpp::List::create( - Rcpp::Named("att") = att, - Rcpp::Named("Ptt") = Ptt, + Rcpp::Named("at") = at, Rcpp::Named("att") = att, + Rcpp::Named("Pt") = Pt, Rcpp::Named("Ptt") = Ptt, Rcpp::Named("weights") = weights, Rcpp::Named("logLik") = loglik, Rcpp::Named("alpha") = alpha); } From 28791efd25f47e3cf628d7af7740c443ef91a060 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 21:42:59 +0300 Subject: [PATCH 019/180] EKFP transpose at --- src/R_ekpf.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/R_ekpf.cpp b/src/R_ekpf.cpp index 9df1f4d4..74c1ad27 100644 --- a/src/R_ekpf.cpp +++ b/src/R_ekpf.cpp @@ -43,6 +43,7 @@ Rcpp::List ekpf(const arma::mat& y, SEXP Z, SEXP H, arma::cube Ptt(m, m, n + 1); filter_summary(alpha, at, att, Pt, Ptt, weights); + arma::inplace_trans(at); arma::inplace_trans(att); return Rcpp::List::create( Rcpp::Named("at") = at, Rcpp::Named("att") = att, From 64c30d84758806b6be2245d659dcd29c4e469db0 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 22:37:35 +0300 Subject: [PATCH 020/180] nlg example models --- R/nlg_example_models.R | 267 ++++++++++++++++++++++++++++++++++++++ man/nlg_example_models.Rd | 21 +++ 2 files changed, 288 insertions(+) create mode 100644 R/nlg_example_models.R create mode 100644 man/nlg_example_models.Rd diff --git a/R/nlg_example_models.R b/R/nlg_example_models.R new file mode 100644 index 00000000..0654323e --- /dev/null +++ b/R/nlg_example_models.R @@ -0,0 +1,267 @@ +#' Example C++ Codes for Non-Linear Models +#' +#' @param example Name of the example model. +#' Run \code{nlg_example_models("abc")} to get the names of possible models. +#' @param return_code If TRUE, will not compile the model but only returns the +#' corresponding code. +#' +#' @return Returns pointers to the C++ snippets defining the model, or in case +#' of \code{return_code = TRUE}, returns the example code without compiling. +#' @export +nlg_example_models <- function(example, return_code = FALSE) { + + example <- match.arg(example, c("linear_gaussian", "sin_exp")) + code <- switch(example, + "linear_gaussian" = { + ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = 0; + return a1; + } + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1, 1); + P1(0,0) = 1; + return P1; + } + + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat R(1, 1); + R(0, 0) = 1; + return R; + } + + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = 1.0; + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = 1.0; + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0); //jacobian term + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation + // returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + } + ' + }, + "sin_exp" = { + ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = 0; + return a1; + } + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1, 1); + P1(0,0) = 1; + return P1; + } + + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat R(1, 1); + R(0, 0) = exp(theta(1)); + return R; + } + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return exp(alpha); + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = exp(alpha(0)); + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return sin(alpha); + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = cos(alpha(0)); + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0) + + R::dnorm(exp(theta(1)), 0, 1, 1) + theta(1); + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation + // returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + } + ' + }) + if (!return_code) { + Rcpp::sourceCpp(code = code) + create_xptrs() + } else code +} diff --git a/man/nlg_example_models.Rd b/man/nlg_example_models.Rd new file mode 100644 index 00000000..c99aef04 --- /dev/null +++ b/man/nlg_example_models.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ngl_example_models.R +\name{nlg_example_models} +\alias{nlg_example_models} +\title{Example C++ Codes for Non-Linear Models} +\usage{ +nlg_example_models(example, return_code = FALSE) +} +\arguments{ +\item{example}{Name of the example model.} + +\item{return_code}{If TRUE, will not compile the model but only returns the +corresponding code.} +} +\value{ +Returns pointers to the C++ snippets defining the model, or in case +of \code{return_code = TRUE}, returns the example code without compiling. +} +\description{ +Example C++ Codes for Non-Linear Models +} From f3e9575bd5c038c4d670b0a5b08782eb6d6ea9d4 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 22:38:01 +0300 Subject: [PATCH 021/180] more tests --- NAMESPACE | 189 ++++++------ NEWS | 2 + R/ekpf_filter.R | 25 +- man/ar1_ng.Rd | 80 +++--- man/bsm_lg.Rd | 145 +++++----- man/bsm_ng.Rd | 208 +++++++------- man/bssm.Rd | 6 +- man/ekf_smoother.Rd | 49 ++-- man/ekpf_filter.Rd | 24 ++ man/expand_sample.Rd | 58 ++-- man/importance_sample.Rd | 4 +- man/particle_smoother.Rd | 240 ++++++++-------- man/post_correct.Rd | 39 ++- man/predict.mcmc_output.Rd | 21 +- man/priors.Rd | 109 +++---- man/run_mcmc.Rd | 55 ++-- man/run_mcmc.ssm_nlg.Rd | 212 +++++++------- man/run_mcmc.ssm_sde.Rd | 185 ++++++------ man/run_mcmc_g.Rd | 183 ++++++------ man/run_mcmc_ng.Rd | 430 ++++++++++++++-------------- man/sim_smoother.Rd | 110 +++---- man/smoother.Rd | 57 ++-- man/ssm_mlg.Rd | 195 +++++++------ man/ssm_mng.Rd | 190 ++++++------ man/ssm_nlg.Rd | 165 ++++++----- man/ssm_sde.Rd | 175 +++++------ man/ssm_ulg.Rd | 46 +-- man/ssm_ung.Rd | 222 +++++++------- man/suggest_N.Rd | 37 +-- man/summary.mcmc_output.Rd | 73 ++--- man/svm.Rd | 101 +++---- tests/testthat/test_approx.R | 127 +------- tests/testthat/test_as_data_frame.R | 41 +++ 33 files changed, 1953 insertions(+), 1850 deletions(-) create mode 100644 tests/testthat/test_as_data_frame.R diff --git a/NAMESPACE b/NAMESPACE index c710e852..44631cf0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,94 +1,95 @@ -# Generated by roxygen2: do not edit by hand - -S3method(as.data.frame,mcmc_output) -S3method(bootstrap_filter,gaussian) -S3method(bootstrap_filter,nongaussian) -S3method(bootstrap_filter,ssm_nlg) -S3method(bootstrap_filter,ssm_sde) -S3method(ekpf_filter,ssm_nlg) -S3method(fast_smoother,gaussian) -S3method(fast_smoother,nongaussian) -S3method(gaussian_approx,nongaussian) -S3method(gaussian_approx,ssm_nlg) -S3method(importance_sample,nongaussian) -S3method(kfilter,gaussian) -S3method(kfilter,nongaussian) -S3method(logLik,gaussian) -S3method(logLik,nongaussian) -S3method(logLik,ssm_nlg) -S3method(logLik,ssm_sde) -S3method(particle_smoother,gaussian) -S3method(particle_smoother,nongaussian) -S3method(particle_smoother,ssm_nlg) -S3method(particle_smoother,ssm_sde) -S3method(predict,mcmc_output) -S3method(print,mcmc_output) -S3method(run_mcmc,gaussian) -S3method(run_mcmc,nongaussian) -S3method(run_mcmc,ssm_nlg) -S3method(run_mcmc,ssm_sde) -S3method(sim_smoother,gaussian) -S3method(sim_smoother,nongaussian) -S3method(smoother,gaussian) -S3method(smoother,nongaussian) -S3method(summary,mcmc_output) -export(ar1_lg) -export(ar1_ng) -export(as_bssm) -export(asymptotic_var) -export(bootstrap_filter) -export(bsm_lg) -export(bsm_ng) -export(ekf) -export(ekf_smoother) -export(ekpf_filter) -export(expand_sample) -export(fast_smoother) -export(gamma) -export(gaussian_approx) -export(halfnormal) -export(importance_sample) -export(kfilter) -export(normal) -export(particle_smoother) -export(post_correct) -export(run_mcmc) -export(sim_smoother) -export(smoother) -export(ssm_mlg) -export(ssm_mng) -export(ssm_nlg) -export(ssm_sde) -export(ssm_ulg) -export(ssm_ung) -export(suggest_N) -export(svm) -export(tnormal) -export(ukf) -export(uniform) -importFrom(Rcpp,evalCpp) -importFrom(coda,mcmc) -importFrom(coda,spectrum0.ar) -importFrom(diagis,ess) -importFrom(diagis,weighted_mean) -importFrom(diagis,weighted_se) -importFrom(diagis,weighted_var) -importFrom(stats,"tsp<-") -importFrom(stats,as.ts) -importFrom(stats,cov) -importFrom(stats,dnorm) -importFrom(stats,end) -importFrom(stats,frequency) -importFrom(stats,is.ts) -importFrom(stats,logLik) -importFrom(stats,na.omit) -importFrom(stats,qlogis) -importFrom(stats,quantile) -importFrom(stats,sd) -importFrom(stats,start) -importFrom(stats,time) -importFrom(stats,ts) -importFrom(stats,ts.union) -importFrom(stats,tsp) -importFrom(stats,var) -useDynLib(bssm) +# Generated by roxygen2: do not edit by hand + +S3method(as.data.frame,mcmc_output) +S3method(bootstrap_filter,gaussian) +S3method(bootstrap_filter,nongaussian) +S3method(bootstrap_filter,ssm_nlg) +S3method(bootstrap_filter,ssm_sde) +S3method(ekpf_filter,ssm_nlg) +S3method(fast_smoother,gaussian) +S3method(fast_smoother,nongaussian) +S3method(gaussian_approx,nongaussian) +S3method(gaussian_approx,ssm_nlg) +S3method(importance_sample,nongaussian) +S3method(kfilter,gaussian) +S3method(kfilter,nongaussian) +S3method(logLik,gaussian) +S3method(logLik,nongaussian) +S3method(logLik,ssm_nlg) +S3method(logLik,ssm_sde) +S3method(particle_smoother,gaussian) +S3method(particle_smoother,nongaussian) +S3method(particle_smoother,ssm_nlg) +S3method(particle_smoother,ssm_sde) +S3method(predict,mcmc_output) +S3method(print,mcmc_output) +S3method(run_mcmc,gaussian) +S3method(run_mcmc,nongaussian) +S3method(run_mcmc,ssm_nlg) +S3method(run_mcmc,ssm_sde) +S3method(sim_smoother,gaussian) +S3method(sim_smoother,nongaussian) +S3method(smoother,gaussian) +S3method(smoother,nongaussian) +S3method(summary,mcmc_output) +export(ar1_lg) +export(ar1_ng) +export(as_bssm) +export(asymptotic_var) +export(bootstrap_filter) +export(bsm_lg) +export(bsm_ng) +export(ekf) +export(ekf_smoother) +export(ekpf_filter) +export(expand_sample) +export(fast_smoother) +export(gamma) +export(gaussian_approx) +export(halfnormal) +export(importance_sample) +export(kfilter) +export(nlg_example_models) +export(normal) +export(particle_smoother) +export(post_correct) +export(run_mcmc) +export(sim_smoother) +export(smoother) +export(ssm_mlg) +export(ssm_mng) +export(ssm_nlg) +export(ssm_sde) +export(ssm_ulg) +export(ssm_ung) +export(suggest_N) +export(svm) +export(tnormal) +export(ukf) +export(uniform) +importFrom(Rcpp,evalCpp) +importFrom(coda,mcmc) +importFrom(coda,spectrum0.ar) +importFrom(diagis,ess) +importFrom(diagis,weighted_mean) +importFrom(diagis,weighted_se) +importFrom(diagis,weighted_var) +importFrom(stats,"tsp<-") +importFrom(stats,as.ts) +importFrom(stats,cov) +importFrom(stats,dnorm) +importFrom(stats,end) +importFrom(stats,frequency) +importFrom(stats,is.ts) +importFrom(stats,logLik) +importFrom(stats,na.omit) +importFrom(stats,qlogis) +importFrom(stats,quantile) +importFrom(stats,sd) +importFrom(stats,start) +importFrom(stats,time) +importFrom(stats,ts) +importFrom(stats,ts.union) +importFrom(stats,tsp) +importFrom(stats,var) +useDynLib(bssm) diff --git a/NEWS b/NEWS index f6bf520a..63b3e485 100644 --- a/NEWS +++ b/NEWS @@ -1,5 +1,7 @@ bssm 1.1.6 (Release date: ) ============== + * Fixed a bug in EKF-based particle filter which returned filtered estimates + also in place of one-step ahead predictions. * Cleaned some codes and added more comprehensive tests in line with pkgcheck tests. bssm 1.1.5 (Release date: 2021-06-14) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 9063c0c4..51bb2984 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -20,6 +20,29 @@ ekpf_filter <- function(object, particles, ...) { #' @method ekpf_filter ssm_nlg #' @export #' @rdname ekpf_filter +#' @examples +#' \dontrun{ +#' set.seed(1) +#' n <- 50 +#' x <- y <- numeric(n) +#' y[1] <- rnorm(1, exp(x[1]), 0.1) +#' for(i in 1:(n-1)) { +#' x[i+1] <- rnorm(1, sin(x[i]), 0.1) +#' y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +#' } +#' +#' pntrs <- nlg_example_models("sin_exp") +#' +#' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, +#' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, +#' Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, +#' theta = c(log_H = log(0.1), log_R = log(0.1)), +#' log_prior_pdf = pntrs$log_prior_pdf, +#' n_states = 1, n_etas = 1, state_names = "state") +#' +#' out <- ekpf_filter(model_nlg, 100) +#' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +#' } ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$integer.max, size = 1), ...) { @@ -27,7 +50,7 @@ ekpf_filter.ssm_nlg <- function(object, particles, nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument", - "`particles` instead.", sep = " ")) + "`particles` instead.", sep = " ")) particles <- nsim } } diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 9aa1e85b..2b5816db 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -1,38 +1,42 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ar1_ng} -\alias{ar1_ng} -\title{Non-Gaussian model with AR(1) latent process} -\usage{ -ar1_ng(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sigma}{Prior for the standard deviation of noise of the AR-process.} - -\item{mu}{A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0.} - -\item{distribution}{Distribution of the observed time series. Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma distribution -this is the shape parameter, and for other distributions this is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -negative binomial distribution, this corresponds to the offset term. For binomial, -this is the number of trials.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} -} -\value{ -Object of class \code{ar1_ng}. -} -\description{ -Constructs a simple non-Gaussian model where the state dynamics follow an AR(1) process. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ar1_ng} +\alias{ar1_ng} +\title{Non-Gaussian model with AR(1) latent process} +\usage{ +ar1_ng(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{rho}{prior for autoregressive coefficient.} + +\item{sigma}{Prior for the standard deviation of noise of the AR-process.} + +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Parameter is omitted if this is set to 0.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored.} + +\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{beta}{Prior for the regression coefficients.} + +\item{xreg}{Matrix containing covariates.} +} +\value{ +Object of class \code{ar1_ng}. +} +\description{ +Constructs a simple non-Gaussian model where the state dynamics follow an +AR(1) process. +} diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index fc755634..1853c6ae 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -1,72 +1,73 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{bsm_lg} -\alias{bsm_lg} -\title{Basic Structural (Time Series) Model} -\usage{ -bsm_lg( - y, - sd_y, - sd_level, - sd_slope, - sd_seasonal, - beta, - xreg = NULL, - period = frequency(y), - a1, - P1, - D, - C -) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{sd_y}{A fixed value or prior for the standard error of -observation equation. See \link[=uniform]{priors} for details.} - -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} - -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} - -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} - -\item{period}{Length of the seasonal component i.e. the number of} - -\item{a1}{Prior means for the initial states (level, slope, seasonals). -Defaults to vector of zeros.} - -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1000 on the diagonal.} - -\item{D, C}{Intercept terms for observation and -state equations, given as a length n vector and m times n matrix respectively.} -} -\value{ -Object of class \code{bsm_lg}. -} -\description{ -Constructs a basic structural model with local level or local trend component -and seasonal component. -} -\examples{ - -prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) -model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, - sd_slope = prior, sd_seasonal = prior) - -mcmc_out <- run_mcmc(model, iter = 5000) -summary(expand_sample(mcmc_out, "theta"))$stat -mcmc_out$theta[which.max(mcmc_out$posterior), ] -sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{bsm_lg} +\alias{bsm_lg} +\title{Basic Structural (Time Series) Model} +\usage{ +bsm_lg( + y, + sd_y, + sd_level, + sd_slope, + sd_seasonal, + beta, + xreg = NULL, + period = frequency(y), + a1 = NULL, + P1 = NULL, + D = NULL, + C = NULL +) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{sd_y}{A fixed value or prior for the standard error of +observation equation. See \link[=uniform]{priors} for details.} + +\item{sd_level}{A fixed value or a prior for the standard error +of the noise in level equation. See \link[=uniform]{priors} for details.} + +\item{sd_slope}{A fixed value or a prior for the standard error +of the noise in slope equation. See \link[=uniform]{priors} for details. +If missing, the slope term is omitted from the model.} + +\item{sd_seasonal}{A fixed value or a prior for the standard error +of the noise in seasonal equation. See \link[=uniform]{priors} for details. +If missing, the seasonal component is omitted from the model.} + +\item{beta}{Prior for the regression coefficients.} + +\item{xreg}{Matrix containing covariates.} + +\item{period}{Length of the seasonal component i.e. the number of} + +\item{a1}{Prior means for the initial states (level, slope, seasonals). +Defaults to vector of zeros.} + +\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +Default is diagonal matrix with 1000 on the diagonal.} + +\item{D, C}{Intercept terms for observation and +state equations, given as a length n vector and m times n matrix +respectively (or scalar and m times 1 matrix).} +} +\value{ +Object of class \code{bsm_lg}. +} +\description{ +Constructs a basic structural model with local level or local trend +component and seasonal component. +} +\examples{ + +prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, + sd_slope = prior, sd_seasonal = prior) + +mcmc_out <- run_mcmc(model, iter = 5000) +summary(expand_sample(mcmc_out, "theta"))$stat +mcmc_out$theta[which.max(mcmc_out$posterior), ] +sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] + +} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index c81bc07b..5276b5d7 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -1,102 +1,106 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{bsm_ng} -\alias{bsm_ng} -\title{Non-Gaussian Basic Structural (Time Series) Model} -\usage{ -bsm_ng( - y, - sd_level, - sd_slope, - sd_seasonal, - sd_noise, - distribution, - phi, - u = 1, - beta, - xreg = NULL, - period = frequency(y), - a1, - P1, - C -) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} - -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} - -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} - -\item{sd_noise}{Prior for the standard error of the additional noise term. -See \link[=uniform]{priors} for details. If missing, no additional noise term is used.} - -\item{distribution}{Distribution of the observed time series. Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma distribution -this is the shape parameter, and for other distributions this is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -negative binomial distribution, this corresponds to the offset term. For binomial, -this is the number of trials.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} - -\item{period}{Length of the seasonal component i.e. the number of -observations per season. Default is \code{frequency(y)}.} - -\item{a1}{Prior means for the initial states (level, slope, seasonals). -Defaults to vector of zeros.} - -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1e5 on the diagonal.} - -\item{C}{Intercept terms for state equation, given as a -m times n matrix.} -} -\value{ -Object of class \code{bsm_ng}. -} -\description{ -Constructs a non-Gaussian basic structural model with local level or -local trend component, a seasonal component, and regression component -(or subset of these components). -} -\examples{ -model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", - sd_level = halfnormal(0.01, 1), - sd_seasonal = halfnormal(0.01, 1), - beta = normal(0, 0, 10), - xreg = Seatbelts[, "law"]) -\dontrun{ -set.seed(123) -mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) -mcmc_out$acceptance_rate -theta <- expand_sample(mcmc_out, "theta") -plot(theta) -summary(theta) - -library("ggplot2") -ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + - geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), - geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + - guides(alpha = "none") - -# Traceplot using as.data.frame method for MCMC output: -library("dplyr") -as.data.frame(mcmc_out) \%>\% - filter(variable == "sd_level") \%>\% - ggplot(aes(y = value, x = iter)) + geom_line() -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{bsm_ng} +\alias{bsm_ng} +\title{Non-Gaussian Basic Structural (Time Series) Model} +\usage{ +bsm_ng( + y, + sd_level, + sd_slope, + sd_seasonal, + sd_noise, + distribution, + phi, + u = 1, + beta, + xreg = NULL, + period = frequency(y), + a1 = NULL, + P1 = NULL, + C = NULL +) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{sd_level}{A fixed value or a prior for the standard error +of the noise in level equation. See \link[=uniform]{priors} for details.} + +\item{sd_slope}{A fixed value or a prior for the standard error +of the noise in slope equation. See \link[=uniform]{priors} for details. +If missing, the slope term is omitted from the model.} + +\item{sd_seasonal}{A fixed value or a prior for the standard error +of the noise in seasonal equation. See \link[=uniform]{priors} for details. +If missing, the seasonal component is omitted from the model.} + +\item{sd_noise}{Prior for the standard error of the additional noise term. +See \link[=uniform]{priors} for details. If missing, no additional noise +term is used.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are +\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, and for other distributions +this is ignored.} + +\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{beta}{Prior for the regression coefficients.} + +\item{xreg}{Matrix containing covariates.} + +\item{period}{Length of the seasonal component i.e. the number of +observations per season. Default is \code{frequency(y)}.} + +\item{a1}{Prior means for the initial states (level, slope, seasonals). +Defaults to vector of zeros.} + +\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +Default is diagonal matrix with 1e5 on the diagonal.} + +\item{C}{Intercept terms for state equation, given as a +m times n matrix.} +} +\value{ +Object of class \code{bsm_ng}. +} +\description{ +Constructs a non-Gaussian basic structural model with local level or +local trend component, a seasonal component, and regression component +(or subset of these components). +} +\examples{ +model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", + sd_level = halfnormal(0.01, 1), + sd_seasonal = halfnormal(0.01, 1), + beta = normal(0, 0, 10), + xreg = Seatbelts[, "law"]) +\dontrun{ +set.seed(123) +mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) +mcmc_out$acceptance_rate +theta <- expand_sample(mcmc_out, "theta") +plot(theta) +summary(theta) + +library("ggplot2") +ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + + geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), + geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + + guides(alpha = "none") + +# Traceplot using as.data.frame method for MCMC output: +library("dplyr") +as.data.frame(mcmc_out) \%>\% + filter(variable == "sd_level") \%>\% + ggplot(aes(y = value, x = iter)) + geom_line() +} +} diff --git a/man/bssm.Rd b/man/bssm.Rd index 93901a04..bc6042ac 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -7,9 +7,9 @@ \description{ This package contains functions for efficient Bayesian inference of state space models, where model is assumed to be either -* Exponential family state space models, where the state equation is linear and Gaussian, - and the conditional observation density is either Gaussian, Poisson, - binomial, negative binomial or Gamma density. +* Exponential family state space models, where the state equation is linear + Gaussian, and the conditional observation density is either Gaussian, + Poisson, binomial, negative binomial or Gamma density. * Basic stochastic volatility model. * General non-linear model with Gaussian noise terms. * Model with continuous SDE dynamics. diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index 93152178..77239c69 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -1,24 +1,25 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smoother.R -\name{ekf_smoother} -\alias{ekf_smoother} -\title{Extended Kalman Smoothing} -\usage{ -ekf_smoother(model, iekf_iter = 0) -} -\arguments{ -\item{model}{Model model} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is -used with \code{iekf_iter} iterations.} -} -\value{ -List containing the log-likelihood, -smoothed state estimates \code{alphahat}, and the corresponding variances \code{Vt} and - \code{Ptt}. -} -\description{ -Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother for -the given non-linear Gaussian model of class \code{ssm_nlg}, -and returns the smoothed estimates of the states and the corresponding variances. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smoother.R +\name{ekf_smoother} +\alias{ekf_smoother} +\title{Extended Kalman Smoothing} +\usage{ +ekf_smoother(model, iekf_iter = 0) +} +\arguments{ +\item{model}{Model model} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations.} +} +\value{ +List containing the log-likelihood, +smoothed state estimates \code{alphahat}, and the corresponding variances +\code{Vt} and \code{Ptt}. +} +\description{ +Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother +for the given non-linear Gaussian model of class \code{ssm_nlg}, +and returns the smoothed estimates of the states and the corresponding +variances. +} diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 733e4f64..f15a8397 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -31,6 +31,30 @@ corresponding covariances, weights, and an estimate of log-likelihood. Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001). } +\examples{ +\dontrun{ +set.seed(1) +n <- 50 +x <- y <- numeric(n) +y[1] <- rnorm(1, exp(x[1]), 0.1) +for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +} + +pntrs <- nlg_example_models("sin_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out <- ekpf_filter(model_nlg, 100) +ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +} +} \references{ Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. In Advances in neural diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index 978f044a..4338bbd3 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -1,27 +1,31 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{expand_sample} -\alias{expand_sample} -\title{Expand the Jump Chain representation} -\usage{ -expand_sample(x, variable = "theta", times, states, by_states = TRUE) -} -\arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} - -\item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} - -\item{times}{Vector of indices. In case of states, what time points to expand? Default is all.} - -\item{states}{Vector of indices. In case of states, what states to expand? Default is all.} - -\item{by_states}{If \code{TRUE} (default), return list by states. Otherwise by time.} -} -\description{ -The MCMC algorithms of \code{bssm} use a jump chain representation where we -store the accepted values and the number of times we stayed in the current value. -Although this saves bit memory and is especially convenient for IS-corrected -MCMC, sometimes we want to have the usual sample paths. Function \code{expand_sample} -returns the expanded sample based on the counts. Note that for IS-corrected output the expanded -sample corresponds to the approximate posterior. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print_mcmc.R +\name{expand_sample} +\alias{expand_sample} +\title{Expand the Jump Chain representation} +\usage{ +expand_sample(x, variable = "theta", times, states, by_states = TRUE) +} +\arguments{ +\item{x}{Output from \code{\link{run_mcmc}}.} + +\item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} + +\item{times}{Vector of indices. In case of states, +what time points to expand? Default is all.} + +\item{states}{Vector of indices. In case of states, +what states to expand? Default is all.} + +\item{by_states}{If \code{TRUE} (default), return list by states. +Otherwise by time.} +} +\description{ +The MCMC algorithms of \code{bssm} use a jump chain representation where we +store the accepted values and the number of times we stayed in the current +value. Although this saves bit memory and is especially convenient for +IS-corrected MCMC, sometimes we want to have the usual sample paths. +Function \code{expand_sample} returns the expanded sample based on the +counts. Note that for IS-corrected output the expanded +sample corresponds to the approximate posterior. +} diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index fb82f9cd..b9b45880 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -29,8 +29,8 @@ models.} \item{max_iter}{Maximum number of iterations used for the approximation.} -\item{conv_tol}{Convergence threshold for the approximation. Approximation is -claimed to be converged when the mean squared difference of the modes is +\item{conv_tol}{Convergence threshold for the approximation. Approximation +is claimed to be converged when the mean squared difference of the modes is less than \code{conv_tol}.} \item{seed}{Seed for the random number generator.} diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index ab78fc11..4d079ada 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -1,112 +1,128 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/particle_smoother.R -\name{particle_smoother} -\alias{particle_smoother} -\alias{particle_smoother.gaussian} -\alias{particle_smoother.nongaussian} -\alias{particle_smoother.ssm_nlg} -\alias{particle_smoother.ssm_sde} -\title{Particle Smoothing} -\usage{ -particle_smoother(model, particles, ...) - -\method{particle_smoother}{gaussian}( - model, - particles, - method = "psi", - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{particle_smoother}{nongaussian}( - model, - particles, - method = "psi", - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - ... -) - -\method{particle_smoother}{ssm_nlg}( - model, - particles, - method = "bsf", - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - ... -) - -\method{particle_smoother}{ssm_sde}( - model, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model.} - -\item{particles}{Number of samples for particle filter.} - -\item{...}{Ignored.} - -\item{method}{Choice of particle filter algorithm. -For Gaussian and non-Gaussian models with linear dynamics, -options are \code{"bsf"} (bootstrap particle filter, default for non-linear models) -and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and -for non-linear models options \code{"ekf"} (extended Kalman particle filter) -is also available.} - -\item{seed}{Seed for RNG.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation. Used \eqn{\psi}-APF.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation. Used \eqn{\psi}-APF.} - -\item{iekf_iter}{If zero (default), first approximation for non-linear -Gaussian models is obtained from extended Kalman filter. If -\code{iekf_iter > 0}, iterated extended Kalman filter is used with -\code{iekf_iter} iterations.} - -\item{L}{Integer defining the discretization level.} -} -\value{ -List with samples (\code{alpha}) from the smoothing distribution and corresponding weights (\code{weights}), - as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) of the states and - estimated log-likelihood (\code{logLik}). -} -\description{ -Function \code{particle_smoother} performs particle smoothing -based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary particle filter (\eqn{\psi}-APF) [2], -or extended Kalman particle filter [3] (or its iterated version [4]). -The smoothing phase is based on the filter-smoother algorithm by [5]. -} -\details{ -See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. -} -\examples{ -set.seed(1) -x <- cumsum(rnorm(100)) -y <- rnorm(100, x) -model <- ssm_ulg(y, Z = 1, T = 1, R = 1, H = 1, P1 = 1) -system.time(out <- particle_smoother(model, particles = 1000)) -# same with simulation smoother: -system.time(out2 <- sim_smoother(model, particles = 1000, use_antithetic = TRUE)) -ts.plot(out$alphahat, rowMeans(out2), col = 1:2) - -} -\references{ -[1] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113. -[2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -[3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). The unscented particle filter. -In Advances in neural information processing systems (pp. 584-590). -[4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. Academic Press. -[5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. -Journal of Computational and Graphical Statistics, 5, 1–25. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/particle_smoother.R +\name{particle_smoother} +\alias{particle_smoother} +\alias{particle_smoother.gaussian} +\alias{particle_smoother.nongaussian} +\alias{particle_smoother.ssm_nlg} +\alias{particle_smoother.ssm_sde} +\title{Particle Smoothing} +\usage{ +particle_smoother(model, particles, ...) + +\method{particle_smoother}{gaussian}( + model, + particles, + method = "psi", + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{particle_smoother}{nongaussian}( + model, + particles, + method = "psi", + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + ... +) + +\method{particle_smoother}{ssm_nlg}( + model, + particles, + method = "bsf", + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + ... +) + +\method{particle_smoother}{ssm_sde}( + model, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model.} + +\item{particles}{Number of samples for particle filter.} + +\item{...}{Ignored.} + +\item{method}{Choice of particle filter algorithm. +For Gaussian and non-Gaussian models with linear dynamics, +options are \code{"bsf"} (bootstrap particle filter, default for +non-linear models) +and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and +for non-linear models options \code{"ekf"} (extended Kalman particle filter) +is also available.} + +\item{seed}{Seed for RNG.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation. +Used \eqn{\psi}-APF.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation. +Used \eqn{\psi}-APF.} + +\item{iekf_iter}{If zero (default), first approximation for non-linear +Gaussian models is obtained from extended Kalman filter. If +\code{iekf_iter > 0}, iterated extended Kalman filter is used with +\code{iekf_iter} iterations.} + +\item{L}{Integer defining the discretization level.} +} +\value{ +List with samples (\code{alpha}) from the smoothing distribution +and corresponding weights (\code{weights}), + as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) + of the states and + estimated log-likelihood (\code{logLik}). +} +\description{ +Function \code{particle_smoother} performs particle smoothing +based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary +particle filter (\eqn{\psi}-APF) [2], +or extended Kalman particle filter [3] (or its iterated version [4]). +The smoothing phase is based on the filter-smoother algorithm by [5]. +} +\details{ +See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. +} +\examples{ +set.seed(1) +x <- cumsum(rnorm(100)) +y <- rnorm(100, x) +model <- ssm_ulg(y, Z = 1, T = 1, R = 1, H = 1, P1 = 1) +system.time(out <- particle_smoother(model, particles = 1000)) +# same with simulation smoother: +system.time(out2 <- sim_smoother(model, particles = 1000, + use_antithetic = TRUE)) +ts.plot(out$alphahat, rowMeans(out2), col = 1:2) + +} +\references{ +[1] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +IEE Proceedings-F, 140, 107–113. + +[2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 + +[3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). +The unscented particle filter. +In Advances in neural information processing systems (pp. 584-590). + +[4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. +Academic Press. + +[5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian +nonlinear state space models. +Journal of Computational and Graphical Statistics, 5, 1–25. +} diff --git a/man/post_correct.Rd b/man/post_correct.Rd index fbf64414..3b76f6d5 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -16,31 +16,37 @@ post_correct( \arguments{ \item{model}{Model of class \code{nongaussian} or \code{ssm_nlg}.} -\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP estimate of theta. -While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., -it is also possible to input previous (asymptotically) exact output.} +\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP +estimate of theta. +While the intended use assumes this is from approximate MCMC, it is not +actually checked, i.e., it is also possible to input previous +(asymptotically) exact output.} \item{particles}{Number of particles for \eqn{\psi}-APF.} \item{threads}{Number of parallel threads.} \item{is_type}{Type of IS-correction. Possible choices are -\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), \code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of particles used for -weight computations is proportional to the length of the jump chain block.} +\code{"is1"} for importance sampling type weighting where the number of +particles used forweight computations is proportional to the length of the +jump chain block.} \item{seed}{Seed for the random number generator.} } \value{ -List with suggested number of particles \code{N} and matrix containing -estimated standard deviations of the log-weights and corresponding number of particles. +List with suggested number of particles \code{N} and matrix +containing estimated standard deviations of the log-weights and +corresponding number of particles. } \description{ -Function \code{post_correct} updates previously obtained approximate MCMC output -with post-correction weights leading to asymptotically exact weighted posterior, -and returns updated MCMC output where components \code{weights}, \code{posterior}, -\code{alpha}, \code{alphahat}, and \code{Vt} are updated (depending on the original output type). +Function \code{post_correct} updates previously obtained approximate MCMC +output with post-correction weights leading to asymptotically exact +weighted posterior, and returns updated MCMC output where components + \code{weights}, \code{posterior}, \code{alpha}, \code{alphahat}, and + \code{Vt} are updated (depending on the original output type). } \examples{ \dontrun{ @@ -115,8 +121,11 @@ ggplot(aes(time, mean, colour = method)) + } \references{ A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, -Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, -Biometrika, Volume 102, Issue 2, 2015, Pages 295–313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +Efficient implementation of Markov chain Monte Carlo when using an unbiased +likelihood estimator. Biometrika, 102, 2, 2015, Pages 295–313, +https://doi.org/10.1093/biomet/asu075 + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 7aeb9fa7..3b2e83ff 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -21,17 +21,19 @@ \item{model}{Model for future observations. Should have same structure as the original model which was used in MCMC, -in order to plug the posterior samples of the model parameters to the right places. -It is also possible to input the original model, which can be useful for example for -posterior predictive checks. In this case, set argument \code{future} to \code{FALSE}.} +in order to plug the posterior samples of the model parameters to the right +places. +It is also possible to input the original model, which can be useful for +example for posterior predictive checks. In this case, set argument +\code{future} to \code{FALSE}.} \item{type}{Return predictions on \code{"mean"} \code{"response"}, or \code{"state"} level.} \item{nsim}{Number of samples to draw.} -\item{future}{Default is \code{TRUE}, in which case predictions are for the future, -using posterior samples of (theta, alpha_T+1) i.e. the +\item{future}{Default is \code{TRUE}, in which case predictions are for the +future, using posterior samples of (theta, alpha_T+1) i.e. the posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} @@ -43,10 +45,11 @@ Otherwise it is assumed that \code{model} corresponds to the original model.} Data frame of predicted samples. } \description{ -Draw samples from the posterior predictive distribution for future time points -given the posterior draws of hyperparameters \eqn{\theta} and \eqn{alpha_{n+1}}. -Function can also be used to draw samples from the posterior predictive distribution -\eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. +Draw samples from the posterior predictive distribution for future +time points given the posterior draws of hyperparameters \eqn{\theta} and +\eqn{alpha_{n+1}}. Function can also be used to draw samples from the +posterior predictive distribution + \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. } \examples{ require("graphics") diff --git a/man/priors.Rd b/man/priors.Rd index b4a42999..3a5d66c3 100644 --- a/man/priors.Rd +++ b/man/priors.Rd @@ -1,53 +1,56 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/priors.R -\name{uniform} -\alias{uniform} -\alias{halfnormal} -\alias{normal} -\alias{tnormal} -\alias{gamma} -\title{Prior objects for bssm models} -\usage{ -uniform(init, min, max) - -halfnormal(init, sd) - -normal(init, mean, sd) - -tnormal(init, mean, sd, min = -Inf, max = Inf) - -gamma(init, shape, rate) -} -\arguments{ -\item{init}{Initial value for the parameter, used in initializing the model components and as a starting value -in MCMC.} - -\item{min}{Lower bound of the uniform and truncated normal prior.} - -\item{max}{Upper bound of the uniform and truncated normal prior.} - -\item{sd}{Standard deviation of the (underlying i.e. non-truncated) Normal distribution.} - -\item{mean}{Mean of the Normal prior.} - -\item{shape}{Shape parameter of the Gamma prior.} - -\item{rate}{Rate parameter of the Gamma prior.} -} -\value{ -object of class \code{bssm_prior}. -} -\description{ -These simple objects of class \code{bssm_prior} are used to construct a prior distributions for the -MCMC runs of \code{bssm} package. Currently supported priors are uniform (\code{uniform()}), -half-normal (\code{halfnormal()}), normal (\code{normal()}), gamma (\code{gamma}), and -truncated normal distribution (\code{tnormal()}).All parameters are vectorized so -for regression coefficient vector beta you can define prior for example -as \code{normal(0, 0, c(10, 20))}. -} -\examples{ -# create uniform prior on [-1, 1] for one parameter with initial value 0.2: -uniform(0.2, -1, 1) -# two normal priors at once i.e. for coefficients beta: -normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/priors.R +\name{uniform} +\alias{uniform} +\alias{halfnormal} +\alias{normal} +\alias{tnormal} +\alias{gamma} +\title{Prior objects for bssm models} +\usage{ +uniform(init, min, max) + +halfnormal(init, sd) + +normal(init, mean, sd) + +tnormal(init, mean, sd, min = -Inf, max = Inf) + +gamma(init, shape, rate) +} +\arguments{ +\item{init}{Initial value for the parameter, used in initializing the model +components and as a starting values in MCMC.} + +\item{min}{Lower bound of the uniform and truncated normal prior.} + +\item{max}{Upper bound of the uniform and truncated normal prior.} + +\item{sd}{Standard deviation of the (underlying i.e. non-truncated) +Normal distribution.} + +\item{mean}{Mean of the Normal prior.} + +\item{shape}{Shape parameter of the Gamma prior.} + +\item{rate}{Rate parameter of the Gamma prior.} +} +\value{ +object of class \code{bssm_prior}. +} +\description{ +These simple objects of class \code{bssm_prior} are used to construct a +prior distributions for the +MCMC runs of \code{bssm} package. Currently supported priors are uniform +(\code{uniform()}), half-normal (\code{halfnormal()}), +normal (\code{normal()}), gamma (\code{gamma}), and +truncated normal distribution (\code{tnormal()}). All parameters are +vectorized so for regression coefficient vector beta you can define prior +for example as \code{normal(0, 0, c(10, 20))}. +} +\examples{ +# create uniform prior on [-1, 1] for one parameter with initial value 0.2: +uniform(0.2, -1, 1) +# two normal priors at once i.e. for coefficients beta: +normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) +} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 27735894..e692b9e7 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -1,27 +1,28 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc} -\alias{run_mcmc} -\title{Bayesian Inference of State Space Models} -\usage{ -run_mcmc(model, iter, ...) -} -\arguments{ -\item{model}{State space model model of \code{bssm} package.} - -\item{iter}{Number of MCMC iterations.} - -\item{...}{Parameters to specific methods. See \code{\link{run_mcmc.gaussian}}, -\code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, -and \code{\link{run_mcmc.ssm_sde}} for details.} -} -\description{ -Adaptive Markov chain Monte Carlo simulation of state space models using -Robust Adaptive Metropolis algorithm by Vihola (2012). -See specific methods for various model types for details. -} -\references{ -Matti Vihola (2012). "Robust adaptive Metropolis algorithm with -coerced acceptance rate". Statistics and Computing, Volume 22, Issue 5, -pages 997--1008. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc} +\alias{run_mcmc} +\title{Bayesian Inference of State Space Models} +\usage{ +run_mcmc(model, iter, ...) +} +\arguments{ +\item{model}{State space model model of \code{bssm} package.} + +\item{iter}{Number of MCMC iterations.} + +\item{...}{Parameters to specific methods. See +\code{\link{run_mcmc.gaussian}}, +\code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, +and \code{\link{run_mcmc.ssm_sde}} for details.} +} +\description{ +Adaptive Markov chain Monte Carlo simulation of state space models using +Robust Adaptive Metropolis algorithm by Vihola (2012). +See specific methods for various model types for details. +} +\references{ +Matti Vihola (2012). "Robust adaptive Metropolis algorithm with +coerced acceptance rate". Statistics and Computing, Volume 22, Issue 5, +pages 997--1008. +} diff --git a/man/run_mcmc.ssm_nlg.Rd b/man/run_mcmc.ssm_nlg.Rd index d009d3af..4e7c9a61 100644 --- a/man/run_mcmc.ssm_nlg.Rd +++ b/man/run_mcmc.ssm_nlg.Rd @@ -1,102 +1,110 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_nlg} -\alias{run_mcmc.ssm_nlg} -\title{Bayesian Inference of non-linear state space models} -\usage{ -\method{run_mcmc}{ssm_nlg}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "bsf", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and hyperparameters theta), -\code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -\code{"approx"} for approximate inference based on the Gaussian approximation of the model, -\code{"ekf"} for approximate inference using extended Kalman filter, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"bsf"} (default), bootstrap filter is used for state sampling. -If \code{"ekf"}, particle filter based on EKF-proposals are used. -If \code{"psi"}, \eqn{\psi}-APF is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the computations -of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of bsm_ng models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is used with -\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.ssm_nlg} +\alias{run_mcmc.ssm_nlg} +\title{Bayesian Inference of non-linear state space models} +\usage{ +\method{run_mcmc}{ssm_nlg}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "bsf", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration. +Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, +\code{"approx"} for approximate inference based on the Gaussian +approximation of the model, +\code{"ekf"} for approximate inference using extended Kalman filter, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of +particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{sampling_method}{If \code{"bsf"} (default), bootstrap filter is used +for state sampling. +If \code{"ekf"}, particle filter based on EKF-proposals are used. +If \code{"psi"}, \eqn{\psi}-APF is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger +value can also be statistically more effective. +Note: With \code{output_type = "summary"}, the thinning does not affect the +computations of the summary statistics in case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of +bsm_ng models) the sampling +is done for transformed parameters with internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin +period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation.} + +\item{seed}{Seed for the random number generator.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations in place of standard EKF. +Defaults to zero.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc.ssm_sde.Rd b/man/run_mcmc.ssm_sde.Rd index 96ca4548..92ea6083 100644 --- a/man/run_mcmc.ssm_sde.Rd +++ b/man/run_mcmc.ssm_sde.Rd @@ -1,90 +1,95 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_sde} -\alias{run_mcmc.ssm_sde} -\title{Bayesian Inference of SDE} -\usage{ -\method{run_mcmc}{ssm_sde}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - L_c, - L_f, - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and hyperparameters theta), -\code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}. -If \code{particles = 0}, this is argument ignored and set to \code{"theta"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{L_c, L_f}{Integer values defining the discretization levels for first and second stages (defined as 2^L). -For PM methods, maximum of these is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the computations -of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of bsm_ng models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.ssm_sde} +\alias{run_mcmc.ssm_sde} +\title{Bayesian Inference of SDE} +\usage{ +\method{run_mcmc}{ssm_sde}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + L_c, + L_f, + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of states alpha and hyperparameters +theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}. +If \code{particles = 0}, this is argument ignored and set to \code{"theta"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of + particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{L_c, L_f}{Integer values defining the discretization levels for first +and second stages (defined as 2^L). For PM methods, maximum of these is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger +value can also be statistically more effective. +Note: With \code{output_type = "summary"}, the thinning does not affect the +computations of the summary statistics in case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., +the total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of +bsm_ng models) the sampling is done for transformed parameters with +internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin +period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc_g.Rd b/man/run_mcmc_g.Rd index c9ef2fbb..dda7283d 100644 --- a/man/run_mcmc_g.Rd +++ b/man/run_mcmc_g.Rd @@ -1,88 +1,95 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.gaussian} -\alias{run_mcmc.gaussian} -\title{Bayesian Inference of Linear-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{gaussian}( - model, - iter, - output_type = "full", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{output_type}{Type of output. Default is \code{"full"}, which returns -samples from the posterior \eqn{p(\alpha, \theta)}. Option \code{"summary"} does not simulate -states directly but computes the posterior means and variances of states using -fast Kalman smoothing. This is slightly faster, more memory efficient and -more accurate than calculations based on simulation smoother. Using option \code{"theta"} will only -return samples from the marginal posterior of the hyperparameters \eqn{\theta}.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of \code{bssm} - used adaptive MCMC during the burn-in period in order to find good proposal.} - -\item{thin}{Thinning rate. All MCMC algorithms in \code{bssm} use the jump chain -representation, and the thinning is applied to these blocks. -Defaults to 1.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of bsm_lg models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation. The default is 1.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Bayesian Inference of Linear-Gaussian State Space Models -} -\examples{ -model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(500, 500, 500), - sd_y = halfnormal(1, 10)) - -mcmc_results <- run_mcmc(model, iter = 2e4) -summary(mcmc_results, return_se = TRUE) - -require("dplyr") -sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% - group_by(time) \%>\% - summarise(mean = mean(value), - lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) -require("ggplot2") -sumr \%>\% ggplot(aes(time, mean)) + - geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + - geom_line() + theme_bw() + - geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), - col = 2) -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.gaussian} +\alias{run_mcmc.gaussian} +\title{Bayesian Inference of Linear-Gaussian State Space Models} +\usage{ +\method{run_mcmc}{gaussian}( + model, + iter, + output_type = "full", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{output_type}{Type of output. Default is \code{"full"}, which returns +samples from the posterior \eqn{p(\alpha, \theta)}. +Option \code{"summary"} does not simulate +states directly but computes the posterior means and variances of states +using fast Kalman smoothing. This is slightly faster, +more memory efficient and more accurate than calculations based on +simulation smoother. Using option \code{"theta"} will only +return samples from the marginal posterior of the hyperparameters +\eqn{\theta}.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of +\code{bssm} used adaptive MCMC during the burn-in period in order to find +good proposal.} + +\item{thin}{Thinning rate. All MCMC algorithms in \code{bssm} use the +jump chain representation, and the thinning is applied to these blocks. +Defaults to 1.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of bsm_lg +models) the sampling is done for transformed parameters with +internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the +burnin period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation. The default is 1.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Bayesian Inference of Linear-Gaussian State Space Models +} +\examples{ +model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(500, 500, 500), + sd_y = halfnormal(1, 10)) + +mcmc_results <- run_mcmc(model, iter = 2e4) +summary(mcmc_results, return_se = TRUE) + +require("dplyr") +sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% + group_by(time) \%>\% + summarise(mean = mean(value), + lwr = quantile(value, 0.025), + upr = quantile(value, 0.975)) +require("ggplot2") +sumr \%>\% ggplot(aes(time, mean)) + + geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + + geom_line() + theme_bw() + + geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), + col = 2) +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd index 0a87fd14..f388d96a 100644 --- a/man/run_mcmc_ng.Rd +++ b/man/run_mcmc_ng.Rd @@ -1,211 +1,219 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.nongaussian} -\alias{run_mcmc.nongaussian} -\title{Bayesian Inference of Non-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{nongaussian}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "psi", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - local_approx = TRUE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and hyperparameters theta), -\code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of PMCMC , -\code{"approx"} for approximate inference based on the Gaussian approximation of the model, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"psi"}, \eqn{\psi}-APF is used for state sampling -(default). If \code{"spdk"}, non-sequential importance sampling based -on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the computations -of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the total acceptance -rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of bsm_ng models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} - -\item{local_approx}{If \code{TRUE} (default), Gaussian approximation needed for -importance sampling is performed at each iteration. If \code{FALSE}, approximation is updated only -once at the start of the MCMC using the initial model.} - -\item{threads}{Number of threads for state simulation. The default is 1. -Note that parallel computing is only used in the post-correction phase of IS-MCMC -and when sampling the states in case of approximate models.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\examples{ -set.seed(1) -n <- 50 -slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) -level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) -y <- rpois(n, exp(level)) -poisson_model <- bsm_ng(y, - sd_level = halfnormal(0.01, 1), - sd_slope = halfnormal(0.01, 0.1), - P1 = diag(c(10, 0.1)), distribution = "poisson") - -# Note small number of iterations for CRAN checks -mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, - mcmc_type = "da") -summary(mcmc_out, what = "theta", return_se = TRUE) - -set.seed(123) -n <- 50 -sd_level <- 0.1 -drift <- 0.01 -beta <- -0.9 -phi <- 5 - -level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) -x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) -y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) - -model <- bsm_ng(y, xreg = x, - beta = normal(0, 0, 10), - phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), - sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), - distribution = "negative binomial") - -# run IS-MCMC -# Note small number of iterations for CRAN checks -fit <- run_mcmc(model, iter = 5000, - particles = 10, mcmc_type = "is2", seed = 1) - -# extract states -d_states <- as.data.frame(fit, variable = "states", time = 1:n) - -library("dplyr") -library("ggplot2") - - # compute summary statistics -level_sumr <- d_states \%>\% - filter(variable == "level") \%>\% - group_by(time) \%>\% - summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), - lwr = Hmisc::wtd.quantile(value, weight, - 0.025, normwt = TRUE), - upr = Hmisc::wtd.quantile(value, weight, - 0.975, normwt = TRUE)) - -# visualize -level_sumr \%>\% ggplot(aes(x = time, y = mean)) + - geom_line() + - geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + - geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + - theme_bw() + - theme(legend.title = element_blank()) + - xlab("Time") + ylab("Level") - -# theta -d_theta <- as.data.frame(fit, variable = "theta") -ggplot(d_theta, aes(x = value)) + - geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + - facet_wrap(~ variable, scales = "free") + - theme_bw() - - -# Bivariate Poisson model: - -set.seed(1) -x <- cumsum(c(3, rnorm(19, sd = 0.5))) -y <- cbind( - rpois(20, exp(x)), - rpois(20, exp(x))) - -prior_fn <- function(theta) { - # half-normal prior using transformation - dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term -} - -update_fn <- function(theta) { - list(R = array(exp(theta), c(1, 1, 1))) -} - -model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, - R = 0.1, P1 = 1, distribution = "poisson", - init_theta = log(0.1), - prior_fn = prior_fn, update_fn = update_fn) - -# Note small number of iterations for CRAN checks -out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") - -sumr <- as.data.frame(out, variable = "states") \%>\% - group_by(time) \%>\% mutate(value = exp(value)) \%>\% - summarise(mean = mean(value), - ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) -ggplot(sumr, aes(time, mean)) + -geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + -geom_line() + -geom_line(data = data.frame(mean = y[, 1], time = 1:20), colour = "tomato") + -geom_line(data = data.frame(mean = y[, 2], time = 1:20), colour = "tomato") + -theme_bw() - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.nongaussian} +\alias{run_mcmc.nongaussian} +\title{Bayesian Inference of Non-Gaussian State Space Models} +\usage{ +\method{run_mcmc}{nongaussian}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "psi", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + local_approx = TRUE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration. +Ignored if \code{mcmc_type} is \code{"approx"}.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of PMCMC , +\code{"approx"} for approximate inference based on the Gaussian +approximation of the model, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of +particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{sampling_method}{If \code{"psi"}, \eqn{\psi}-APF is used for state +sampling (default). If \code{"spdk"}, non-sequential importance sampling + based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter +is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger value can also be +statistically more effective. Note: With \code{output_type = "summary"}, +the thinning does not affect the computations of the summary statistics in +case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of +bsm_ng models) the sampling is done for transformed parameters with +internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the +burnin period. Default is \code{FALSE}.} + +\item{local_approx}{If \code{TRUE} (default), Gaussian approximation +needed for importance sampling is performed at each iteration. +If \code{FALSE}, approximation is updated only once at the start of the +MCMC using the initial model.} + +\item{threads}{Number of threads for state simulation. The default is 1. +Note that parallel computing is only used in the post-correction phase of + IS-MCMC and when sampling the states in case of approximate models.} + +\item{seed}{Seed for the random number generator.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\examples{ +set.seed(1) +n <- 50 +slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) +level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) +y <- rpois(n, exp(level)) +poisson_model <- bsm_ng(y, + sd_level = halfnormal(0.01, 1), + sd_slope = halfnormal(0.01, 0.1), + P1 = diag(c(10, 0.1)), distribution = "poisson") + +# Note small number of iterations for CRAN checks +mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, + mcmc_type = "da") +summary(mcmc_out, what = "theta", return_se = TRUE) + +set.seed(123) +n <- 50 +sd_level <- 0.1 +drift <- 0.01 +beta <- -0.9 +phi <- 5 + +level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) +x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) +y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) + +model <- bsm_ng(y, xreg = x, + beta = normal(0, 0, 10), + phi = halfnormal(1, 10), + sd_level = halfnormal(0.1, 1), + sd_slope = halfnormal(0.01, 0.1), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + distribution = "negative binomial") + +# run IS-MCMC +# Note small number of iterations for CRAN checks +fit <- run_mcmc(model, iter = 5000, + particles = 10, mcmc_type = "is2", seed = 1) + +# extract states +d_states <- as.data.frame(fit, variable = "states", time = 1:n) + +library("dplyr") +library("ggplot2") + + # compute summary statistics +level_sumr <- d_states \%>\% + filter(variable == "level") \%>\% + group_by(time) \%>\% + summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), + lwr = Hmisc::wtd.quantile(value, weight, + 0.025, normwt = TRUE), + upr = Hmisc::wtd.quantile(value, weight, + 0.975, normwt = TRUE)) + +# visualize +level_sumr \%>\% ggplot(aes(x = time, y = mean)) + + geom_line() + + geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + + geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + + theme_bw() + + theme(legend.title = element_blank()) + + xlab("Time") + ylab("Level") + +# theta +d_theta <- as.data.frame(fit, variable = "theta") +ggplot(d_theta, aes(x = value)) + + geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + + facet_wrap(~ variable, scales = "free") + + theme_bw() + + +# Bivariate Poisson model: + +set.seed(1) +x <- cumsum(c(3, rnorm(19, sd = 0.5))) +y <- cbind( + rpois(20, exp(x)), + rpois(20, exp(x))) + +prior_fn <- function(theta) { + # half-normal prior using transformation + dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term +} + +update_fn <- function(theta) { + list(R = array(exp(theta), c(1, 1, 1))) +} + +model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, + R = 0.1, P1 = 1, distribution = "poisson", + init_theta = log(0.1), + prior_fn = prior_fn, update_fn = update_fn) + +# Note small number of iterations for CRAN checks +out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") + +sumr <- as.data.frame(out, variable = "states") \%>\% + group_by(time) \%>\% mutate(value = exp(value)) \%>\% + summarise(mean = mean(value), + ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) +ggplot(sumr, aes(time, mean)) + +geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + +geom_line() + +geom_line(data = data.frame(mean = y[, 1], time = 1:20), + colour = "tomato") + +geom_line(data = data.frame(mean = y[, 2], time = 1:20), + colour = "tomato") + +theme_bw() + +} diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 6a15543e..8f240d84 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -1,54 +1,56 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sim_smoother.R -\name{sim_smoother} -\alias{sim_smoother} -\alias{sim_smoother.gaussian} -\alias{sim_smoother.nongaussian} -\title{Simulation Smoothing} -\usage{ -sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) - -\method{sim_smoother}{gaussian}( - model, - nsim = 1, - seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, - ... -) - -\method{sim_smoother}{nongaussian}( - model, - nsim = 1, - seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, - ... -) -} -\arguments{ -\item{model}{Model object.} - -\item{nsim}{Number of independent samples.} - -\item{seed}{Seed for the random number generator.} - -\item{use_antithetic}{Use an antithetic variable for location. -Default is \code{FALSE}. Ignored for multivariate models.} - -\item{...}{Ignored.} -} -\value{ -An array containing the generated samples. -} -\description{ -Function \code{sim_smoother} performs simulation smoothing i.e. simulates the states -from the conditional distribution \eqn{p(\alpha | y, \theta)} for linear-Gaussian models. -} -\details{ -For non-Gaussian/non-linear models, the simulation is based on the approximating -Gaussian model. -} -\examples{ -model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), sd_y = uniform(1,0,5)) -sim <- sim_smoother(model, 12) -ts.plot(sim[, 1, ]) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sim_smoother.R +\name{sim_smoother} +\alias{sim_smoother} +\alias{sim_smoother.gaussian} +\alias{sim_smoother.nongaussian} +\title{Simulation Smoothing} +\usage{ +sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) + +\method{sim_smoother}{gaussian}( + model, + nsim = 1, + seed = sample(.Machine$integer.max, size = 1), + use_antithetic = FALSE, + ... +) + +\method{sim_smoother}{nongaussian}( + model, + nsim = 1, + seed = sample(.Machine$integer.max, size = 1), + use_antithetic = FALSE, + ... +) +} +\arguments{ +\item{model}{Model object.} + +\item{nsim}{Number of independent samples.} + +\item{seed}{Seed for the random number generator.} + +\item{use_antithetic}{Use an antithetic variable for location. +Default is \code{FALSE}. Ignored for multivariate models.} + +\item{...}{Ignored.} +} +\value{ +An array containing the generated samples. +} +\description{ +Function \code{sim_smoother} performs simulation smoothing i.e. simulates +the states from the conditional distribution \eqn{p(\alpha | y, \theta)} +for linear-Gaussian models. +} +\details{ +For non-Gaussian/non-linear models, the simulation is based on the +approximating Gaussian model. +} +\examples{ +model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), + sd_y = uniform(1,0,5)) +sim <- sim_smoother(model, 12) +ts.plot(sim[, 1, ]) +} diff --git a/man/smoother.Rd b/man/smoother.Rd index 92185cd2..b0a4c554 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -1,28 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smoother.R -\name{fast_smoother} -\alias{fast_smoother} -\alias{smoother} -\title{Kalman Smoothing} -\usage{ -fast_smoother(model, ...) - -smoother(model, ...) -} -\arguments{ -\item{model}{Model model.} - -\item{...}{Ignored.} -} -\value{ -Matrix containing the smoothed estimates of states, or a list -with the smoothed states and the variances. -} -\description{ -Methods for Kalman smoothing of the states. Function \code{fast_smoother} -computes only smoothed estimates of the states, and function -\code{smoother} computes also smoothed variances. -} -\details{ -For non-Gaussian models, the smoothing is based on the approximate Gaussian model. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smoother.R +\name{fast_smoother} +\alias{fast_smoother} +\alias{smoother} +\title{Kalman Smoothing} +\usage{ +fast_smoother(model, ...) + +smoother(model, ...) +} +\arguments{ +\item{model}{Model model.} + +\item{...}{Ignored.} +} +\value{ +Matrix containing the smoothed estimates of states, or a list +with the smoothed states and the variances. +} +\description{ +Methods for Kalman smoothing of the states. Function \code{fast_smoother} +computes only smoothed estimates of the states, and function +\code{smoother} computes also smoothed variances. +} +\details{ +For non-Gaussian models, the smoothing is based on the approximate Gaussian +model. +} diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index 29599474..9b0af57f 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -1,93 +1,102 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_mlg} -\alias{ssm_mlg} -\title{General multivariate linear Gaussian state space models} -\usage{ -ssm_mlg( - y, - Z, - H, - T, - R, - a1, - P1, - init_theta = numeric(0), - D, - C, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as multivariate time series or matrix with dimensions n x p.} - -\item{Z}{System matrix Z of the observation equation as p x m matrix or p x m x n array.} - -\item{H}{Lower triangular matrix H of the observation. Either a scalar or a vector of length n.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a -m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as a p x n matrix.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_mlg}. -} -\description{ -Construct an object of class \code{ssm_mlg} by directly defining the corresponding terms of -the model. -} -\details{ -The general multivariate linear-Gaussian model is defined using the following -observational and state equations: - -\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here p is the number of time series and k is the number of disturbance terms -(which can be less than m, the number of states). - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_mlg}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -} -\examples{ - -data("GlobalTemp", package = "KFAS") -model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), - R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) -ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_mlg} +\alias{ssm_mlg} +\title{General multivariate linear Gaussian state space models} +\usage{ +ssm_mlg( + y, + Z, + H, + T, + R, + a1 = NULL, + P1 = NULL, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as multivariate time series or matrix with +dimensions n x p.} + +\item{Z}{System matrix Z of the observation equation as p x m matrix or +p x m x n array.} + +\item{H}{Lower triangular matrix H of the observation. Either a scalar or +a vector of length n.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix +or a m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as a p x n matrix.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be +onstant wrt. theta.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_mlg}. +} +\description{ +Construct an object of class \code{ssm_mlg} by directly defining the +corresponding terms of the model. +} +\details{ +The general multivariate linear-Gaussian model is defined using the +following observational and state equations: + +\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. +Here p is the number of time series and k is the number of disturbance terms +(which can be less than m, the number of states). + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be +constant wrt. theta. +Note that while you can input say R as m x k matrix for \code{ssm_mlg}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +} +\examples{ + +data("GlobalTemp", package = "KFAS") +model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), + R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) +ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) + +} diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index ebd1fbeb..831a5f9c 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -1,91 +1,99 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_mng} -\alias{ssm_mng} -\title{General Non-Gaussian State Space Model} -\usage{ -ssm_mng( - y, - Z, - T, - R, - a1, - P1, - distribution, - phi = 1, - u = 1, - init_theta = numeric(0), - D, - C, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as multivariate time series or matrix with dimensions n x p.} - -\item{Z}{System matrix Z of the observation equation as p x m matrix or p x m x n array.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a -m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{distribution}{vector of distributions of the observed series. Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, \code{"gamma"}, -and \code{"gaussian"}.} - -\item{phi}{Additional parameters relating to the non-Gaussian distributions. -For negative binomial distribution this is the dispersion term, for gamma distribution -this is the shape parameter, for Gaussian this is standard deviation, -and for other distributions this is ignored.} - -\item{u}{Constant parameter for non-Gaussian models. For Poisson, gamma, -and negative binomial distribution, this corresponds to the offset term. -For binomial, this is the number of trials.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as p x n matrix.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and -\code{phi}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_mng}. -} -\description{ -Construct an object of class \code{ssm_mng} by directly defining the corresponding terms of -the model. -} -\details{ -The general multivariate non-Gaussian model is defined using the following -observational and state equations: - -\deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} - -where \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and \eqn{p^i(y_t | .)} -is either Poisson, binomial, gamma, Gaussian, or negative binomial distribution for -each observation series \eqn{i=1,...,p}.Here k is the number of disturbance terms -(which can be less than m, the number of states). -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_mng} +\alias{ssm_mng} +\title{General Non-Gaussian State Space Model} +\usage{ +ssm_mng( + y, + Z, + T, + R, + a1 = NULL, + P1 = NULL, + distribution, + phi = 1, + u = 1, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as multivariate time series or matrix with dimensions +n x p.} + +\item{Z}{System matrix Z of the observation equation as p x m matrix or +p x m x n array.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a m x k +matrix or a +m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{distribution}{vector of distributions of the observed series. +Possible choices are +\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, +\code{"gamma"}, and \code{"gaussian"}.} + +\item{phi}{Additional parameters relating to the non-Gaussian distributions. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, for Gaussian this is +standard deviation, +and for other distributions this is ignored.} + +\item{u}{Constant parameter for non-Gaussian models. For Poisson, gamma, +and negative binomial distribution, this corresponds to the offset term. +For binomial, this is the number of trials.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as p x n matrix.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and +\code{phi}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant wrt. +theta.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_mng}. +} +\description{ +Construct an object of class \code{ssm_mng} by directly defining the +corresponding terms of the model. +} +\details{ +The general multivariate non-Gaussian model is defined using the following +observational and state equations: + +\deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and +\eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or +negative binomial distribution for each observation series \eqn{i=1,...,p}. +Here k is the number of disturbance terms (which can be less than m, +the number of states). +} diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index d0681cfc..44ea1195 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -1,79 +1,86 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_nlg} -\alias{ssm_nlg} -\title{General multivariate nonlinear Gaussian state space models} -\usage{ -ssm_nlg( - y, - Z, - H, - T, - R, - Z_gn, - T_gn, - a1, - P1, - theta, - known_params = NA, - known_tv_params = matrix(NA), - n_states, - n_etas, - log_prior_pdf, - time_varying = rep(TRUE, 4), - state_names = paste0("state", 1:n_states) -) -} -\arguments{ -\item{y}{Observations as multivariate time series (or matrix) of length \eqn{n}.} - -\item{Z, H, T, R}{An external pointers for the C++ functions which -define the corresponding model functions.} - -\item{Z_gn, T_gn}{An external pointers for the C++ functions which -define the gradients of the corresponding model functions.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{theta}{Parameter vector passed to all model functions.} - -\item{known_params}{Vector of known parameters passed to all model functions.} - -\item{known_tv_params}{Matrix of known parameters passed to all model functions.} - -\item{n_states}{Number of states in the model.} - -\item{n_etas}{Dimension of the noise term of the transition equation.} - -\item{log_prior_pdf}{An external pointer for the C++ function which -computes the log-prior density given theta.} - -\item{time_varying}{Optional logical vector of length 4, denoting whether the values of -Z, H, T, and R vary with respect to time variable (given identical states). -If used, this can speed up some computations.} - -\item{state_names}{Names for the states.} -} -\value{ -Object of class \code{ssm_nlg}. -} -\description{ -Constructs an object of class \code{ssm_nlg} by defining the corresponding terms -of the observation and state equation. -} -\details{ -The nonlinear Gaussian model is defined as - -\deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, (\textrm{observation equation})} -\deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, (\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions -\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector \eqn{\theta}. - -Compared to other models, these general models need a bit more effort from -the user, as you must provide the several small C++ snippets which define the -model structure. See examples in the vignette. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_nlg} +\alias{ssm_nlg} +\title{General multivariate nonlinear Gaussian state space models} +\usage{ +ssm_nlg( + y, + Z, + H, + T, + R, + Z_gn, + T_gn, + a1, + P1, + theta, + known_params = NA, + known_tv_params = matrix(NA), + n_states, + n_etas, + log_prior_pdf, + time_varying = rep(TRUE, 4), + state_names = paste0("state", 1:n_states) +) +} +\arguments{ +\item{y}{Observations as multivariate time series (or matrix) of length +\eqn{n}.} + +\item{Z, H, T, R}{An external pointers for the C++ functions which +define the corresponding model functions.} + +\item{Z_gn, T_gn}{An external pointers for the C++ functions which +define the gradients of the corresponding model functions.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{theta}{Parameter vector passed to all model functions.} + +\item{known_params}{Vector of known parameters passed to all model +functions.} + +\item{known_tv_params}{Matrix of known parameters passed to all model +functions.} + +\item{n_states}{Number of states in the model.} + +\item{n_etas}{Dimension of the noise term of the transition equation.} + +\item{log_prior_pdf}{An external pointer for the C++ function which +computes the log-prior density given theta.} + +\item{time_varying}{Optional logical vector of length 4, denoting whether +the values of +Z, H, T, and R vary with respect to time variable (given identical states). +If used, this can speed up some computations.} + +\item{state_names}{Names for the states.} +} +\value{ +Object of class \code{ssm_nlg}. +} +\description{ +Constructs an object of class \code{ssm_nlg} by defining the corresponding +terms of the observation and state equation. +} +\details{ +The nonlinear Gaussian model is defined as + +\deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions +\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector +\eqn{\theta}. + +Compared to other models, these general models need a bit more effort from +the user, as you must provide the several small C++ snippets which define the +model structure. See examples in the vignette. +} diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 48dfc558..7c732f1a 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -1,86 +1,89 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_sde} -\alias{ssm_sde} -\title{Univariate state space model with continuous SDE dynamics} -\usage{ -ssm_sde( - y, - drift, - diffusion, - ddiffusion, - obs_pdf, - prior_pdf, - theta, - x0, - positive -) -} -\arguments{ -\item{y}{Observations as univariate time series (or vector) of length \eqn{n}.} - -\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions which -define the drift, diffusion and derivative of diffusion functions of SDE.} - -\item{obs_pdf}{An external pointer for the C++ function which -computes the observational log-density given the the states and parameter vector theta.} - -\item{prior_pdf}{An external pointer for the C++ function which -computes the prior log-density given the parameter vector theta.} - -\item{theta}{Parameter vector passed to all model functions.} - -\item{x0}{Fixed initial value for SDE at time 0.} - -\item{positive}{If \code{TRUE}, positivity constraint is -forced by \code{abs} in Milstein scheme.} -} -\value{ -Object of class \code{ssm_sde}. -} -\description{ -Constructs an object of class \code{ssm_sde} by defining the functions for -the drift, diffusion and derivative of diffusion terms of univariate SDE, -as well as the log-density of observation equation. We assume that the -observations are measured at integer times (missing values are allowed). -} -\details{ -As in case of \code{ssm_nlg} models, these general models need a bit more effort from -the user, as you must provide the several small C++ snippets which define the -model structure. See vignettes for an example. -} -\examples{ -\dontrun{ -library("sde") -set.seed(1) -# theta_0 = rho = 0.5 -# theta_1 = nu = 2 -# theta_2 = sigma = 0.3 -x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, - drift = expression(0.5 * (2 - x)), - sigma = expression(0.3), - sigma.x = expression(0)) -y <- rpois(50, exp(x[-1])) - -# Template can be found in the vignette -Rcpp::sourceCpp("ssm_sde_template.cpp") -pntrs <- create_xptrs() - -sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, - pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, - c(rho = 0.5, nu = 2, sigma = 0.3), 1, positive = FALSE) - -est <- particle_smoother(sde_model, L = 12, particles = 500) - -ts.plot(cbind(x, est$alphahat, - est$alphahat - 2*sqrt(c(est$Vt)), - est$alphahat + 2*sqrt(c(est$Vt))), - col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) - -# Takes time with finer mesh, parallelization with IS-MCMC helps a lot -out <- run_mcmc(sde_model, L_c = 4, L_f = 8, - particles = 50, iter = 2e4, - threads = 4L) - -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_sde} +\alias{ssm_sde} +\title{Univariate state space model with continuous SDE dynamics} +\usage{ +ssm_sde( + y, + drift, + diffusion, + ddiffusion, + obs_pdf, + prior_pdf, + theta, + x0, + positive +) +} +\arguments{ +\item{y}{Observations as univariate time series (or vector) of length +\eqn{n}.} + +\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions +which +define the drift, diffusion and derivative of diffusion functions of SDE.} + +\item{obs_pdf}{An external pointer for the C++ function which +computes the observational log-density given the the states and parameter +vector theta.} + +\item{prior_pdf}{An external pointer for the C++ function which +computes the prior log-density given the parameter vector theta.} + +\item{theta}{Parameter vector passed to all model functions.} + +\item{x0}{Fixed initial value for SDE at time 0.} + +\item{positive}{If \code{TRUE}, positivity constraint is +forced by \code{abs} in Milstein scheme.} +} +\value{ +Object of class \code{ssm_sde}. +} +\description{ +Constructs an object of class \code{ssm_sde} by defining the functions for +the drift, diffusion and derivative of diffusion terms of univariate SDE, +as well as the log-density of observation equation. We assume that the +observations are measured at integer times (missing values are allowed). +} +\details{ +As in case of \code{ssm_nlg} models, these general models need a bit more +effort from the user, as you must provide the several small C++ snippets +which define the model structure. See vignettes for an example. +} +\examples{ +\dontrun{ +library("sde") +set.seed(1) +# theta_0 = rho = 0.5 +# theta_1 = nu = 2 +# theta_2 = sigma = 0.3 +x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, + drift = expression(0.5 * (2 - x)), + sigma = expression(0.3), + sigma.x = expression(0)) +y <- rpois(50, exp(x[-1])) + +# Template can be found in the vignette +Rcpp::sourceCpp("ssm_sde_template.cpp") +pntrs <- create_xptrs() + +sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, + pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, + c(rho = 0.5, nu = 2, sigma = 0.3), 1, positive = FALSE) + +est <- particle_smoother(sde_model, L = 12, particles = 500) + +ts.plot(cbind(x, est$alphahat, + est$alphahat - 2*sqrt(c(est$Vt)), + est$alphahat + 2*sqrt(c(est$Vt))), + col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) + +# Takes time with finer mesh, parallelization with IS-MCMC helps a lot +out <- run_mcmc(sde_model, L_c = 4, L_f = 8, + particles = 50, iter = 2e4, + threads = 4L) + +} +} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 9fbf8689..caefa952 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -10,11 +10,11 @@ ssm_ulg( H, T, R, - a1, - P1, + a1 = NULL, + P1 = NULL, init_theta = numeric(0), - D, - C, + D = NULL, + C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn @@ -23,15 +23,17 @@ ssm_ulg( \arguments{ \item{y}{Observations as time series (or vector) of length \eqn{n}.} -\item{Z}{System matrix Z of the observation equation as m x 1 or m x n matrix.} +\item{Z}{System matrix Z of the observation equation as m x 1 or +m x n matrix.} -\item{H}{Vector of standard deviations. Either a scalar or a vector of length n.} +\item{H}{Vector of standard deviations. Either a scalar or a vector of +length n.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a m x m x n array.} -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a -m x k x n array.} +\item{R}{Lower triangular matrix R the state equation. Either a +m x k matrix or a m x k x n array.} \item{a1}{Prior mean for the initial state as a vector of length m.} @@ -39,7 +41,8 @@ m x k x n array.} \item{init_theta}{Initial values for the unknown hyperparameters theta.} -\item{D}{Intercept terms for observation equation, given as a length n vector.} +\item{D}{Intercept terms for observation equation, given as a +length n vector.} \item{C}{Intercept terms for state equation, given as m x n matrix.} @@ -69,17 +72,21 @@ observational and state equations: where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here k is the number of disturbance terms which can be less than m, the number of states. +Here k is the number of disturbance terms which can be less than m, the +number of states. The \code{update_fn} function should take only one vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta. +If any of these components is missing, it is assumed to be constant wrt. +theta. Note that while you can input say R as m x k matrix for \code{ssm_ulg}, \code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function and then check -the expected structure of the model components from the output. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from the +output. } \examples{ @@ -107,10 +114,10 @@ update_fn <- function(theta) { } # prior for standard deviations as half-normal(1) prior_fn <- function(theta) { - if(any(theta < 0)){ - log_p <- -Inf + if(any(theta < 0)) { + log_p <- -Inf } else { - log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) + log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) } log_p } @@ -144,7 +151,7 @@ model_bssm <- as_bssm(model_kfas, kappa = 100) # "manually" by constructing only necessary matrices, # i.e., in this case a list with H and Q) -updatefn <- function(theta){ +updatefn <- function(theta) { model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ SSMseasonal(period = 12, @@ -167,7 +174,8 @@ model_bssm <- as_bssm(model_kfas, kappa = 100, out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) out -# Above the regression coefficients are modelled as time-invariant latent states. +# Above the regression coefficients are modelled as +# time-invariant latent states. # Here is an alternative way where we use variable D so that the # coefficients are part of parameter vector theta: diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 96611a8c..5b7e552d 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -1,106 +1,116 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_ung} -\alias{ssm_ung} -\title{General univariate non-Gaussian state space model} -\usage{ -ssm_ung( - y, - Z, - T, - R, - a1, - P1, - distribution, - phi = 1, - u = 1, - init_theta = numeric(0), - D, - C, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as time series (or vector) of length \eqn{n}.} - -\item{Z}{System matrix Z of the observation equation. Either a vector of length m, -a m x n matrix, or object which can be coerced to such.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array, or object which can be coerced to such.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a -m x k x n array, or object which can be coerced to such.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{distribution}{Distribution of the observed time series. Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma distribution -this is the shape parameter, and for other distributions this is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, gamma, and -negative binomial distribution, this corresponds to the offset term. For binomial, -this is the number of trials.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a -scalar or vector of length n.} - -\item{C}{Intercept terms \eqn{C_t} for the state equation, given as a -m times 1 or m times n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_ung}. -} -\description{ -Construct an object of class \code{ssm_ung} by directly defining the corresponding terms of -the model. -} -\details{ -The general univariate non-Gaussian model is defined using the following -observational and state equations: - -\deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})} - -where \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, -and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or negative binomial distribution. -Here k is the number of disturbance terms which can be less than m, the number of states. - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_ung}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function and then check -the expected structure of the model components from the output. -} -\examples{ - -data("drownings", package = "bssm") -model <- ssm_ung(drownings[, "deaths"], Z = 1, T = 1, R = 0.2, - a1 = 0, P1 = 10, distribution = "poisson", u = drownings[, "population"]) - -# approximate results based on Gaussian approximation -out <- smoother(model) -ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_ung} +\alias{ssm_ung} +\title{General univariate non-Gaussian state space model} +\usage{ +ssm_ung( + y, + Z, + T, + R, + a1 = NULL, + P1 = NULL, + distribution, + phi = 1, + u = 1, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as time series (or vector) of length \eqn{n}.} + +\item{Z}{System matrix Z of the observation equation. Either a +vector of length m, +a m x n matrix, or object which can be coerced to such.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array, or object which can be coerced to such.} + +\item{R}{Lower triangular matrix R the state equation. Either +a m x k matrix or a m x k x n array, or object which can be coerced to such.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are +\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, and for other +distributions this is ignored.} + +\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a +scalar or vector of length n.} + +\item{C}{Intercept terms \eqn{C_t} for the state equation, given as a +m times 1 or m times n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. See details.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_ung}. +} +\description{ +Construct an object of class \code{ssm_ung} by directly defining the +corresponding terms of the model. +} +\details{ +The general univariate non-Gaussian model is defined using the following +observational and state equations: + +\deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, +and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or +negative binomial distribution. +Here k is the number of disturbance terms which can be less than m, +the number of states. + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, + and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant +wrt. theta. +Note that while you can input say R as m x k matrix for \code{ssm_ung}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from +the output. +} +\examples{ + +data("drownings", package = "bssm") +model <- ssm_ung(drownings[, "deaths"], Z = 1, T = 1, R = 0.2, + a1 = 0, P1 = 10, distribution = "poisson", u = drownings[, "population"]) + +# approximate results based on Gaussian approximation +out <- smoother(model) +ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) +} diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index f9a84f8f..57c9ce8e 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -15,33 +15,35 @@ suggest_N( \arguments{ \item{model}{Model of class \code{nongaussian} or \code{ssm_nlg}.} -\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP estimate of theta. -While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., +\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP +estimate of theta. While the intended use assumes this is from +approximate MCMC, it is not actually checked, i.e., it is also possible to input previous (asymptotically) exact output.} -\item{candidates}{Vector containing the candidate number of particles to test. Default -is \code{seq(10, 100, by = 10)}.} +\item{candidates}{Vector containing the candidate number of particles to +test. Default is \code{seq(10, 100, by = 10)}.} -\item{replications}{How many replications should be used for computing the standard deviations? -Default is 100.} +\item{replications}{How many replications should be used for computing +the standard deviations? Default is 100.} \item{seed}{Seed for the random number generator.} } \value{ -List with suggested number of particles \code{N} and matrix containing -estimated standard deviations of the log-weights and corresponding number of particles. +List with suggested number of particles \code{N} and matrix +containing estimated standard deviations of the log-weights and +corresponding number of particles. } \description{ -Function \code{estimate_N} estimates suitable number particles needed for accurate -post-correction of approximate MCMC +Function \code{estimate_N} estimates suitable number particles needed for +accurate post-correction of approximate MCMC. } \details{ Function \code{suggest_N} estimates the standard deviation of the logarithm of the post-correction weights at approximate MAP of theta, using various particle sizes and suggest smallest number of particles -which still leads standard deviation less than 1. Similar approach was suggested in -the context of pseudo-marginal MCMC by Doucet et al. (2015), but see also -Section 10.3 in Vihola et al (2020). +which still leads standard deviation less than 1. Similar approach was +suggested in the context of pseudo-marginal MCMC by Doucet et al. (2015), + but see also Section 10.3 in Vihola et al (2020). } \examples{ \dontrun{ @@ -78,8 +80,11 @@ estN$N } \references{ A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, -Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, -Biometrika, Volume 102, Issue 2, 2015, Pages 295–313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. +Efficient implementation of Markov chain Monte Carlo when using an +unbiased likelihood estimator, Biometrika, 102, 2, 2015, Pages 295–313, +https://doi.org/10.1093/biomet/asu075 + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 0562569f..ed7838f8 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -1,35 +1,38 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{summary.mcmc_output} -\alias{summary.mcmc_output} -\title{Summary of MCMC object} -\usage{ -\method{summary}{mcmc_output}(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) -} -\arguments{ -\item{object}{Output from \code{run_mcmc}} - -\item{return_se}{if \code{FALSE} (default), computation of standard -errors and effective sample sizes is omitted.} - -\item{variable}{Are the summary statistics computed for either \code{"theta"} (default), -\code{"states"}, or \code{"both"}?} - -\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only for hyperparameters theta.} - -\item{...}{Ignored.} -} -\description{ -This functions returns a list containing mean, standard deviations, standard errors, and -effective sample size estimates for parameters and states. -} -\details{ -For IS-MCMC two types of standard errors are reported. -SE-IS can be regarded as the square root of independent IS variance, -whereas SE corresponds to the square root of total asymptotic variance ( -see Remark 3 of Vihola et al. (2020)). -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print_mcmc.R +\name{summary.mcmc_output} +\alias{summary.mcmc_output} +\title{Summary of MCMC object} +\usage{ +\method{summary}{mcmc_output}(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) +} +\arguments{ +\item{object}{Output from \code{run_mcmc}} + +\item{return_se}{if \code{FALSE} (default), computation of standard +errors and effective sample sizes is omitted.} + +\item{variable}{Are the summary statistics computed for either +\code{"theta"} (default), \code{"states"}, or \code{"both"}?} + +\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only +for hyperparameters theta.} + +\item{...}{Ignored.} +} +\description{ +This functions returns a list containing mean, standard deviations, +standard errors, and effective sample size estimates for parameters and +states. +} +\details{ +For IS-MCMC two types of standard errors are reported. +SE-IS can be regarded as the square root of independent IS variance, +whereas SE corresponds to the square root of total asymptotic variance +(see Remark 3 of Vihola et al. (2020)). +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/svm.Rd b/man/svm.Rd index e589ff67..fefdac96 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -1,49 +1,52 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{svm} -\alias{svm} -\title{Stochastic Volatility Model} -\usage{ -svm(y, mu, rho, sd_ar, sigma) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{mu}{Prior for mu parameter of transition equation.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sd_ar}{Prior for the standard deviation of noise of the AR-process.} - -\item{sigma}{Prior for sigma parameter of observation equation, internally denoted as phi. Ignored -if \code{mu} is provided. Note that typically parametrization using mu is preferred due to -better numerical properties and availability of better Gaussian approximation. -Most notably the global approximation approach does not work with sigma parameterization as -sigma is not a parameter of the resulting approximate model.} -} -\value{ -Object of class \code{svm}. -} -\description{ -Constructs a simple stochastic volatility model with Gaussian errors and -first order autoregressive signal. -} -\examples{ - -data("exchange") -exchange <- exchange[1:100] # faster CRAN check -model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), - sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) - -obj <- function(pars) { - -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)), particles = 0) -} -opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), upper = c(0.999,10,10)) -pars <- opt$par -model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{svm} +\alias{svm} +\title{Stochastic Volatility Model} +\usage{ +svm(y, mu, rho, sd_ar, sigma) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{mu}{Prior for mu parameter of transition equation.} + +\item{rho}{prior for autoregressive coefficient.} + +\item{sd_ar}{Prior for the standard deviation of noise of the AR-process.} + +\item{sigma}{Prior for sigma parameter of observation equation, internally +denoted as phi. Ignored if \code{mu} is provided. Note that typically +parametrization using mu is preferred due to better numerical properties and +availability of better Gaussian approximation. +Most notably the global approximation approach does not work with sigma +parameterization as sigma is not a parameter of the resulting approximate +model.} +} +\value{ +Object of class \code{svm}. +} +\description{ +Constructs a simple stochastic volatility model with Gaussian errors and +first order autoregressive signal. +} +\examples{ + +data("exchange") +exchange <- exchange[1:100] # faster CRAN check +model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), + sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) + +obj <- function(pars) { + -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), + sd_ar = halfnormal(pars[2],sd=5), + sigma = halfnormal(pars[3],sd=2)), particles = 0) +} +opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), + upper = c(0.999,10,10)) +pars <- opt$par +model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), + sd_ar = halfnormal(pars[2],sd=5), + sigma = halfnormal(pars[3],sd=2)) + +} diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index 161920aa..978dbbc8 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -138,135 +138,14 @@ test_that("Two iid model gives same results as two univariate models", { test_that("Gaussian approximation works for nonlinear models", { - Rcpp::sourceCpp(code = ' - #include - // [[Rcpp::depends(RcppArmadillo)]] - // [[Rcpp::interfaces(r, cpp)]] - - // [[Rcpp::export]] - arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::vec a1(1); - a1(0) = 0; - return a1; - } - // [[Rcpp::export]] - arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::mat P1(1, 1); - P1(0,0) = 1; - return P1; - } - - // [[Rcpp::export]] - arma::mat H_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat H(1,1); - H(0, 0) = exp(theta(0)); - return H; - } - - // Function for the Cholesky of state level covariance - // [[Rcpp::export]] - arma::mat R_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat R(1, 1); - R(0, 0) = 1; - return R; - } - - - // Z function - // [[Rcpp::export]] - arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return alpha; - } - // Jacobian of Z function - // [[Rcpp::export]] - arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Z_gn(1, 1); - Z_gn(0, 0) = 1.0; - return Z_gn; - } - - // T function - // [[Rcpp::export]] - arma::vec T_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return alpha; - } - - // Jacobian of T function - // [[Rcpp::export]] - arma::mat T_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Tg(1, 1); - Tg(0, 0) = 1.0; - return Tg; - } - - // log-prior pdf for theta - // [[Rcpp::export]] - double log_prior_pdf(const arma::vec& theta) { - return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0); //jacobian term - } - - // [[Rcpp::export]] - Rcpp::List create_xptrs() { - - // typedef for a pointer of nonlinear function of model equation - // returning vec (T, Z) - typedef arma::vec (*nvec_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear function returning mat (Tg, Zg, H, R) - typedef arma::mat (*nmat_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - - // typedef for a pointer returning a1 - typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer returning P1 - typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer of log-prior function - typedef double (*prior_fnPtr)(const arma::vec& theta); - - return Rcpp::List::create( - Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), - Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), - Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), - Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), - Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), - Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), - Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), - Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), - Rcpp::Named("log_prior_pdf") = - Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); - }') - pntrs <- create_xptrs() + pntrs <- nlg_example_models("linear_gaussian") set.seed(1) y <- cumsum(rnorm(10)) + rnorm(10) model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, - theta = c("log_H" = 0), log_prior_pdf = pntrs$log_prior_pdf, - n_states = 1, n_etas = 1, state_names = c("state")) + theta = c(log_H = 0), log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") model_gaussian <- bsm_lg(y, sd_y = 1, sd_level = 1, P1 = 1) expect_equal( logLik(model_nlg, method = "ekf", particles = 0), diff --git a/tests/testthat/test_as_data_frame.R b/tests/testthat/test_as_data_frame.R new file mode 100644 index 00000000..c3a7e604 --- /dev/null +++ b/tests/testthat/test_as_data_frame.R @@ -0,0 +1,41 @@ +context("Test as.data.frame") + +set.seed(123) +model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, + sd_y = uniform(1, 0, 10), + sd_level = uniform(1, 0, 10)) + +test_that("expanded and not expanded data frame work equally for theta", { + + expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, seed = 1), NA) + d <- expect_error(as.data.frame(mcmc_bsm), NA) + expect_equal(colnames(d), c("iter", "value", "variable", "weight")) + expect_equal(unique(d$variable), c("sd_y", "sd_level")) + expect_equal(mean(d$value[d$variable == "sd_level"]), + weighted.mean(mcmc_bsm$theta[, 2], mcmc_bsm$counts)) + + d <- expect_error(as.data.frame(mcmc_bsm, expand = FALSE), NA) + expect_equal(colnames(d), c("iter", "value", "variable", "weight")) + expect_equal(unique(d$variable), c("sd_y", "sd_level")) + expect_equal(weighted.mean(d$value[d$variable == "sd_level"], + d$weight[d$variable == "sd_level"]), + weighted.mean(mcmc_bsm$theta[, 2], mcmc_bsm$counts)) +}) + +test_that("expanded and not expanded data frame work equally for states", { + + expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, seed = 1), NA) + d <- expect_error(as.data.frame(mcmc_bsm, variable = "state"), NA) + expect_equal(colnames(d), c("iter", "value", "variable", "time", "weight")) + expect_equal(unique(d$variable), c("level", "slope")) + expect_equal(mean(d$value[d$variable == "slope" & d$time == 3]), + weighted.mean(mcmc_bsm$alpha[3, 2, ], mcmc_bsm$counts)) + + d <- expect_error(as.data.frame(mcmc_bsm, variable = "state", + expand = FALSE), NA) + expect_equal(colnames(d), c("iter", "value", "variable", "time", "weight")) + expect_equal(unique(d$variable), c("level", "slope")) + expect_equal(weighted.mean(d$value[d$variable == "slope" & d$time == 3], + d$weight[d$variable == "slope" & d$time == 3]), + weighted.mean(mcmc_bsm$alpha[3, 2, ], mcmc_bsm$counts)) +}) From d28e6e4dfad7d976b42a31e1c8027815921598af Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 1 Sep 2021 23:32:43 +0300 Subject: [PATCH 022/180] test particle_smoother --- tests/testthat/test_particle_smoother.R | 96 +++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 tests/testthat/test_particle_smoother.R diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R new file mode 100644 index 00000000..41ee76be --- /dev/null +++ b/tests/testthat/test_particle_smoother.R @@ -0,0 +1,96 @@ + +context("Test that particle smoothers work") + + +test_that("Test that particle_smoother for LGSSM works as Kalman smoother", { + + expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, + sd_y = 0.01, a1 = c(1, 0), P1 = diag(0.01, 2)), NA) + expect_equal(smoother(model_bsm)$alphahat, + particle_smoother(model_bsm, 1e5, seed = 1)$alphahat, tolerance = 1e-2) +}) + +test_that("Test that BSF and PSI particle_smoother for LGSSM are with MC error", { + + expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, + sd_y = 0.01, a1 = c(1, 0), P1 = diag(0.01, 2)), NA) + expect_error(out1 <- + particle_smoother(model_bsm, 1e4, method = "psi", seed = 1), NA) + expect_error(out2 <- + particle_smoother(model_bsm, 1e4, method = "bsf", seed = 1), NA) + expect_equal(out$alphahat, + out2$alphahat, tolerance = 1e-2) + expect_equal(out$Vt, + out2$Vtt, tolerance = 1e-2) +}) + +test_that("Particle smoother for LGSSM returns finite values", { + + expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), + H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(2, 2), c(2, 2, 1)), + a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), + NA) + expect_error(out <- particle_smoother(model_ssm_ulg, 10, seed = 1), + NA) + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) +}) + +test_that("Particle smoother for poisson bsm_ng returns finite values", { + + expect_error(model <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, + P1 = diag(2, 2), distribution = "poisson"), NA) + expect_error(out <- particle_smoother(model, 10, seed = 1), NA) + + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) +}) + +test_that("Particle smoother for binomial bsm_ng returns finite values", { + + expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, + sd_slope = 2, P1 = diag(2, 2), + distribution = "binomial"), NA) + expect_error(out <- particle_smoother(model, 10, seed = 1), NA) + + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) + +}) + +test_that("Particle smoother for NB bsm_ng returns finite values", { + + expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, + sd_slope = 2, P1 = diag(2, 2), + distribution = "negative binomial"), NA) + expect_error(out <- particle_smoother(model, 10, seed = 1), NA) + + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) + +}) + + +test_that("Particle smoother for svm returns finite values", { + + data("exchange") + model <- svm(exchange[1:20], rho = uniform(0.98, -0.999, 0.999), + sd_ar = halfnormal(0.2, 5), sigma = halfnormal(0.2, 2)) + + expect_error(out1 <- + particle_smoother(model, 1000, method = "psi", seed = 1), NA) + expect_error(out2 <- + particle_smoother(model, 10000, method = "bsf", seed = 1), NA) + + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) + + expect_equal(out1$alphahat, out2$alphahat, tol = 1e-2) +}) + From ccf8dbd240c54607a3650b34a996eed97110c1d2 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 08:06:33 +0300 Subject: [PATCH 023/180] fix particle smoother test --- tests/testthat/test_particle_smoother.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 41ee76be..447a6991 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -87,9 +87,9 @@ test_that("Particle smoother for svm returns finite values", { expect_error(out2 <- particle_smoother(model, 10000, method = "bsf", seed = 1), NA) - expect_true(is.finite(sum(out$alpha))) - expect_true(is.finite(sum(out$alphahat))) - expect_true(is.finite(sum(out$Vt))) + expect_true(is.finite(sum(out1$alpha))) + expect_true(is.finite(sum(out1$alphahat))) + expect_true(is.finite(sum(out1$Vt))) expect_equal(out1$alphahat, out2$alphahat, tol = 1e-2) }) From fe83988cca7c149c74260747dab4883f5d6def57 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 08:57:48 +0300 Subject: [PATCH 024/180] fix calls to post-correction due to merge mess earlier --- src/R_postcorrection.cpp | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/R_postcorrection.cpp b/src/R_postcorrection.cpp index 8dc05a70..79ddb2eb 100644 --- a/src/R_postcorrection.cpp +++ b/src/R_postcorrection.cpp @@ -17,11 +17,10 @@ arma::vec suggest_n_nongaussian(const Rcpp::List model_, const int model_type) { arma::vec sds(candidates.n_elem); - Rcpp::Function update_fn = model_["update_fn"]; switch (model_type) { case 0: { ssm_mng model(model_, seed); - model.update_model(theta, update_fn); + model.update_model(theta, model_["update_fn"]); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); arma::cube alpha(model.m, model.n + 1, nsim); @@ -37,7 +36,7 @@ arma::vec suggest_n_nongaussian(const Rcpp::List model_, } break; case 1: { ssm_ung model(model_, seed); - model.update_model(theta, update_fn); + model.update_model(theta, model_["update_fn"]); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); arma::cube alpha(model.m, model.n + 1, nsim); @@ -53,7 +52,7 @@ arma::vec suggest_n_nongaussian(const Rcpp::List model_, } break; case 2: { bsm_ng model(model_, seed); - model.update_model(theta, update_fn); + model.update_model(theta); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); arma::cube alpha(model.m, model.n + 1, nsim); @@ -69,7 +68,7 @@ arma::vec suggest_n_nongaussian(const Rcpp::List model_, } break; case 3: { svm model(model_, seed); - model.update_model(theta, update_fn); + model.update_model(theta); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); arma::cube alpha(model.m, model.n + 1, nsim); @@ -85,7 +84,7 @@ arma::vec suggest_n_nongaussian(const Rcpp::List model_, } break; case 4: { ar1_ng model(model_, seed); - model.update_model(theta, update_fn); + model.update_model(theta); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); arma::cube alpha(model.m, model.n + 1, nsim); @@ -114,7 +113,6 @@ arma::vec suggest_n_nonlinear(const arma::mat& y, SEXP Z, SEXP H, const arma::vec theta_map, const arma::vec candidates, const unsigned int replications, const unsigned int seed) { - Rcpp::XPtr xpfun_Z(Z); Rcpp::XPtr xpfun_H(H); Rcpp::XPtr xpfun_T(T); From 9b7b0f7efc60f165a1281ea2a7303cd94f6e5bf9 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 08:58:13 +0300 Subject: [PATCH 025/180] tests for sim_smoother and post_correct --- tests/testthat/test_post_correct.R | 45 ++++++++++++++++++++++++++++++ tests/testthat/test_sim_smoother.R | 26 +++++++++++++++++ 2 files changed, 71 insertions(+) create mode 100644 tests/testthat/test_post_correct.R create mode 100644 tests/testthat/test_sim_smoother.R diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R new file mode 100644 index 00000000..aac2bc3e --- /dev/null +++ b/tests/testthat/test_post_correct.R @@ -0,0 +1,45 @@ + +context("Post-correction and suggest_N") + + +test_that("Test that sim_smoother for LGSSM works as Kalman smoother", { + set.seed(1) + n <- 14 + x1 <- sin((2 * pi / 12) * 1:n) + x2 <- cos((2 * pi / 12) * 1:n) + alpha <- numeric(n) + alpha[1] <- 0 + rho <- 0.7 + sigma <- 2 + mu <- 1 + for(i in 2:n) { + alpha[i] <- rnorm(1, mu * (1 - rho) + rho * alpha[i-1], sigma) + } + u <- rpois(n, 50) + y <- rbinom(n, size = u, plogis(0.5 * x1 + x2 + alpha)) + + expect_error(model <- ar1_ng(y, distribution = "binomial", + rho = uniform(0.5, -1, 1), sigma = gamma(1, 2, 0.001), + mu = normal(0, 0, 10), + xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), + u = u), NA) + + + expect_error(out_approx <- run_mcmc(model, mcmc_type = "approx", + local_approx = FALSE, iter = 1000, output_type = "summary"), NA) + + expect_error(estN <- suggest_N(model, out_approx, + replications = 10, candidates = c(5, 10)), NA) + + expect_identical(estN$N, 5) + + # Can't really test for correctness with limited time + expect_error(out_is2 <- post_correct(model, out_approx, particles = estN$N, + threads = 2), NA) + expect_lt(sum(out_is2$theta), Inf) + expect_lt(sum(out_is2$alphahat), Inf) + expect_lt(sum(out_is2$Vt), Inf) + expect_lt(max(out_is2$weights), Inf) + expect_gt(max(out_is2$weights), 0) +}) + diff --git a/tests/testthat/test_sim_smoother.R b/tests/testthat/test_sim_smoother.R new file mode 100644 index 00000000..2e5767ed --- /dev/null +++ b/tests/testthat/test_sim_smoother.R @@ -0,0 +1,26 @@ + +context("Test that simulation smoother work") + + +test_that("Test that sim_smoother for LGSSM works as Kalman smoother", { + y <- c(0.89, -0.05, -1.9, -1.9, 1.77, -0.22) + expect_error(model_bsm <- bsm_lg(y, sd_level = 1, sd_slope = 0.01, + sd_y = 1, a1 = c(0, 0), P1 = diag(2)), NA) + expect_error(sims <- sim_smoother(model_bsm, nsim = 10, + use_antithetic = TRUE), NA) + expect_equal(smoother(model_bsm)$alphahat, + as.ts(apply(sims, 1:2, mean))) +}) + + +test_that("sim_smoother for non-gaussian model works as Kalman smoother", { + + y <- c(11, 3, 1, 1, 354, 2) + expect_error(model <- bsm_ng(y, sd_level = 1, + sd_seasonal = 0.1, period = 4, + P1 = diag(c(1, 0.1, 0.1, 0.1)), distribution = "poisson"), NA) + expect_error(sims <- sim_smoother(model, nsim = 10, + use_antithetic = TRUE), NA) + expect_equal(smoother(model)$alphahat, + as.ts(apply(sims, 1:2, mean))) +}) From f2c22af88d2190f9b8ae07e8bb94091bc7e2398d Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 10:15:07 +0300 Subject: [PATCH 026/180] add phi, use priors --- tests/testthat/test_particle_smoother.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 447a6991..5affce73 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -64,8 +64,10 @@ test_that("Particle smoother for binomial bsm_ng returns finite values", { test_that("Particle smoother for NB bsm_ng returns finite values", { - expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, - sd_slope = 2, P1 = diag(2, 2), + expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), + sd_level = uniform(0.1,0,1), + sd_slope = halfnormal(0.1, 1), + P1 = diag(2, 2), phi = gamma(1, 2, 2), distribution = "negative binomial"), NA) expect_error(out <- particle_smoother(model, 10, seed = 1), NA) From 1745915ec02e03459d38ef0d14bfe7054d543aa0 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 12:17:27 +0300 Subject: [PATCH 027/180] fix smoother tests --- tests/testthat/test_particle_smoother.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 5affce73..219490d5 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -18,9 +18,9 @@ test_that("Test that BSF and PSI particle_smoother for LGSSM are with MC error", particle_smoother(model_bsm, 1e4, method = "psi", seed = 1), NA) expect_error(out2 <- particle_smoother(model_bsm, 1e4, method = "bsf", seed = 1), NA) - expect_equal(out$alphahat, + expect_equal(out1$alphahat, out2$alphahat, tolerance = 1e-2) - expect_equal(out$Vt, + expect_equal(out1$Vt, out2$Vtt, tolerance = 1e-2) }) @@ -81,11 +81,11 @@ test_that("Particle smoother for NB bsm_ng returns finite values", { test_that("Particle smoother for svm returns finite values", { data("exchange") - model <- svm(exchange[1:20], rho = uniform(0.98, -0.999, 0.999), - sd_ar = halfnormal(0.2, 5), sigma = halfnormal(0.2, 2)) + model <- svm(exchange[1:20], rho = uniform(0.98, -1, 1), + sd_ar = halfnormal(0.01,0.1), mu = normal(0, 0, 1)) expect_error(out1 <- - particle_smoother(model, 1000, method = "psi", seed = 1), NA) + particle_smoother(model, 100, method = "psi", seed = 1), NA) expect_error(out2 <- particle_smoother(model, 10000, method = "bsf", seed = 1), NA) @@ -93,6 +93,6 @@ test_that("Particle smoother for svm returns finite values", { expect_true(is.finite(sum(out1$alphahat))) expect_true(is.finite(sum(out1$Vt))) - expect_equal(out1$alphahat, out2$alphahat, tol = 1e-2) + expect_equal(out1$alphahat, out2$alphahat, tol = 0.1) }) From 6da9c2b0d4d8a8459eec7a531a1a03923f732e4f Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 16:40:58 +0300 Subject: [PATCH 028/180] typo in tests --- tests/testthat/test_particle_smoother.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 219490d5..9903bf2f 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -21,7 +21,7 @@ test_that("Test that BSF and PSI particle_smoother for LGSSM are with MC error", expect_equal(out1$alphahat, out2$alphahat, tolerance = 1e-2) expect_equal(out1$Vt, - out2$Vtt, tolerance = 1e-2) + out2$Vt, tolerance = 1e-2) }) test_that("Particle smoother for LGSSM returns finite values", { From b75ecf1f7515311d33e1a0a90829be0339107daf Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 18:16:03 +0300 Subject: [PATCH 029/180] more tests --- tests/testthat/test_ekpf.R | 31 +++++++++++++++++++++++++++++++ tests/testthat/test_mcmc.R | 21 +++++++++++++++++++-- tests/testthat/test_predict.R | 30 ++++++++++++++++++++++++++++++ 3 files changed, 80 insertions(+), 2 deletions(-) create mode 100644 tests/testthat/test_ekpf.R create mode 100644 tests/testthat/test_predict.R diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R new file mode 100644 index 00000000..cf3d5442 --- /dev/null +++ b/tests/testthat/test_ekpf.R @@ -0,0 +1,31 @@ +context("Test Gaussian approximation") + + +test_that("Gaussian approximation results of bssm and KFAS coincide", { + + skip_on_cran() + set.seed(1) + n <- 10 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + + pntrs <- nlg_example_models("sin_exp") + + expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + + expect_error(out <- ekpf_filter(model_nlg, 10), NA) + expect_lt(out$logLik, 6) + expect_gt(out$logLik, 1) + expect_gte(min(out$w), 0-1e16) + expect_lte(max(out$w), 1+1e16) + expect_warning(ekpf_filter(model_nlg, nsim = 10)) +}) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 76f93abc..9c7b40b0 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -42,6 +42,21 @@ test_that("DA-MCMC results for Poisson model are correct", { expect_error(mcmc_poisson <- run_mcmc(model_bssm, mcmc_type = "da", iter = 100, particles = 5, seed = 42), NA) + expect_warning(summary(mcmc_poisson, only_theta = TRUE)) + + sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) + + expect_equal(sumr$theta, + structure(c(0.25892090511681, 0.186796779799571), + .Dim = 1:2, .Dimnames = list( + "sd_level", c("Mean", "SD"))), tol = 0.01) + + + states <- expand_sample(mcmc_poisson, variable = "states") + + expect_equal(as.numeric(sumr$states$Mean[,1]), + as.numeric(colMeans(states$level))) + expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, particles = 5)[-14], run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, @@ -72,7 +87,7 @@ test_that("MCMC results for SV model using IS-correction are correct", { mcmc_type = "is1", seed = 1)[-16], run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", seed = 1)[-16]) - + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1)[-16], run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", @@ -95,7 +110,9 @@ test_that("MCMC results for SV model using IS-correction are correct", { expect_error(mcmc_sv <- run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf"), NA) - + + expect_warning(expand_sample(mcmc_sv)) + expect_gt(mcmc_sv$acceptance_rate, 0) expect_true(is.finite(sum(mcmc_sv$theta))) expect_true(is.finite(sum(mcmc_sv$alpha))) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R new file mode 100644 index 00000000..50dc6f32 --- /dev/null +++ b/tests/testthat/test_predict.R @@ -0,0 +1,30 @@ +context("Test predictions") + + +test_that("Gaussian predictions work", { + + set.seed(1) + y <- rnorm(10, cumsum(rnorm(10, 0, 0.1)), 0.1) + model <- ar1_lg(y, + rho = uniform(0.9, 0, 0.99), mu = 0, + sigma = halfnormal(0.1, 1), + sd_y = halfnormal(0.1, 1)) + + mcmc_results <- run_mcmc(model, iter = 1000) + future_model <- model + future_model$y <- rep(NA, 3) + pred <- predict(mcmc_results, future_model, type = "mean", + nsim = 100) + + expect_gt(mean(pred$value[pred$time == 3]), -0.5) + expect_lt(mean(pred$value[pred$time == 3]), 0.5) + + # Posterior predictions for past observations: + yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100) + meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100) + + expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + +}) From 03a8ac1d5df794916755d1bc5a3814be04a370cd Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 2 Sep 2021 20:09:29 +0300 Subject: [PATCH 030/180] increase tolerance for mcmc test --- tests/testthat/test_mcmc.R | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 9c7b40b0..f3200b56 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -46,10 +46,7 @@ test_that("DA-MCMC results for Poisson model are correct", { sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) - expect_equal(sumr$theta, - structure(c(0.25892090511681, 0.186796779799571), - .Dim = 1:2, .Dimnames = list( - "sd_level", c("Mean", "SD"))), tol = 0.01) + expect_lt(sum(abs(sumr$theta - c(0.25892090511681, 0.186796779799571))), 0.5) states <- expand_sample(mcmc_poisson, variable = "states") From d3a2118b7e62f8bf15c192bbd03abee31456019b Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 00:18:13 +0300 Subject: [PATCH 031/180] more tests for ekf --- man/nlg_example_models.Rd | 5 +++-- tests/testthat/test_ekpf.R | 36 +++++++++++++++++++++++++++++++++--- tests/testthat/test_mcmc.R | 15 +++++++++++---- 3 files changed, 47 insertions(+), 9 deletions(-) diff --git a/man/nlg_example_models.Rd b/man/nlg_example_models.Rd index c99aef04..21f55806 100644 --- a/man/nlg_example_models.Rd +++ b/man/nlg_example_models.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ngl_example_models.R +% Please edit documentation in R/nlg_example_models.R \name{nlg_example_models} \alias{nlg_example_models} \title{Example C++ Codes for Non-Linear Models} @@ -7,7 +7,8 @@ nlg_example_models(example, return_code = FALSE) } \arguments{ -\item{example}{Name of the example model.} +\item{example}{Name of the example model. +Run \code{nlg_example_models("abc")} to get the names of possible models.} \item{return_code}{If TRUE, will not compile the model but only returns the corresponding code.} diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index cf3d5442..4a273f59 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -1,7 +1,7 @@ -context("Test Gaussian approximation") +context("Test EKF") -test_that("Gaussian approximation results of bssm and KFAS coincide", { +test_that("Particle filtering based on EKF works", { skip_on_cran() set.seed(1) @@ -22,10 +22,40 @@ test_that("Gaussian approximation results of bssm and KFAS coincide", { log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state"), NA) - expect_error(out <- ekpf_filter(model_nlg, 10), NA) + expect_error(out <- ekpf_filter(model_nlg, 100), NA) expect_lt(out$logLik, 6) expect_gt(out$logLik, 1) expect_gte(min(out$w), 0-1e16) expect_lte(max(out$w), 1+1e16) expect_warning(ekpf_filter(model_nlg, nsim = 10)) + + out_ekf <- particle_smoother(model_nlg, 1000, method = "ekf") + out_psi <- particle_smoother(model_nlg, 1000, method = "psi") + out_bsf <- particle_smoother(model_nlg, 1000, method = "bsf") + expect_equal(out_ekf$alphahat[9:10], + c(0.0263875638744833, 0.0734903567971838), tol = 0.1) + expect_equal(out_psi$alphahat[9:10], + c(0.0263875638744833, 0.0734903567971838), tol = 0.1) + expect_equal(out_bsf$alphahat[9:10], + c(0.0263875638744833, 0.0734903567971838), tol = 0.1) + +}) + +test_that("EKF and IEKF work", { + skip_on_cran() + set.seed(1) + n <- 10 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + + pntrs <- nlg_example_models("sin_exp") + + expect_equal(ekf(model_nlg)$logLik, 3.55814184565819) + expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 3.69550903344128) + expect_equal(ekf(model_nlg, iekf_iter = 1), + ekf(model_nlg, iekf_iter = 2)) }) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index f3200b56..650d7048 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -29,7 +29,7 @@ test_that("MCMC results for Gaussian model are correct", { expect_gte(min(mcmc_bsm$theta), 0) expect_lt(max(mcmc_bsm$theta), Inf) expect_true(is.finite(sum(mcmc_bsm$alpha))) - + }) @@ -66,7 +66,7 @@ test_that("DA-MCMC results for Poisson model are correct", { output_type = "theta", particles = 5)[-13], run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, output_type = "theta", particles = 5)[-13]) - + expect_gt(mcmc_poisson$acceptance_rate, 0) expect_gte(min(mcmc_poisson$theta), 0) expect_lt(max(mcmc_poisson$theta), Inf) @@ -84,7 +84,7 @@ test_that("MCMC results for SV model using IS-correction are correct", { mcmc_type = "is1", seed = 1)[-16], run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", seed = 1)[-16]) - + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1)[-16], run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", @@ -105,9 +105,16 @@ test_that("MCMC results for SV model using IS-correction are correct", { run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf")[-16]) + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + threads = 2L)[-16], + run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + threads = 2L)[-16]) + expect_error(mcmc_sv <- run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf"), NA) - + expect_warning(expand_sample(mcmc_sv)) expect_gt(mcmc_sv$acceptance_rate, 0) From 5a1703c23f83a8eb271b0c2a65d60090286c2795 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 00:31:23 +0300 Subject: [PATCH 032/180] SDE tests --- tests/testthat/test_sde.R | 109 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 tests/testthat/test_sde.R diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R new file mode 100644 index 00000000..9a36e834 --- /dev/null +++ b/tests/testthat/test_sde.R @@ -0,0 +1,109 @@ +context("Test SDE") + +code <- ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // Drift function + // [[Rcpp::export]] + double drift(const double x, const arma::vec& theta) { + return theta(0) * x; + } + // diffusion function + // [[Rcpp::export]] + double diffusion(const double x, const arma::vec& theta) { + return std::max(0.0, theta(1) * x); + } + // Derivative of the diffusion function + // [[Rcpp::export]] + double ddiffusion(const double x, const arma::vec& theta) { + return theta(1) * (x > 0.0); + } + + // log-density of the prior + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + + double log_pdf = 0.0; + if(theta(0) <= -0.05 || theta(1) <= 0.0 || theta(2) <= 0.0) { + log_pdf = -std::numeric_limits::infinity(); + } + else { + log_pdf = R::dnorm(theta(0), 0, 0.05, 1) + + R::dnorm(theta(1), 0, 0.5, 1) + + R::dnorm(theta(2), 1, 0.1, 1); + } + return log_pdf; + } + + // log-density of observations + // given vector of sampled states alpha + // [[Rcpp::export]] + arma::vec log_obs_density(const double y, + const arma::vec& alpha, const arma::vec& theta) { + + arma::vec log_pdf(alpha.n_elem); + for (unsigned int i = 0; i < alpha.n_elem; i++) { + log_pdf(i) = R::dnorm(y, std::log(alpha(i)), theta(2), 1); + } + return log_pdf; + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + // typedef for a pointer of drift/volatility function + typedef double (*fnPtr)(const double x, const arma::vec& theta); + // typedef for log_prior_pdf + typedef double (*prior_fnPtr)(const arma::vec& theta); + // typedef for log_obs_density + typedef arma::vec (*obs_fnPtr)(const double y, + const arma::vec& alpha, const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), + Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), + Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), + Rcpp::Named("prior") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), + Rcpp::Named("obs_density") = + Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + } +' +Rcpp::sourceCpp(code = code) +pntrs <- create_xptrs() +set.seed(1) +x <- 1 +y <- rep(NA, 10) +dt <- 1 +mu <- 0.05 +sigma_x <- 0.3 +sigma_y <- 1 +for (k in 1:10) { + x <- x*exp((mu-0.5*sigma_x^2)*dt + sqrt(dt) * rnorm(1, sd=sigma_x)) + y[k] <- rnorm(1, mean=log(x), sd=sigma_y) +} + + +test_that("MCMC for SDE works", { + set.seed(123) + model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, + pntrs$ddiffusion, pntrs$obs_density, + pntrs$prior, c(0.05, 0.3, 1), x0 = 1, positive = TRUE) + + expect_error(ll <- logLik(model, 10000, L = 3), NA) + expect_equal(ll, -17, tol = 1) + expect_error(out_bsf <- bootstrap_filter(model, 1000, L = 3), NA) + expect_equal(ll, out_bsf$logLik, tol = 1) + + expect_error(out <- run_mcmc(model, iter = 500, + particles = 10, mcmc_type = "pm", L_f = 2), NA) + + expect_gt(out$acceptance_rate, 0) + + expect_error(out2 <- run_mcmc(model, iter = 500, + particles = 10, mcmc_type = "is2", L_c = 1, L_f = 2), NA) + + expect_gt(out2$acceptance_rate, 0) + expect_equal(mean(colMeans(out$theta)-colMeans(out2$theta)), 0, tol = 1) +}) From 3619ff6ac048277d343f2ae0790727e91245379c Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 08:26:05 +0300 Subject: [PATCH 033/180] fix tests, ignore codemeta in R build --- .Rbuildignore | 34 +++++++++++++++++----------------- R/nlg_example_models.R | 2 ++ tests/testthat/test_ekpf.R | 7 +++++++ 3 files changed, 26 insertions(+), 17 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 273ca2ed..21c58f1b 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,17 +1,17 @@ -^\.git$ -^.*\.Rproj$ -^\.Rproj\.user$ -^README\.md$ -^\.gitignore$ -^\.travis\.yml$ -^TODO$ -^\.Rhistory$ -^bssm.pdf$ -^growth_model.pdf$ -^vignettes/psi_pf_experiments/.*result.*\.rds$ -^vignettes/psi_pf_experiments/.*truth.*\.rds$ -^vignettes/psi_pf_experiments/.*\.sh$ -^vignettes/psi_pf_experiments/.*\.R$ -^\.github$ -slides_UseR2021 -^codecov\.yml$ +^\.git$ +^.*\.Rproj$ +^\.Rproj\.user$ +^README\.md$ +^\.gitignore$ +^\.travis\.yml$ +^TODO$ +^\.Rhistory$ +^bssm.pdf$ +^growth_model.pdf$ +^vignettes/psi_pf_experiments/.*result.*\.rds$ +^vignettes/psi_pf_experiments/.*truth.*\.rds$ +^vignettes/psi_pf_experiments/.*\.sh$ +^vignettes/psi_pf_experiments/.*\.R$ +^\.github$ +^codecov\.yml$ +^codemeta\.json$ diff --git a/R/nlg_example_models.R b/R/nlg_example_models.R index 0654323e..5992e1d7 100644 --- a/R/nlg_example_models.R +++ b/R/nlg_example_models.R @@ -261,6 +261,8 @@ nlg_example_models <- function(example, return_code = FALSE) { ' }) if (!return_code) { + # create dummy variable to get rid of "undefined variable" note + create_xptrs <- NULL Rcpp::sourceCpp(code = code) create_xptrs() } else code diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index 4a273f59..4827a067 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -54,6 +54,13 @@ test_that("EKF and IEKF work", { pntrs <- nlg_example_models("sin_exp") + expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + expect_equal(ekf(model_nlg)$logLik, 3.55814184565819) expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 3.69550903344128) expect_equal(ekf(model_nlg, iekf_iter = 1), From 65beb55a422a4c2df651039aa29a33a6765ea545 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 15:28:44 +0300 Subject: [PATCH 034/180] import fast ekf smoother, test it too --- NAMESPACE | 1 + R/smoother.R | 13 ++++++------- man/ekf_smoother.Rd | 6 +++++- tests/testthat/test_ekpf.R | 8 ++++++++ 4 files changed, 20 insertions(+), 8 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 44631cf0..f4cdc427 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -40,6 +40,7 @@ export(bootstrap_filter) export(bsm_lg) export(bsm_ng) export(ekf) +export(ekf_fast_smoother) export(ekf_smoother) export(ekpf_filter) export(expand_sample) diff --git a/R/smoother.R b/R/smoother.R index 24055645..164a8fed 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -62,7 +62,8 @@ smoother.nongaussian <- function(model, ...) { #' Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother #' for the given non-linear Gaussian model of class \code{ssm_nlg}, #' and returns the smoothed estimates of the states and the corresponding -#' variances. +#' variances. Function \code{ekf_fast_smoother} computes only smoothed +#' estimates of the states. #' #' @param model Model model #' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is @@ -72,7 +73,6 @@ smoother.nongaussian <- function(model, ...) { #' \code{Vt} and \code{Ptt}. #' @export #' @rdname ekf_smoother -#' @export ekf_smoother <- function(model, iekf_iter = 0) { out <- ekf_smoother_nlg(t(model$y), model$Z, model$H, model$T, @@ -88,7 +88,8 @@ ekf_smoother <- function(model, iekf_iter = 0) { start = start(model$y), frequency = frequency(model$y)) out } - +#' @rdname ekf_smoother +#' @export ekf_fast_smoother <- function(model, iekf_iter = 0) { out <- ekf_fast_smoother_nlg(t(model$y), model$Z, model$H, model$T, @@ -96,9 +97,7 @@ ekf_fast_smoother <- function(model, iekf_iter = 0) { model$theta, model$log_prior_pdf, model$known_params, model$known_tv_params, model$n_states, model$n_etas, as.integer(model$time_varying), iekf_iter) - colnames(out$alphahat) <- colnames(out$Vt) <- - rownames(out$Vt) <- model$state_names - ts(out[-nrow(out$alphahat), , drop = FALSE], start = start(model$y), + colnames(out$alphahat) <- model$state_names + ts(out$alphahat[-nrow(out$alphahat),, drop = FALSE], start = start(model$y), frequency = frequency(model$y)) } - diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index 77239c69..c3b4c57b 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -2,9 +2,12 @@ % Please edit documentation in R/smoother.R \name{ekf_smoother} \alias{ekf_smoother} +\alias{ekf_fast_smoother} \title{Extended Kalman Smoothing} \usage{ ekf_smoother(model, iekf_iter = 0) + +ekf_fast_smoother(model, iekf_iter = 0) } \arguments{ \item{model}{Model model} @@ -21,5 +24,6 @@ smoothed state estimates \code{alphahat}, and the corresponding variances Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother for the given non-linear Gaussian model of class \code{ssm_nlg}, and returns the smoothed estimates of the states and the corresponding -variances. +variances. Function \code{ekf_fast_smoother} computes only smoothed +estimates of the states. } diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index 4827a067..11893336 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -65,4 +65,12 @@ test_that("EKF and IEKF work", { expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 3.69550903344128) expect_equal(ekf(model_nlg, iekf_iter = 1), ekf(model_nlg, iekf_iter = 2)) + + out_ekf1 <- ekf_smoother(model_nlg) + out_ekf2 <- ekf_fast_smoother(model_nlg) + expect_equal(out_ekf1$alphahat[9:10], + c(0.0333634309012196, 0.0797729159367873), tol = 0.1) + expect_equal(out_ekf1$alphahat, out_ekf2) + + expect_error(ukf(model_nlg), NA) }) From 275df241e5f924831804cb86b5e611504b7ba935 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 15:38:25 +0300 Subject: [PATCH 035/180] test MCMC for nlg model --- tests/testthat/test_mcmc.R | 72 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 650d7048..6ded1ca1 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -123,3 +123,75 @@ test_that("MCMC results for SV model using IS-correction are correct", { expect_gte(min(mcmc_sv$weights), 0) expect_lt(max(mcmc_sv$weights), Inf) }) + + + +test_that("MCMC for nonlinear models work", { + skip_on_cran() + set.seed(1) + n <- 10 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + + pntrs <- nlg_example_models("sin_exp") + + expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is1", seed = 1, sampling_mcmc_type = "psi")[-16], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is1", seed = 1, sampling_mcmc_type = "psi")[-16]) + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf")[-16], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf")[-16]) + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is3", seed = 1, sampling_mcmc_type = "ekf")[-16], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is3", seed = 1, sampling_mcmc_type = "ekf")[-16]) + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + threads = 2L)[-16], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + threads = 2L)[-16]) + + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary")[-15], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary")[-15]) + + expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary", + threads = 2L)[-17], + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary", + threads = 2L)[-17]) + + expect_error(out<-run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "da", seed = 1, sampling_mcmc_type = "bsf"), NA) + + expect_equal(sum(is.na(out$theta)), 0) + expect_equal(sum(is.na(out$alpha)), 0) + expect_equal(sum(!is.finite(out$theta)), 0) + expect_equal(sum(!is.finite(out$alpha)), 0) + +}) From dfb6b8ce7a868896911c6860d81b2851e575cfbb Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 15:40:20 +0300 Subject: [PATCH 036/180] few more tests for MCMC --- tests/testthat/test_mcmc.R | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 6ded1ca1..1c453389 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -122,6 +122,22 @@ test_that("MCMC results for SV model using IS-correction are correct", { expect_true(is.finite(sum(mcmc_sv$alpha))) expect_gte(min(mcmc_sv$weights), 0) expect_lt(max(mcmc_sv$weights), Inf) + + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary")[-15], + run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary")[-15]) + + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary", + threads = 2L)[-17], + run_mcmc(model_bssm, iter = 100, particles = 10, + mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + output_type = "summary", + threads = 2L)[-17]) }) From c8cd1f04dc0a4dd198e49d2a9592da905379a4e9 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 16:01:43 +0300 Subject: [PATCH 037/180] more bsf tests --- R/models.R | 4 +-- tests/testthat/test_bootstrap_filter.R | 19 +++++++--- tests/testthat/test_particle_smoother.R | 46 +++++++++++++++++++++++++ 3 files changed, 63 insertions(+), 6 deletions(-) diff --git a/R/models.R b/R/models.R index 2569f708..4c42cb0f 100644 --- a/R/models.R +++ b/R/models.R @@ -1214,9 +1214,9 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, if(ncol(regression_part$xreg) > 1) { - priors <- c(list(rho, sigma, mu, phi), beta) + priors <- c(list(rho, sigma, mu, phi), regression_part$beta) } else { - priors <- list(rho, sigma, mu, phi, beta) + priors <- list(rho, sigma, mu, phi, regression_part$beta) } names(priors) <- c("rho", "sigma", "mu", "phi", names(regression_part$coefs)) diff --git a/tests/testthat/test_bootstrap_filter.R b/tests/testthat/test_bootstrap_filter.R index a1a44bc3..89a7e42b 100644 --- a/tests/testthat/test_bootstrap_filter.R +++ b/tests/testthat/test_bootstrap_filter.R @@ -32,6 +32,16 @@ test_that("Test that gaussian bsf still works", { expect_true(is.finite(bsf_ssm_ulg$logLik)) expect_true(is.finite(sum(bsf_ssm_ulg$att))) expect_true(is.finite(sum(bsf_ssm_ulg$Ptt))) + + expect_error(model_ar1_lg <- ar1_lg(y = 1:10, rho = tnormal(0.6, 0, 0.5, -1, 1), + sigma = gamma(1,2,2), sd_y = 0.1, mu = 1), NA) + expect_error(bsf_ar1_lg <- bootstrap_filter(model_ar1_lg, 10, seed = 1), + NA) + expect_gte(min(bsf_ar1_lg$weights), 0) + expect_lt(max(bsf_ar1_lg$weights), Inf) + expect_true(is.finite(bsf_ar1_lg$logLik)) + expect_true(is.finite(sum(bsf_ar1_lg$att))) + expect_true(is.finite(sum(bsf_ar1_lg$Ptt))) }) test_that("Test that poisson bsm_ng still works", { @@ -47,10 +57,12 @@ test_that("Test that poisson bsm_ng still works", { expect_true(is.finite(sum(bsf_poisson$Ptt))) }) -test_that("Test that binomial bsm_ng still works", { +test_that("Test that binomial ar1_ng still works", { - expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, - sd_slope = 2, P1 = diag(2, 2), + expect_error(model <- ar1_ng(c(1, 0, 1, 1, 1, 0, 0, 0), + rho = uniform(0.9, 0, 1), sigma = gamma(1, 2, 2), + mu = normal(1, 0, 1), + xreg = 1:8, beta = normal(0, 0, 0.1), distribution = "binomial"), NA) expect_error(bsf_binomial <- bootstrap_filter(model, 10, seed = 1), NA) @@ -63,7 +75,6 @@ test_that("Test that binomial bsm_ng still works", { }) - test_that("Test that negative binomial bsm_ng still works", { expect_error(model <- bsm_ng(c(1, 0, 1, 1, 1, 0, 0, 0), sd_level = 2, diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 9903bf2f..5b2ead61 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -96,3 +96,49 @@ test_that("Particle smoother for svm returns finite values", { expect_equal(out1$alphahat, out2$alphahat, tol = 0.1) }) +tol <- 1e-8 +test_that("Test that gaussian bsf smoother still works", { + + expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), + H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), + R = array(diag(2, 2), c(2, 2, 1)), + a1 = matrix(0, 2, 1), P1 = diag(2, 2), state_names = c("level", "slope")), + NA) + expect_error(bsf_ssm_ulg <- particle_smoother(model_ssm_ulg, 10, seed = 1, + method = "bsf"), + NA) + expect_gte(min(bsf_ssm_ulg$weights), 0) + expect_lt(max(bsf_ssm_ulg$weights), Inf) + expect_true(is.finite(bsf_ssm_ulg$logLik)) + expect_true(is.finite(sum(bsf_ssm_ulg$alphahat))) + expect_true(is.finite(sum(bsf_ssm_ulg$Vt))) + + expect_error(model_ar1_lg <- ar1_lg(y = 1:10, rho = tnormal(0.6, 0, 0.5, -1, 1), + sigma = gamma(1,2,2), sd_y = 0.1, mu = 1), NA) + expect_error(bsf_ar1_lg <- particle_smoother(model_ar1_lg, 10, seed = 1, + method = "bsf"), NA) + expect_gte(min(bsf_ar1_lg$weights), 0) + expect_lt(max(bsf_ar1_lg$weights), Inf) + expect_true(is.finite(bsf_ar1_lg$logLik)) + expect_true(is.finite(sum(bsf_ar1_lg$alphahat))) + expect_true(is.finite(sum(bsf_ar1_lg$Vt))) +}) + + +test_that("Test that binomial ar1_ng still works", { + + expect_error(model <- ar1_ng(c(1, 0, 1, 1, 1, 0, 0, 0), + rho = uniform(0.9, 0, 1), sigma = gamma(1, 2, 2), + mu = normal(1, 0, 1), + xreg = 1:8, beta = normal(0, 0, 0.1), + distribution = "binomial"), NA) + expect_error(bsf_binomial <- particle_smoother(model, 10, method = "bsf", + seed = 1), NA) + + expect_gte(min(bsf_binomial$weights), 0) + expect_lt(max(bsf_binomial$weights), Inf) + expect_true(is.finite(bsf_binomial$logLik)) + expect_true(is.finite(sum(bsf_binomial$alphahat))) + expect_true(is.finite(sum(bsf_binomial$Vt))) + +}) \ No newline at end of file From 502e44bef1cbc3068326e268b3f5d0e9784d2664 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 3 Sep 2021 23:48:16 +0300 Subject: [PATCH 038/180] increase testing --- tests/testthat/test_approx.R | 24 ++++ tests/testthat/test_mcmc.R | 233 ++++++++++++++++++++++++---------- tests/testthat/test_predict.R | 40 ++++++ tests/testthat/test_sde.R | 10 ++ 4 files changed, 241 insertions(+), 66 deletions(-) diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index 978dbbc8..a25afb70 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -138,6 +138,8 @@ test_that("Two iid model gives same results as two univariate models", { test_that("Gaussian approximation works for nonlinear models", { + skip_on_cran() + pntrs <- nlg_example_models("linear_gaussian") set.seed(1) y <- cumsum(rnorm(10)) + rnorm(10) @@ -151,4 +153,26 @@ test_that("Gaussian approximation works for nonlinear models", { logLik(model_nlg, method = "ekf", particles = 0), logLik(gaussian_approx(model_nlg))) expect_equal(logLik(model_gaussian), logLik(gaussian_approx(model_nlg))) + + set.seed(1) + n <- 30 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + y[2:5] <- NA + pntrs <- nlg_example_models("sin_exp") + + expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + + expect_equal(gaussian_approx(model_nlg), + gaussian_approx(model_nlg, max_iter = 2)) + }) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 1c453389..5f591f0e 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -9,19 +9,21 @@ test_that("MCMC results for Gaussian model are correct", { expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, seed = 1), NA) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1)[-14], + expect_equal( + run_mcmc(model_bssm, iter = 100, seed = 1)[-14], run_mcmc(model_bssm, iter = 100, seed = 1)[-14]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, - output_type = "summary")[-15], + expect_equal( + run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")[-15], run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")[-15]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, - output_type = "theta")[-13], + expect_equal( + run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")[-13], run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")[-13]) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, - output_type = "theta")$theta, + expect_equal( + run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "theta")$theta, run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")$theta) - expect_equal(run_mcmc(model_bssm, iter = 100, seed = 1, - output_type = "theta")$acceptance_rate, + expect_equal( + run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "theta")$acceptance_rate, run_mcmc(model_bssm, iter = 100, seed = 1, output_type = "summary")$acceptance_rate) @@ -30,10 +32,115 @@ test_that("MCMC results for Gaussian model are correct", { expect_lt(max(mcmc_bsm$theta), Inf) expect_true(is.finite(sum(mcmc_bsm$alpha))) + set.seed(1) + n <- 20 + x1 <- rnorm(n) + x2 <- rnorm(n) + b1 <- 1 + cumsum(rnorm(n, sd = 0.5)) + b2 <- 2 + cumsum(rnorm(n, sd = 0.1)) + y <- 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) + + Z <- rbind(1, x1, x2) + H <- 0.1 + T <- diag(3) + R <- diag(c(0, 1, 0.1)) + a1 <- rep(0, 3) + P1 <- diag(10, 3) + + # updates the model given the current values of the parameters + update_fn <- function(theta) { + R <- diag(c(0, theta[1], theta[2])) + dim(R) <- c(3, 3, 1) + list(R = R, H = theta[3]) + } + # prior for standard deviations as half-normal(1) + prior_fn <- function(theta) { + if(any(theta < 0)) { + log_p <- -Inf + } else { + log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) + } + log_p + } + + model <- ssm_ulg(y, Z, H, T, R, a1, P1, + init_theta = c(1, 0.1, 0.1), + update_fn = update_fn, prior_fn = prior_fn) + + expect_error(out <- run_mcmc(model, iter = 50, seed = 1), NA) + + expect_gt(out$acceptance_rate, 0) + expect_gte(min(out$theta), 0) + expect_lt(max(out$theta), Inf) + expect_true(is.finite(sum(out$alpha))) + + expect_equal( + run_mcmc(model, iter = 100, seed = 1)[-14], + run_mcmc(model, iter = 100, seed = 1)[-14]) + expect_equal( + run_mcmc(model, iter = 100, seed = 1, output_type = "summary")[-15], + run_mcmc(model, iter = 100, seed = 1, output_type = "summary")[-15]) + expect_equal( + run_mcmc(model, iter = 100, seed = 1, output_type = "theta")[-13], + run_mcmc(model, iter = 100, seed = 1, output_type = "theta")[-13]) + expect_equal( + run_mcmc(model, iter = 100, seed = 1, output_type = "theta")$theta, + run_mcmc(model, iter = 100, seed = 1, output_type = "summary")$theta) + expect_equal( + run_mcmc(model, iter = 100, seed = 1, + output_type = "theta")$acceptance_rate, + run_mcmc(model, iter = 100, seed = 1, + output_type = "summary")$acceptance_rate) }) +test_that("MCMC for ssm_mng work", { + + set.seed(1) + n <- 20 + x <- cumsum(rnorm(n, sd = 0.5)) + phi <- 2 + y <- cbind(rgamma(n, shape = phi, scale = exp(x) / phi), + rbinom(n, 1, plogis(x))) + + Z <- matrix(1, 2, 1) + T <- 1 + R <- 0.5 + a1 <- 0 + P1 <- 1 + + update_fn <- function(theta) { + list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) + } + + prior_fn <- function(theta) { + ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) + } + + expect_error(model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), + init_theta = c(0.5, 2), + distribution = c("gamma", "binomial"), + update_fn = update_fn, prior_fn = prior_fn), NA) + + for(type in c("pm", "da", "is1", "is3", "is3", "approx")) { + for(method in c("psi", "bsf", "spdk")) { + for(output in c("full", "summary", "theta")) { + expect_error( + out <- run_mcmc(model, mcmc_type = type, sampling_method = method, + output_type = output, iter = 1000, seed = 1, particles = 10), NA) + expect_equal(sum(is.na(out$theta)), 0) + expect_equal(sum(is.na(out$alpha)), 0) + expect_equal(sum(!is.finite(out$theta)), 0) + expect_equal(sum(!is.finite(out$alpha)), 0) + expect_equal(sum(!is.finite(out$posterior)), 0) + expect_gt(out$acceptance_rate, 0) + expect_gte(min(out$theta), 0) + } + } + } +}) -test_that("DA-MCMC results for Poisson model are correct", { +test_that("MCMC results with psi-APF for Poisson model are correct", { + set.seed(123) model_bssm <- bsm_ng(rpois(10, exp(0.2) * (2:11)), P1 = diag(2, 2), sd_slope = 0, sd_level = uniform(2, 0, 10), u = 2:11, @@ -42,36 +149,40 @@ test_that("DA-MCMC results for Poisson model are correct", { expect_error(mcmc_poisson <- run_mcmc(model_bssm, mcmc_type = "da", iter = 100, particles = 5, seed = 42), NA) + expect_gt(mcmc_poisson$acceptance_rate, 0) + expect_gte(min(mcmc_poisson$theta), 0) + expect_lt(max(mcmc_poisson$theta), Inf) + expect_true(is.finite(sum(mcmc_poisson$alpha))) + expect_warning(summary(mcmc_poisson, only_theta = TRUE)) sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) expect_lt(sum(abs(sumr$theta - c(0.25892090511681, 0.186796779799571))), 0.5) - states <- expand_sample(mcmc_poisson, variable = "states") expect_equal(as.numeric(sumr$states$Mean[,1]), as.numeric(colMeans(states$level))) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - particles = 5)[-14], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - particles = 5)[-14]) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - output_type = "summary", particles = 5)[-15], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - output_type = "summary", particles = 5)[-15]) - expect_equal(run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - output_type = "theta", particles = 5)[-13], - run_mcmc(model_bssm, mcmc_type = "da", iter = 100, seed = 1, - output_type = "theta", particles = 5)[-13]) - - expect_gt(mcmc_poisson$acceptance_rate, 0) - expect_gte(min(mcmc_poisson$theta), 0) - expect_lt(max(mcmc_poisson$theta), Inf) - expect_true(is.finite(sum(mcmc_poisson$alpha))) - + for(type in c("pm", "da", "is1", "is3", "is3", "approx")) { + z <- 2*type%in%c("is1", "is3", "is3", "approx") + expect_equal( + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + particles = 5)[-14 - z], + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + particles = 5)[-14 - z]) + expect_equal( + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + output_type = "summary", particles = 5)[-15 - z], + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + output_type = "summary", particles = 5)[-15 - z]) + expect_equal( + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + output_type = "theta", particles = 5)[-13 - z], + run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, + output_type = "theta", particles = 5)[-13 - z]) + } }) @@ -163,51 +274,41 @@ test_that("MCMC for nonlinear models work", { n_states = 1, n_etas = 1, state_names = "state"), NA) - expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is1", seed = 1, sampling_mcmc_type = "psi")[-16], - run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is1", seed = 1, sampling_mcmc_type = "psi")[-16]) - - expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf")[-16], - run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf")[-16]) - - expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is3", seed = 1, sampling_mcmc_type = "ekf")[-16], - run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is3", seed = 1, sampling_mcmc_type = "ekf")[-16]) + for(type in c("pm", "da", "is1", "is3", "is3", "approx", "ekf")) { + for(method in c("psi", "bsf", "ekf")) { + for(output in c("full", "summary", "theta")) { + if(type %in% c("is1", "is2", "is3") && method == "ekf") { + expect_error(run_mcmc(model_nlg, mcmc_type = type, sampling_method = method, + output_type = output, iter = 100, seed = 1, particles = 5)) + } else { + expect_error( + run_mcmc(out <- model_nlg, mcmc_type = type, sampling_method = method, + output_type = output, iter = 100, seed = 1, particles = 5), NA) + expect_equal(sum(is.na(out$theta)), 0) + expect_equal(sum(is.na(out$alpha)), 0) + expect_equal(sum(!is.finite(out$theta)), 0) + expect_equal(sum(!is.finite(out$alpha)), 0) + expect_equal(sum(!is.finite(out$posterior)), 0) + } + } + } + } expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + mcmc_type = "is2", seed = 1, sampling_method = "psi", threads = 2L)[-16], run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + mcmc_type = "is2", seed = 1, sampling_method = "psi", threads = 2L)[-16]) - - expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", - output_type = "summary")[-15], - run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "pm", seed = 1, sampling_mcmc_type = "psi", - output_type = "summary")[-15]) - - expect_equal(run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", - output_type = "summary", - threads = 2L)[-17], + expect_equal( + run_mcmc(model_nlg, iter = 100, particles = 10, + mcmc_type = "is1", seed = 1, sampling_method = "psi", + output_type = "summary", + threads = 2L)[-17], run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "is2", seed = 1, sampling_mcmc_type = "psi", + mcmc_type = "is1", seed = 1, sampling_method = "psi", output_type = "summary", threads = 2L)[-17]) - expect_error(out<-run_mcmc(model_nlg, iter = 100, particles = 10, - mcmc_type = "da", seed = 1, sampling_mcmc_type = "bsf"), NA) - - expect_equal(sum(is.na(out$theta)), 0) - expect_equal(sum(is.na(out$alpha)), 0) - expect_equal(sum(!is.finite(out$theta)), 0) - expect_equal(sum(!is.finite(out$alpha)), 0) - }) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 50dc6f32..ec187177 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -28,3 +28,43 @@ test_that("Gaussian predictions work", { expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) }) + + +test_that("Predictions for nlg_ssm work", { + skip_on_cran() + set.seed(1) + n <- 10 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + + pntrs <- nlg_example_models("sin_exp") + + expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + + expect_error(mcmc_results <- run_mcmc(model, iter = 1000, particles = 10), + NA) + future_model <- model + future_model$y <- rep(NA, 3) + expect_error(pred <- predict(mcmc_results, particles = 10, + future_model, type = "mean", nsim = 1000), NA) + + expect_gt(mean(pred$value[pred$time == 3]), 0) + expect_lt(mean(pred$value[pred$time == 3]), 1) + + # Posterior predictions for past observations: + yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100) + meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100) + + expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) +}) diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index 9a36e834..6eb1e416 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -98,7 +98,10 @@ test_that("MCMC for SDE works", { expect_error(out <- run_mcmc(model, iter = 500, particles = 10, mcmc_type = "pm", L_f = 2), NA) + expect_gt(out$acceptance_rate, 0) + expect_error(out <- run_mcmc(model, iter = 500, + particles = 10, mcmc_type = "da", L_c = 2, LL_f = 3), NA) expect_gt(out$acceptance_rate, 0) expect_error(out2 <- run_mcmc(model, iter = 500, @@ -106,4 +109,11 @@ test_that("MCMC for SDE works", { expect_gt(out2$acceptance_rate, 0) expect_equal(mean(colMeans(out$theta)-colMeans(out2$theta)), 0, tol = 1) + + expect_error(out2 <- run_mcmc(model, iter = 500, + particles = 10, mcmc_type = "is1", L_c = 1, L_f = 2, threads = 2), NA) + + expect_gt(out2$acceptance_rate, 0) + expect_equal(mean(colMeans(out$theta)-colMeans(out2$theta)), 0, tol = 1) + }) From c1de6bfe87a36d3e5f15d2f4ce3fa574d9801423 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 00:20:50 +0300 Subject: [PATCH 039/180] fix tests... --- tests/testthat/test_predict.R | 2 +- tests/testthat/test_sde.R | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index ec187177..d5ec8b03 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -43,7 +43,7 @@ test_that("Predictions for nlg_ssm work", { pntrs <- nlg_example_models("sin_exp") - expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, theta = c(log_H = log(0.1), log_R = log(0.1)), diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index 6eb1e416..39243747 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -101,7 +101,7 @@ test_that("MCMC for SDE works", { expect_gt(out$acceptance_rate, 0) expect_error(out <- run_mcmc(model, iter = 500, - particles = 10, mcmc_type = "da", L_c = 2, LL_f = 3), NA) + particles = 10, mcmc_type = "da", L_c = 2, L_f = 3), NA) expect_gt(out$acceptance_rate, 0) expect_error(out2 <- run_mcmc(model, iter = 500, From ae159f9b1f9047f78dc1b7bd510c8f44a930540d Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 09:22:59 +0300 Subject: [PATCH 040/180] n_states as integer --- R/models.R | 2 ++ tests/testthat/test_predict.R | 6 +++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/R/models.R b/R/models.R index 4c42cb0f..235bf8a4 100644 --- a/R/models.R +++ b/R/models.R @@ -1392,6 +1392,8 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, if(missing(n_etas)) { n_etas <- n_states } + n_states <- as.integer(n_states) + n_etas <- as.integer(n_etas) structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, Z_gn = Z_gn, T_gn = T_gn, a1 = a1, P1 = P1, theta = theta, log_prior_pdf = log_prior_pdf, known_params = known_params, diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index d5ec8b03..52dd8dac 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -50,15 +50,15 @@ test_that("Predictions for nlg_ssm work", { log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state"), NA) - expect_error(mcmc_results <- run_mcmc(model, iter = 1000, particles = 10), + expect_error(mcmc_results <- run_mcmc(model, iter = 5000, particles = 10), NA) future_model <- model future_model$y <- rep(NA, 3) expect_error(pred <- predict(mcmc_results, particles = 10, future_model, type = "mean", nsim = 1000), NA) - expect_gt(mean(pred$value[pred$time == 3]), 0) - expect_lt(mean(pred$value[pred$time == 3]), 1) + expect_gt(mean(pred$value[pred$time == 3]), 0.5) + expect_lt(mean(pred$value[pred$time == 3]), 1.5) # Posterior predictions for past observations: yrep <- predict(mcmc_results, model, type = "response", From 985eee43de9ad7d4c213799dd7eaf4adb8c516ca Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 11:10:59 +0300 Subject: [PATCH 041/180] tweak argument checks, more tests --- R/check_arguments.R | 2 +- R/models.R | 15 +++++------ tests/testthat/test_as_data_frame.R | 9 ++++++- tests/testthat/test_bootstrap_filter.R | 3 ++- tests/testthat/test_mcmc.R | 12 +++++---- tests/testthat/test_models.R | 33 +++++++++++++++++++++---- tests/testthat/test_particle_smoother.R | 7 +++--- tests/testthat/test_predict.R | 26 +++++++++++++++++++ 8 files changed, 84 insertions(+), 23 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index a3335592..f4f2a7a7 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -6,7 +6,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { stop("Argument y must be a numeric matrix or multivariate ts object.") } } else { - if (!is.numeric(unclass(x))) { + if (!is.numeric(unclass(x)) || !is.vector(unclass(x))) { stop("Argument y must be a numeric vector or ts object.") } if (distribution != "gaussian" && any(na.omit(x) < 0)) { diff --git a/R/models.R b/R/models.R index 235bf8a4..f4800ec1 100644 --- a/R/models.R +++ b/R/models.R @@ -592,7 +592,8 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' If missing, the seasonal component is omitted from the model. #' @param xreg Matrix containing covariates. #' @param beta Prior for the regression coefficients. -#' @param period Length of the seasonal component i.e. the number of +#' @param period Length of the seasonal pattern. +#' Default is \code{frequency(y)}. Must be at least 3. #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. #' @param P1 Prior covariance for the initial states (level, slope, seasonals). @@ -669,8 +670,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, seasonal <- FALSE sd_seasonal <- NULL } else { - if (period < 2) { - stop("Period of seasonal component must be larger than 1. ") + if (period < 3) { + stop("Period of seasonal component must be larger than 2. ") } if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") @@ -807,8 +808,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' term. For binomial, this is the number of trials. #' @param beta Prior for the regression coefficients. #' @param xreg Matrix containing covariates. -#' @param period Length of the seasonal component i.e. the number of -#' observations per season. Default is \code{frequency(y)}. +#' @param period Length of the seasonal pattern. +#' Default is \code{frequency(y)}. Must be at least 3. #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. #' @param P1 Prior covariance for the initial states (level, slope, seasonals). @@ -888,8 +889,8 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, seasonal <- FALSE sd_seasonal <- NULL } else { - if (period < 2) { - stop("Period of seasonal component must be larger than 1. ") + if (period < 3) { + stop("Period of seasonal component must be larger than 2. ") } if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") diff --git a/tests/testthat/test_as_data_frame.R b/tests/testthat/test_as_data_frame.R index c3a7e604..814318be 100644 --- a/tests/testthat/test_as_data_frame.R +++ b/tests/testthat/test_as_data_frame.R @@ -31,11 +31,18 @@ test_that("expanded and not expanded data frame work equally for states", { expect_equal(mean(d$value[d$variable == "slope" & d$time == 3]), weighted.mean(mcmc_bsm$alpha[3, 2, ], mcmc_bsm$counts)) - d <- expect_error(as.data.frame(mcmc_bsm, variable = "state", + expect_error(d <- as.data.frame(mcmc_bsm, variable = "state", expand = FALSE), NA) expect_equal(colnames(d), c("iter", "value", "variable", "time", "weight")) expect_equal(unique(d$variable), c("level", "slope")) expect_equal(weighted.mean(d$value[d$variable == "slope" & d$time == 3], d$weight[d$variable == "slope" & d$time == 3]), weighted.mean(mcmc_bsm$alpha[3, 2, ], mcmc_bsm$counts)) + + expect_error(d <- as.data.frame(mcmc_bsm, variable = "theta"), NA) + expect_error(sumr <- summary(mcmc_bsm, variable = "both", return_se = TRUE), + NA) + expect_identical(mean(d$value[d$variable == "sd_y"]), + sumr$theta["sd_y", "Mean"]) + }) diff --git a/tests/testthat/test_bootstrap_filter.R b/tests/testthat/test_bootstrap_filter.R index 89a7e42b..561563aa 100644 --- a/tests/testthat/test_bootstrap_filter.R +++ b/tests/testthat/test_bootstrap_filter.R @@ -33,7 +33,8 @@ test_that("Test that gaussian bsf still works", { expect_true(is.finite(sum(bsf_ssm_ulg$att))) expect_true(is.finite(sum(bsf_ssm_ulg$Ptt))) - expect_error(model_ar1_lg <- ar1_lg(y = 1:10, rho = tnormal(0.6, 0, 0.5, -1, 1), + expect_error(model_ar1_lg <- ar1_lg(y = 1:10, + rho = tnormal(0.6, 0, 0.5, -1, 1), sigma = gamma(1,2,2), sd_y = 0.1, mu = 1), NA) expect_error(bsf_ar1_lg <- bootstrap_filter(model_ar1_lg, 10, seed = 1), NA) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 5f591f0e..c1fc8b10 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -227,7 +227,7 @@ test_that("MCMC results for SV model using IS-correction are correct", { mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf"), NA) expect_warning(expand_sample(mcmc_sv)) - + sumr <- expect_error(summary(mcmc_sv, variable = "both"), NA) expect_gt(mcmc_sv$acceptance_rate, 0) expect_true(is.finite(sum(mcmc_sv$theta))) expect_true(is.finite(sum(mcmc_sv$alpha))) @@ -278,12 +278,14 @@ test_that("MCMC for nonlinear models work", { for(method in c("psi", "bsf", "ekf")) { for(output in c("full", "summary", "theta")) { if(type %in% c("is1", "is2", "is3") && method == "ekf") { - expect_error(run_mcmc(model_nlg, mcmc_type = type, sampling_method = method, - output_type = output, iter = 100, seed = 1, particles = 5)) + expect_error(run_mcmc(model_nlg, mcmc_type = type, + sampling_method = method, output_type = output, iter = 100, + seed = 1, particles = 5)) } else { expect_error( - run_mcmc(out <- model_nlg, mcmc_type = type, sampling_method = method, - output_type = output, iter = 100, seed = 1, particles = 5), NA) + run_mcmc(out <- model_nlg, mcmc_type = type, + sampling_method = method, output_type = output, iter = 100, + seed = 1, particles = 5), NA) expect_equal(sum(is.na(out$theta)), 0) expect_equal(sum(is.na(out$alpha)), 0) expect_equal(sum(!is.finite(out$theta)), 0) diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index 189d8d10..bc16b8da 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -27,17 +27,26 @@ test_that("proper arguments for bsm don't throw an error", { test_that("bad argument values for bsm_ng throws an error", { expect_error(bsm_ng("character vector", distribution = "poisson")) - expect_error(bsm_ng(matrix(0, 2, 2), distribution = "poisson")) + expect_error(bsm_ng(1:10, distribution = "poisson")) + expect_error(bsm_ng(diag(2), distribution = "poisson", + sd_level = 1)) expect_error(bsm_ng(1, distribution = "poisson")) expect_error(bsm_ng(c(1, Inf), distribution = "poisson")) expect_error(bsm_ng(1:10, sd_level = "character", distribution = "poisson")) - expect_error(bsm_ng(1:10, sd_y = Inf, distribution = "poisson")) + expect_error(bsm_ng(1:10, sd_level = Inf, distribution = "poisson")) expect_error(bsm_ng(1:10, no_argument = 5, distribution = "poisson")) - expect_error(bsm_ng(1:10, xreg = matrix(1:20), beta = uniform(0, 0, 1), + expect_error(bsm_ng(1:10, 1, 1, xreg = matrix(1:20), beta = uniform(0, 0, 1), + distribution = "poisson")) + expect_error(bsm_ng(1:10, 1, 1, xreg = matrix(Inf, 10, 1), + beta = uniform(0, 0, 1), distribution = "poisson")) + expect_error(bsm_ng(1:10, 1, 1, xreg = 1:10, beta = NA, distribution = "poisson")) - expect_error(bsm_ng(1:10, xreg = 1:10, beta = NA, distribution = "poisson")) expect_error(bsm_ng(1:10, 1, 1, a1 = "a", distribution = "poisson")) - expect_error(bsm_ng(1:10, 1, 1, 1, 1, distribution = "poisson")) + expect_error(bsm_ng(1:2, 1, 1, 1, distribution = "poisson", period = 2)) + expect_error(bsm_ng(-(1:2), 1, 1, distribution = "poisson")) + expect_error(bsm_ng(1:2 + 0.1, 1, 1,distribution = "poisson")) + expect_error(bsm_ng(1:2, 1, sd_y = halfnormal(0, 1:2), + distribution = "poisson")) }) test_that("proper arguments for ng_bsm don't throw an error", { @@ -78,6 +87,20 @@ test_that("multivariate non-gaussian model", { ufun <- function(theta) { list(R = array(diag(exp(theta)), c(2, 2, 1))) } + + expect_error(mng_model <- ssm_mng(y = data.frame(1:4,1:4), Z = diag(2), T = diag(2), + R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", + init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun)) + + expect_error(mng_model <- ssm_mng(y = y - 10, Z = diag(2), T = diag(2), + R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", + init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun)) + + expect_error(ssm_mng(y = y + 0.1, Z = diag(2), T = diag(2), + R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", + init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun)) + + expect_error(mng_model <- ssm_mng(y = y, Z = diag(2), T = diag(2), R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun), NA) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 5b2ead61..93b260d4 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -10,7 +10,7 @@ test_that("Test that particle_smoother for LGSSM works as Kalman smoother", { particle_smoother(model_bsm, 1e5, seed = 1)$alphahat, tolerance = 1e-2) }) -test_that("Test that BSF and PSI particle_smoother for LGSSM are with MC error", { +test_that("Test that BSF&PSI particle_smoother for LGSSM are with MC error", { expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, sd_y = 0.01, a1 = c(1, 0), P1 = diag(0.01, 2)), NA) @@ -113,7 +113,8 @@ test_that("Test that gaussian bsf smoother still works", { expect_true(is.finite(sum(bsf_ssm_ulg$alphahat))) expect_true(is.finite(sum(bsf_ssm_ulg$Vt))) - expect_error(model_ar1_lg <- ar1_lg(y = 1:10, rho = tnormal(0.6, 0, 0.5, -1, 1), + expect_error(model_ar1_lg <- ar1_lg(y = 1:10, + rho = tnormal(0.6, 0, 0.5, -1, 1), sigma = gamma(1,2,2), sd_y = 0.1, mu = 1), NA) expect_error(bsf_ar1_lg <- particle_smoother(model_ar1_lg, 10, seed = 1, method = "bsf"), NA) @@ -141,4 +142,4 @@ test_that("Test that binomial ar1_ng still works", { expect_true(is.finite(sum(bsf_binomial$alphahat))) expect_true(is.finite(sum(bsf_binomial$Vt))) -}) \ No newline at end of file +}) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 52dd8dac..939b95e3 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -29,6 +29,32 @@ test_that("Gaussian predictions work", { }) +test_that("Non-gaussian predictions work", { + + set.seed(1) + y <- rpois(10, exp(cumsum(rnorm(10, 0, 0.1)))) + model <- ar1_ng(y, + rho = uniform(0.9, 0, 0.99), mu = 0, + sigma = halfnormal(0.1, 1), distribution = "poisson") + + expect_error(mcmc_results <- run_mcmc(model, iter = 1000, particles = 5), NA) + future_model <- model + future_model$y <- rep(NA, 3) + expect_error(pred <- predict(mcmc_results, future_model, type = "mean", + nsim = 100), NA) + + expect_gt(mean(pred$value[pred$time == 3]), 1) + expect_lt(mean(pred$value[pred$time == 3]), 2.5) + + # Posterior predictions for past observations: + yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100) + meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100) + + expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + +}) test_that("Predictions for nlg_ssm work", { skip_on_cran() From 7a9d5009a145788e2cf0cc12d4d901b8ca622928 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 12:52:20 +0300 Subject: [PATCH 042/180] fix check_y, add more prediction tests --- R/check_arguments.R | 34 +++++++++++++++++++++++---------- R/models.R | 20 +++++++++---------- tests/testthat/test_predict.R | 36 +++++++++++++++++++++++++++++++++++ 3 files changed, 70 insertions(+), 20 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index f4f2a7a7..b6b9c293 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -3,11 +3,27 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(!is.na(x))) { if (multivariate) { if (!is.matrix(x)) { - stop("Argument y must be a numeric matrix or multivariate ts object.") + stop("Argument y must be a matrix or multivariate ts object.") + } + if (nrow(x) < 2) { + stop("Number of rows in y, i.e. number of time points, must be > 1. ") } } else { - if (!is.numeric(unclass(x)) || !is.vector(unclass(x))) { - stop("Argument y must be a numeric vector or ts object.") + if (!is.vector(x) || is.list(x)) { + if (is.ts(x) || is.matrix(x)) { + if (ncol(x) == 1 || length(dim(x)) < 3) { + dim(x) <- NULL + } else { + if(ncol(x) > 1) { + stop("Argument y must be a vector or univariate ts object.") + } + } + } else { + stop("Argument y must be a vector or univariate ts object.") + } + } + if (length(x) < 2) { + stop("Length of argument y, i.e. number of time points, must be > 1.") } if (distribution != "gaussian" && any(na.omit(x) < 0)) { stop(paste0("Negative values not allowed for ", distribution, @@ -24,10 +40,8 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(is.infinite(x))) { stop("Argument y must contain only finite or NA values.") } - if (length(x) < 2) { - stop("Length of argument y must be at least two.") - } } + x } check_distribution <- function(x, distribution) { @@ -173,7 +187,7 @@ check_C <- function(x, m, n) { if(!is.numeric(x)) stop("'C' must be numeric. ") if (is.null(dim(x)) || nrow(x) != m || !(ncol(x) %in% c(1, n))) { stop(paste("'C' must be m x 1 or m x n matrix, where m is", - "the number of states.", sep = " ")) + "the number of states.", sep = " ")) } } x @@ -220,7 +234,7 @@ check_Z <- function(x, p, n, multivariate = FALSE) { } else { if (!(dim(x)[2] %in% c(1, NA, n))) { stop(paste("'Z' must be a (m x 1) or (m x n) matrix, where", - "m is the number of states and n is the length of the series. ", + "m is the number of states and n is the length of the series. ", sep = " ")) } else { dim(x) <- @@ -230,8 +244,8 @@ check_Z <- function(x, p, n, multivariate = FALSE) { } else { if (dim(x)[1] != p || !(dim(x)[3] %in% c(1, NA, n))) { stop(paste("'Z' must be a (p x m) matrix or (p x m x n) array", - "where p is the number of series, m is the number of states,", - "and n is the length of the series. ", sep = " ")) + "where p is the number of series, m is the number of states,", + "and n is the length of the series. ", sep = " ")) } else { dim(x) <- c(p, dim(x)[2], (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) diff --git a/R/models.R b/R/models.R index f4800ec1..f236310b 100644 --- a/R/models.R +++ b/R/models.R @@ -183,7 +183,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { - check_y(y) + y <- check_y(y) n <- length(y) # create Z @@ -305,7 +305,7 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, distribution <- match.arg(distribution, c("poisson", "binomial", "negative binomial", "gamma")) - check_y(y, distribution = distribution) + y <- check_y(y, distribution = distribution) n <- length(y) Z <- check_Z(Z, 1L, n) m <- dim(Z)[1] @@ -419,7 +419,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, update_fn = default_update_fn, prior_fn = default_prior_fn) { # create y - check_y(y, multivariate = TRUE) + y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) @@ -519,7 +519,7 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, # create y - check_y(y, multivariate = TRUE) + y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) if(length(distribution) == 1) distribution <- rep(distribution, p) @@ -618,7 +618,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, D = NULL, C = NULL) { - check_y(y) + y <- check_y(y) n <- length(y) regression_part <- create_regression(beta, xreg, n) @@ -852,7 +852,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, distribution <- match.arg(distribution, c("poisson", "binomial", "negative binomial", "gamma")) - check_y(y, multivariate = FALSE, distribution) + y <- check_y(y, multivariate = FALSE, distribution) n <- length(y) regression_part <- create_regression(beta, xreg, n) @@ -1082,7 +1082,7 @@ svm <- function(y, mu, rho, sd_ar, sigma) { stop("Define either sigma or mu, but not both.") } - check_y(y) + y <- check_y(y) check_rho(rho$init) check_sd(sd_ar$init, "rho") @@ -1163,7 +1163,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, distribution <- match.arg(distribution, c("poisson", "binomial", "negative binomial", "gamma")) - check_y(y, multivariate = FALSE, distribution) + y <- check_y(y, multivariate = FALSE, distribution) n <- length(y) regression_part <- create_regression(beta, xreg, n) @@ -1268,7 +1268,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, #' summary(out, return_se = TRUE) ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { - check_y(y) + y <- check_y(y) n <- length(y) regression_part <- create_regression(beta, xreg, n) @@ -1471,7 +1471,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive) { - check_y(y) + y <- check_y(y) structure(list(y = as.ts(y), drift = drift, diffusion = diffusion, diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 939b95e3..a9dc19ca 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -37,6 +37,7 @@ test_that("Non-gaussian predictions work", { rho = uniform(0.9, 0, 0.99), mu = 0, sigma = halfnormal(0.1, 1), distribution = "poisson") + set.seed(123) expect_error(mcmc_results <- run_mcmc(model, iter = 1000, particles = 5), NA) future_model <- model future_model$y <- rep(NA, 3) @@ -54,6 +55,41 @@ test_that("Non-gaussian predictions work", { expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + update_fn <- function(x) { + T <- array(x[1]) + R <- array(x[2]) + dim(T) <- dim(R) <- c(1, 1, 1) + P1 <- matrix(x[2]^2) / (1 - x[1]^2) + list(T = T, R = R, P1 = P1) + } + prior_fn <- function(x) { + ifelse(x[1] < 0 | x[1] > 0.99 | x[2] < 0, -Inf, - 0.5 * x[2]^2) + } + model2 <- ssm_ung(model$y, Z = 1, T = model$T, R = model$R, a1 = model$a1, + P1 = model$P1, distribution = "poisson", update_fn = update_fn, + prior_fn = prior_fn, init_theta = model$theta, state_names = "signal") + + set.seed(123) + expect_error(mcmc_results2 <- run_mcmc(model2, iter = 1000, particles = 5), + NA) + expect_equal(mcmc_results$theta, mcmc_results2$theta) + expect_equal(mcmc_results$alpha, mcmc_results2$alpha) + expect_equal(mcmc_results$posterior, mcmc_results2$posterior) + + future_model2 <- model2 + future_model2$y <- rep(NA, 3) + expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", + nsim = 100), NA) + expect_equal(pred, pred2) + # Posterior predictions for past observations: + yrep2 <- predict(mcmc_results2, model2, type = "response", + future = FALSE, nsim = 100) + meanrep2 <- predict(mcmc_results2, model2, type = "mean", + future = FALSE, nsim = 100) + expect_equal(yrep, yrep2) + expect_equal(meanrep, meanrep2) + expect_error(predict(mcmc_results2, model, type = "response", + future = FALSE, nsim = 100)) }) test_that("Predictions for nlg_ssm work", { From ada45555d1d8fa33613adff638d75c21cae38e9c Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 13:39:05 +0300 Subject: [PATCH 043/180] fix check_y for matrix case, added examples --- R/check_arguments.R | 2 +- R/models.R | 66 +++++++++++++++++++++++++++++++++++ R/predict.R | 4 +-- R/run_mcmc.R | 8 ++--- tests/testthat/test_predict.R | 4 +-- 5 files changed, 75 insertions(+), 9 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index b6b9c293..0146536a 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -11,7 +11,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } else { if (!is.vector(x) || is.list(x)) { if (is.ts(x) || is.matrix(x)) { - if (ncol(x) == 1 || length(dim(x)) < 3) { + if (ncol(x) == 1 && length(dim(x)) < 3) { dim(x) <- NULL } else { if(ncol(x) > 1) { diff --git a/R/models.R b/R/models.R index f236310b..2cf1cd49 100644 --- a/R/models.R +++ b/R/models.R @@ -513,6 +513,40 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' @param state_names Names for the states. #' @return Object of class \code{ssm_mng}. #' @export +#' @examples +#' +#' set.seed(1) +#' n <- 20 +#' x <- cumsum(rnorm(n, sd = 0.5)) +#' phi <- 2 +#' y <- cbind( +#' rgamma(n, shape = phi, scale = exp(x) / phi), +#' rbinom(n, 10, plogis(x))) +#' +#' Z <- matrix(1, 2, 1) +#' T <- 1 +#' R <- 0.5 +#' a1 <- 0 +#' P1 <- 1 +#' +#' update_fn <- function(theta) { +#' list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) +#' } +#' +#' prior_fn <- function(theta) { +#' ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) +#' } +#' +#' model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), +#' init_theta = c(0.5, 2), +#' distribution = c("gamma", "binomial"), +#' u = cbind(1, rep(10, n)), +#' update_fn = update_fn, prior_fn = prior_fn) +#' +#' # smoothing based on approximating gaussian model +#' ts.plot(cbind(y, fast_smoother(model)), +#' col = 1:3, lty = c(1, 1, 2)) +#' ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, u = 1, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { @@ -1157,6 +1191,15 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' @return Object of class \code{ar1_ng}. #' @export #' @rdname ar1_ng +#' @examples +#' model <- ar1_ng(discoveries, rho = uniform(0.5,-1,1), +#' sigma = halfnormal(0.1, 1), mu = normal(0, 0, 1), +#' distribution = "poisson") +#' out <- run_mcmc(model, iter = 1e4, mcmc_type = "approx", +#' output_type = "summary") +#' +#' ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) +#' ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) { @@ -1381,6 +1424,29 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @param state_names Names for the states. #' @return Object of class \code{ssm_nlg}. #' @export +#' @examples +#' \dontrun{ +#' set.seed(1) +#' n <- 50 +#' x <- y <- numeric(n) +#' y[1] <- rnorm(1, exp(x[1]), 0.1) +#' for(i in 1:(n-1)) { +#' x[i+1] <- rnorm(1, sin(x[i]), 0.1) +#' y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +#' } +#' +#' pntrs <- nlg_example_models("sin_exp") +#' +#' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, +#' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, +#' Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, +#' theta = c(log_H = log(0.1), log_R = log(0.1)), +#' log_prior_pdf = pntrs$log_prior_pdf, +#' n_states = 1, n_etas = 1, state_names = "state") +#' +#' out <- ekf(model_nlg, 100) +#' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +#' } ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, known_params = NA, known_tv_params = matrix(NA), n_states, n_etas, log_prior_pdf, time_varying = rep(TRUE, 4), diff --git a/R/predict.R b/R/predict.R index b6370929..550ea73a 100644 --- a/R/predict.R +++ b/R/predict.R @@ -2,8 +2,8 @@ #' #' Draw samples from the posterior predictive distribution for future #' time points given the posterior draws of hyperparameters \eqn{\theta} and -#' \eqn{alpha_{n+1}}. Function can also be used to draw samples from the -#' posterior predictive distribution +#' latent state \eqn{alpha_{n+1}}. Function can also be used to draw samples +#' from the posterior predictive distribution #' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. #' #' @param object mcmc_output object obtained from diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 3f86f5d0..e87591df 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -158,7 +158,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' @param particles Number of state samples per MCMC iteration. #' Ignored if \code{mcmc_type} is \code{"approx"}. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and +#' (default, returns posterior samples of latent states alpha and #' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and @@ -444,7 +444,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' @param particles Number of state samples per MCMC iteration. #' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and +#' (default, returns posterior samples of latent states alpha and #' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and @@ -633,8 +633,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", #' @param iter Number of MCMC iterations. #' @param particles Number of state samples per MCMC iteration. #' @param output_type Either \code{"full"} -#' (default, returns posterior samples of states alpha and hyperparameters -#' theta), \code{"theta"} (for marginal posterior of theta), +#' (default, returns posterior samples of latent states alpha and +#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), #' or \code{"summary"} (return the mean and variance estimates of the states #' and posterior samples of theta). In case of \code{"summary"}, means and #' covariances are computed using the full output of particle filter diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index a9dc19ca..81e2fe0c 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -34,7 +34,7 @@ test_that("Non-gaussian predictions work", { set.seed(1) y <- rpois(10, exp(cumsum(rnorm(10, 0, 0.1)))) model <- ar1_ng(y, - rho = uniform(0.9, 0, 0.99), mu = 0, + rho = uniform(0.9, 0, 1), mu = 0, sigma = halfnormal(0.1, 1), distribution = "poisson") set.seed(123) @@ -63,7 +63,7 @@ test_that("Non-gaussian predictions work", { list(T = T, R = R, P1 = P1) } prior_fn <- function(x) { - ifelse(x[1] < 0 | x[1] > 0.99 | x[2] < 0, -Inf, - 0.5 * x[2]^2) + ifelse(x[1] < 0 | x[1] > 1 | x[2] < 0, -Inf, - 0.5 * x[2]^2) } model2 <- ssm_ung(model$y, Z = 1, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, distribution = "poisson", update_fn = update_fn, From b0ab8c0067e571809cd99d6abe24eea3302606ba Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 16:42:33 +0300 Subject: [PATCH 044/180] still fixing check_y --- R/check_arguments.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 0146536a..08462c66 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -11,10 +11,10 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } else { if (!is.vector(x) || is.list(x)) { if (is.ts(x) || is.matrix(x)) { - if (ncol(x) == 1 && length(dim(x)) < 3) { + if (!is.null(dim(x)) && ncol(x) == 1 && length(dim(x)) < 3) { dim(x) <- NULL } else { - if(ncol(x) > 1) { + if(!is.null(dim(x)) && ncol(x) > 1) { stop("Argument y must be a vector or univariate ts object.") } } From 8261f2cb966e499b40f2a14f0310a4963d68292c Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 16:50:38 +0300 Subject: [PATCH 045/180] update news --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index d7744f86..f2b5b0bc 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,12 @@ devtools::install_github("helske/bssm") Recent changes (For all changes, see NEWS file.) ========================================================================== +bssm 1.1.6 (Release date: ) +============== + * Fixed a bug in EKF-based particle filter which returned filtered estimates + also in place of one-step ahead predictions. + * Cleaned codes and added more comprehensive tests in line with pkgcheck tests. + bssm 1.1.4 (Release date: 2021-04-13) ============== * Better documentation for SV model, and changed ordering of arguments to emphasise the From d1838c0f225ded2ee66fb2af5c474d07c44d8034 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 19:42:08 +0300 Subject: [PATCH 046/180] allow p==1 for mlg model --- R/check_arguments.R | 15 +++++++++------ R/models.R | 8 +++++++- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 08462c66..89fd60a8 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -242,14 +242,17 @@ check_Z <- function(x, p, n, multivariate = FALSE) { } } } else { - if (dim(x)[1] != p || !(dim(x)[3] %in% c(1, NA, n))) { - stop(paste("'Z' must be a (p x m) matrix or (p x m x n) array", - "where p is the number of series, m is the number of states,", - "and n is the length of the series. ", sep = " ")) + if(p == 1 && length(x) == 1) { + dim(x) <- c(1, 1, 1) } else { - dim(x) <- - c(p, dim(x)[2], (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) + if (is.null(dim(x)) || dim(x)[1] != p || !(dim(x)[3] %in% c(1, NA, n))) { + stop(paste("'Z' must be a (p x m) matrix or (p x m x n) array", + "where p is the number of series, m is the number of states,", + "and n is the length of the series. ", sep = " ")) + } } + dim(x) <- + c(p, dim(x)[2], (n - 1) * (max(dim(x)[3], 0, na.rm = TRUE) > 1) + 1) } x } diff --git a/R/models.R b/R/models.R index 2cf1cd49..35189342 100644 --- a/R/models.R +++ b/R/models.R @@ -422,7 +422,12 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) - + if (p == 1) { + warning(paste( + "Found univariate series as input but defining multivariate model.", + "It can be more efficient to use 'ssm_ulg' instead of 'ssm_mlg'.", + sep = " ")) + } # create Z Z <- check_Z(Z, p, n, multivariate = TRUE) m <- dim(Z)[2] @@ -436,6 +441,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, P1 <- check_P1(P1, m) D <- check_D(D, p, n) + D <- as.matrix(D) # p = 1 C <- check_C(C, m, n) H <- check_H(H, p, n, multivariate = TRUE) From a7fc6224ab5cfedcd4ace03454df147f7a4667dc Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 19:43:21 +0300 Subject: [PATCH 047/180] allow p==1 for mng model --- R/models.R | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/R/models.R b/R/models.R index 35189342..d1359483 100644 --- a/R/models.R +++ b/R/models.R @@ -562,6 +562,12 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) + if (p == 1) { + warning(paste( + "Found univariate series as input but defining multivariate model.", + "It can be more efficient to use 'ssm_ung' instead of 'ssm_mng'.", + sep = " ")) + } if(length(distribution) == 1) distribution <- rep(distribution, p) check_distribution(y, distribution) if(length(phi) == 1) phi <- rep(phi, p) @@ -584,6 +590,7 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, P1 <- check_P1(P1, m) D <- check_D(D, p, n) + if (p == 1) D <- as.matrix(D) C <- check_C(C, m, n) if (length(u) == 1) { From 03f4dbc8ac1ae0cb5f6d33fa6c349cac46b0dee0 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 19:43:56 +0300 Subject: [PATCH 048/180] fix noise term update for ar1_lg --- src/model_ar1_lg.cpp | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/src/model_ar1_lg.cpp b/src/model_ar1_lg.cpp index 32f79d8c..1d9560d2 100644 --- a/src/model_ar1_lg.cpp +++ b/src/model_ar1_lg.cpp @@ -14,17 +14,17 @@ void ar1_lg::update_model(const arma::vec& new_theta) { T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); + RR(0, 0, 0) = std::pow(new_theta(1)); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); } - P1(0, 0) = std::pow(new_theta(1), 2) / (1.0 - std::pow(new_theta(0), 2)); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); - compute_RR(); if(sd_y_est) { H(0) = new_theta(2 + mu_est); - HH(0) = H(0); + HH(0) = std::pow(H(0), 2); } if(xreg.n_cols > 0) { @@ -38,17 +38,16 @@ void ar1_lg::update_model(const arma::vec& new_theta, const Rcpp::Function updat T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); + RR(0, 0, 0) = std::pow(new_theta(1)); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); } - P1(0, 0) = std::pow(new_theta(1), 2) / (1.0 - std::pow(new_theta(0), 2)); - - compute_RR(); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); if(sd_y_est) { H(0) = new_theta(2 + mu_est); - HH(0) = H(0); + HH(0) = std::pow(H(0), 2); } if(xreg.n_cols > 0) { From f58ecf8bdcf461b0a52384e82d7d5310cd8d393b Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 19:44:29 +0300 Subject: [PATCH 049/180] same direct computation of RR as in lg case --- src/model_ar1_ng.cpp | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/src/model_ar1_ng.cpp b/src/model_ar1_ng.cpp index f33d8c4b..8c3e80e5 100644 --- a/src/model_ar1_ng.cpp +++ b/src/model_ar1_ng.cpp @@ -13,13 +13,12 @@ void ar1_ng::update_model(const arma::vec& new_theta) { T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); + RR(0, 0, 0) = std::pow(new_theta(1)); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); } - P1(0, 0) = std::pow(new_theta(1), 2) / (1.0 - std::pow(new_theta(0), 2)); - - compute_RR(); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); if(phi_est) { phi = new_theta(2 + mu_est); @@ -37,13 +36,12 @@ void ar1_ng::update_model(const arma::vec& new_theta, const Rcpp::Function updat T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); + RR(0, 0, 0) = std::pow(new_theta(1)); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); } - P1(0, 0) = std::pow(new_theta(1), 2) / (1.0 - std::pow(new_theta(0), 2)); - - compute_RR(); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); if(phi_est) { phi = new_theta(2 + mu_est); From b49c1da49465c1395aee92d5b765bb6a717db046 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 19:54:58 +0300 Subject: [PATCH 050/180] add power to pow --- src/model_ar1_lg.cpp | 7 ++++--- src/model_ar1_ng.cpp | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/src/model_ar1_lg.cpp b/src/model_ar1_lg.cpp index 1d9560d2..d3a710b2 100644 --- a/src/model_ar1_lg.cpp +++ b/src/model_ar1_lg.cpp @@ -14,7 +14,7 @@ void ar1_lg::update_model(const arma::vec& new_theta) { T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1)); + RR(0, 0, 0) = std::pow(new_theta(1), 2); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); @@ -35,10 +35,11 @@ void ar1_lg::update_model(const arma::vec& new_theta) { } void ar1_lg::update_model(const arma::vec& new_theta, const Rcpp::Function update_fn) { - + // sampling of all parameters is on constrained scale, would make sense to + // modify as in bsm models T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1)); + RR(0, 0, 0) = std::pow(new_theta(1), 2); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); diff --git a/src/model_ar1_ng.cpp b/src/model_ar1_ng.cpp index 8c3e80e5..f76142b2 100644 --- a/src/model_ar1_ng.cpp +++ b/src/model_ar1_ng.cpp @@ -13,7 +13,7 @@ void ar1_ng::update_model(const arma::vec& new_theta) { T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1)); + RR(0, 0, 0) = std::pow(new_theta(1), 2); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); @@ -36,7 +36,7 @@ void ar1_ng::update_model(const arma::vec& new_theta, const Rcpp::Function updat T(0, 0, 0) = new_theta(0); R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1)); + RR(0, 0, 0) = std::pow(new_theta(1), 2); if (mu_est) { a1(0) = new_theta(2); C.fill(new_theta(2) * (1.0 - new_theta(0))); From 35efee4e43aafedaa979ac9a4db3ea901da8d5ef Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 20:00:55 +0300 Subject: [PATCH 051/180] mng to mlg in predict call --- R/predict.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/predict.R b/R/predict.R index 550ea73a..4f488aff 100644 --- a/R/predict.R +++ b/R/predict.R @@ -158,7 +158,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), - c("ssm_mng", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) + c("ssm_mlg", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) }, ssm_mng =, From b86b15edd25672242fcb68f821ab62f3d981e0c4 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 20:21:43 +0300 Subject: [PATCH 052/180] mng to mlg in predict past call and new tests --- R/predict.R | 8 ++++--- tests/testthat/test_predict.R | 45 +++++++++++++++++++++++++++++++++-- 2 files changed, 48 insertions(+), 5 deletions(-) diff --git a/R/predict.R b/R/predict.R index 4f488aff..24cf3cce 100644 --- a/R/predict.R +++ b/R/predict.R @@ -205,7 +205,8 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } } else { variables <- colnames(model$y) - if (is.null(variables)) variables <- "Series 1" + if (is.null(variables)) + variables <- paste("Series", 1:max(1, ncol(model$y))) } d <- data.frame(value = as.numeric(pred), variable = variables, @@ -240,7 +241,8 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } else { variables <- colnames(model$y) - if (is.null(variables)) variables <- "Series 1" + if (is.null(variables)) + variables <- paste("Series", 1:max(1, ncol(model$y))) if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- @@ -263,7 +265,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), - c("ssm_mng", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) + c("ssm_mlg", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) }, ssm_mng =, diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 81e2fe0c..bc39727c 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -6,10 +6,11 @@ test_that("Gaussian predictions work", { set.seed(1) y <- rnorm(10, cumsum(rnorm(10, 0, 0.1)), 0.1) model <- ar1_lg(y, - rho = uniform(0.9, 0, 0.99), mu = 0, + rho = uniform(0.9, 0, 1), mu = 0, sigma = halfnormal(0.1, 1), sd_y = halfnormal(0.1, 1)) + set.seed(123) mcmc_results <- run_mcmc(model, iter = 1000) future_model <- model future_model$y <- rep(NA, 3) @@ -25,7 +26,47 @@ test_that("Gaussian predictions work", { meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100) - expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) + + ufun <- function(x) { + T <- array(x[1]) + R <- array(x[2]) + H <- array(x[3]) + dim(T) <- dim(R) <- dim(H) <- c(1, 1, 1) + P1 <- matrix(x[2]^2) / (1 - x[1]^2) + list(T = T, R = R, P1 = P1, H = H) + } + pfun <- function(x) { + ifelse(x[1] > 1 | any(x < 0), -Inf, - sum(0.5 * x[2:3]^2)) + } + + expect_warning(model2 <- ssm_mlg(matrix(model$y, length(model$y), 1), + Z = 1, H = model$H, T = model$T, R = model$R, + a1 = model$a1, P1 = model$P1, + init_theta = c(rho = 0.9, sigma = 0.1, sd_y = 0.1), + update_fn = ufun, prior_fn = pfun, state_names = "signal")) + + set.seed(123) + expect_error(mcmc_results2 <- run_mcmc(model2, iter = 1000), + NA) + expect_equal(mcmc_results$theta, mcmc_results2$theta) + expect_equal(mcmc_results$alpha, mcmc_results2$alpha) + expect_equal(mcmc_results$posterior, mcmc_results2$posterior) + + future_model2 <- model2 + future_model2$y <- matrix(NA, 3, 1) + expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", + nsim = 100), NA) + expect_equal(pred, pred2) + # Posterior predictions for past observations: + yrep2 <- predict(mcmc_results2, model2, type = "response", + future = FALSE, nsim = 100) + meanrep2 <- predict(mcmc_results2, model2, type = "mean", + future = FALSE, nsim = 100) + expect_equal(yrep, yrep2) + expect_equal(meanrep, meanrep2) + expect_error(predict(mcmc_results2, model, type = "response", + future = FALSE, nsim = 100)) }) From 6558dfb4a1afeddb52796f7c33280fa0d894766c Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 21:12:35 +0300 Subject: [PATCH 053/180] fix suggest_N for nlg --- src/R_postcorrection.cpp | 2 +- tests/testthat/test_post_correct.R | 41 +++++++++++++++++++++++++++++- 2 files changed, 41 insertions(+), 2 deletions(-) diff --git a/src/R_postcorrection.cpp b/src/R_postcorrection.cpp index 79ddb2eb..11dc126a 100644 --- a/src/R_postcorrection.cpp +++ b/src/R_postcorrection.cpp @@ -128,7 +128,7 @@ arma::vec suggest_n_nonlinear(const arma::mat& y, SEXP Z, SEXP H, n_states, n_etas, time_varying, seed); - model.update_model(theta_map, R_NilValue); + model.update_model(theta_map); arma::vec sds(candidates.n_elem); for(unsigned int i = 0; i < candidates.n_elem; i++) { int nsim = candidates(i); diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index aac2bc3e..e7c2fd79 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -2,7 +2,7 @@ context("Post-correction and suggest_N") -test_that("Test that sim_smoother for LGSSM works as Kalman smoother", { +test_that("Test post correction for AR1 model", { set.seed(1) n <- 14 x1 <- sin((2 * pi / 12) * 1:n) @@ -43,3 +43,42 @@ test_that("Test that sim_smoother for LGSSM works as Kalman smoother", { expect_gt(max(out_is2$weights), 0) }) +test_that("Test post correction for non-linear model", { + skip_on_cran() + set.seed(1) + n <- 10 + x <- y <- numeric(n) + y[1] <- rnorm(1, exp(x[1]), 0.1) + for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) + } + + pntrs <- nlg_example_models("sin_exp") + + expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state"), NA) + + + expect_error(out_approx <- run_mcmc(model, mcmc_type = "approx", + local_approx = FALSE, iter = 1000, output_type = "full"), NA) + + expect_error(estN <- suggest_N(model, out_approx, + replications = 10, candidates = c(5, 10)), NA) + + expect_identical(estN$N, 5) + + # Can't really test for correctness with limited time + expect_error(out_is2 <- post_correct(model, out_approx, particles = estN$N, + threads = 2), NA) + expect_lt(sum(out_is2$theta), Inf) + expect_lt(sum(out_is2$alphahat), Inf) + expect_lt(sum(out_is2$Vt), Inf) + expect_lt(max(out_is2$weights), Inf) + expect_gt(max(out_is2$weights), 0) + +}) From 6cc56fe41c95827a23f245d74e1f2c693a3af062 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 21:20:53 +0300 Subject: [PATCH 054/180] update NEWS --- NEWS | 144 +++++++++++++++++++++++++++++++++++++---------------------- 1 file changed, 90 insertions(+), 54 deletions(-) diff --git a/NEWS b/NEWS index 63b3e485..782b8533 100644 --- a/NEWS +++ b/NEWS @@ -2,7 +2,11 @@ bssm 1.1.6 (Release date: ) ============== * Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions. - * Cleaned some codes and added more comprehensive tests in line with pkgcheck tests. + * Fixed a bug which caused an error in suggest_N for nlg_ssm. + * Fixed a bug which caused incorrect sampling of smoothing distribution for + ar1_lg model when predicting past or when using simulation smoother. + * Cleaned some codes and added more comprehensive tests in line with + pkgcheck tests. bssm 1.1.5 (Release date: 2021-06-14) ============== @@ -14,14 +18,16 @@ bssm 1.1.5 (Release date: 2021-06-14) bssm 1.1.4 (Release date: 2021-04-13) ============== - * Better documentation for SV model, and changed ordering of arguments to emphasise the - recommended parameterization. + * Better documentation for SV model, and changed ordering of arguments to + emphasise the recommended parameterization. * Fixed predict method for SV model. - * Removed parallelization in one example which failed on Solaris for some unknown reason. + * Removed parallelization in one example which failed on Solaris for some + unknown reason. bssm 1.1.3-2 (Release date: 2021-02-24) ============== - * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. + * Fixed missing parenthesis causing compilation fail in case of no OpenMP + support. * Added pandoc version >= 1.12.3 to system requirements. * Restructured C++ classes so no R structures are present in OpenMP regions. @@ -30,28 +36,34 @@ bssm 1.1.3-1 (Release date: 2021-02-22) * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. * Fixed the state covariance estimates of IS-MCMC, approx-MCMC, and Gaussian MCMC when output_type = "summary". - * Fixed memory leaks due to uninitialized variables due to aborted particle filter. - * Fixed numerical issues of multivariate normal density for nonlinear models. + * Fixed memory leaks due to uninitialized variables due to aborted particle + filter. + * Fixed numerical issues of multivariate normal density for nonlinear + models. * Removed dependency on R::lchoose for safer parallel code. * Added vignette for SDE models. * Updated citation information and streamlined the main vignette. bssm 1.1.2 (Release date: 2021-02-08) ============== - * Changed the definition of D in ssm_ulg and ssm_ung, functions now accept D as scalar or vector as + * Changed the definition of D in ssm_ulg and ssm_ung, functions now accept + D as scalar or vector as was originally intended. - * Fixed a segfault issue with parallel state sampling in general ssm_ulg/mlg/ung/mng models - caused by calls to R function inside parallel region. - * Fixed a bug from version 1.0.0 in IS1 type sampling which actually lead to IS2 type sampling. + * Fixed a segfault issue with parallel state sampling in general + ssm_ulg/mlg/ung/mng models caused by calls to R function inside parallel + region. + * Fixed a bug from version 1.0.0 in IS1 type sampling which actually lead + to IS2 type sampling. * Fixed out-of-bounds error in IS3 sampling. - * Fixed weight computations for multivariate nonlinear models in case of psi-APF - in some border cases with non-standard H. + * Fixed weight computations for multivariate nonlinear models in case of + psi-APF in some border cases with non-standard H. * Removed Armadillo bound checks for efficiency gains. bssm 1.1.1 (Release date: 2021-01-22) ============== - * Added missing scaling for Gamma distribution in importance sampling weights for added numerical robustness. + * Added missing scaling for Gamma distribution in importance sampling + weights for added numerical robustness. * Fixed sequential importance sampling for multivariate non-gaussian models. * Fixed simulation smoother for multivariate Gaussian models. @@ -63,12 +75,15 @@ bssm 1.1.0 (Release date: 2021-01-19) * Added function `post_correct` which can be used to update previous approximate MCMC with IS-weights. * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. - * The adaptation of the proposal distribution now continues also after the burn-in by default. + * The adaptation of the proposal distribution now continues also after the + burn-in by default. * Changed default MCMC type to typically most efficient and robust IS2. - * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). - * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. - This resulted error within MCMC algorithms. - * Fixed a dimension drop bug in the predict method which caused error for univariate models. + * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` + also works with a warning). + * Fixed a bug with bsm models with covariates, where all standard deviation + parameters were fixed. This resulted error within MCMC algorithms. + * Fixed a dimension drop bug in the predict method which caused error for + univariate models. * Fixed some docs and added more examples. * Fixed few typos in vignette (thanks Kyle Hussman) * Reduced runtime of MCMC in growth model vignette as requested by CRAN. @@ -95,19 +110,25 @@ bssm 1.0.0 (Release date: 2020-06-09) Major update * Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely on C++ snippets). + can be defined via R functions (non-linear and SDE models still rely on + C++ snippets). * Added support for multivariate non-Gaussian models. * Added support for gamma distributions. - * Added the function as.data.frame for mcmc output which converts the MCMC samples - to data.frame format for easier post-processing. + * Added the function as.data.frame for mcmc output which converts the MCMC + samples to data.frame format for easier post-processing. * Added truncated normal prior. - * Many argument names and model building functions have been changed for clarity and consistency. - * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. - * Allow zero as initial value for positive-constrained parameters of bsm models. - * Small changes to summary method which can now return also only summaries of the states. + * Many argument names and model building functions have been changed for + clarity and consistency. + * Major overhaul of C++ internals which can bring minor efficiency gains + and smaller installation size. + * Allow zero as initial value for positive-constrained parameters of bsm + models. + * Small changes to summary method which can now return also only summaries + of the states. * Fixed a bug in initializing run_mcmc for negative binomial model. * Fixed a bug in phi-APF for non-linear models. - * Reimplemented predict method which now always produces data frame of samples. + * Reimplemented predict method which now always produces data frame of + samples. bssm 0.1.11 (Release date: 2020-02-25) ============== @@ -132,28 +153,35 @@ bssm 0.1.8-1 (Release date: 2019-12-20) bssm 0.1.8 (Release date: 2019-09-23) ============== - * Fixed a bug in predict method which prevented the method working in case of ngssm models. - * Fixed a bug in predict method which threw an error due to dimension drop of models with single state. + * Fixed a bug in predict method which prevented the method working in case + of ngssm models. + * Fixed a bug in predict method which threw an error due to dimension drop of + models with single state. * Fixed issues with the vignette. bssm 0.1.7 (Release date: 2019-03-19) ============== - * Fixed a bug in EKF smoother which resulted wrong smoothed state estimates in - case of partially missing multivariate observations. Thanks for Santeri Karppinen for spotting the bug. - * Added twisted SMC based simulation smoothing algorithm for Gaussian models, as an alternative to - Kalman smoother based simulation. + * Fixed a bug in EKF smoother which resulted wrong smoothed state estimates + in case of partially missing multivariate observations. Thanks for Santeri + Karppinen for spotting the bug. + * Added twisted SMC based simulation smoothing algorithm for Gaussian models, + as an alternative to Kalman smoother based simulation. bssm 0.1.6-1 (Release date: 2018-11-20) ============== - * Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik methods for SDE and ng_ar1 models. + * Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik + methods for SDE and ng_ar1 models. * Added a missing Jacobian for ng_bsm and bsm models using IS-correction. - * Changed internal parameterization of ng_bsm and bsm models from log(1+theta) to log(theta). + * Changed internal parameterization of ng_bsm and bsm models from + log(1+theta) to log(theta). bssm 0.1.5 (Release date: 2018-05-23) ============== - * Fixed the Cholesky decomposition in filtering recursions of multivariate models. + * Fixed the Cholesky decomposition in filtering recursions of multivariate + models. * as_gssm now works for multivariate Gaussian models of KFAS as well. - * Fixed several issues regarding partially missing observations in multivariate models. + * Fixed several issues regarding partially missing observations in + multivariate models. * Added the MASS package to Suggests as it is used in some unit tests. * Added missing type argument to SDE MCMC call with delayed acceptance. @@ -163,30 +191,36 @@ bssm 0.1.4-1 (Release date: 2018-02-04) bssm 0.1.4 (Release date: 2018-02-04) ============== - * MCMC output can now be defined with argument `type`. Instead of returning joint posterior - samples, run_mcmc can now return only marginal samples of theta, or summary statistics of - the states. - * Due to the above change, argument `sim_states` was removed from the Gaussian MCMC methods. - * MCMC functions are now less memory intensive, especially with `type="theta"`. + * MCMC output can now be defined with argument `type`. Instead of returning + joint posterior samples, run_mcmc can now return only marginal samples of + theta, or summary statistics of the states. + * Due to the above change, argument `sim_states` was removed from the + Gaussian MCMC methods. + * MCMC functions are now less memory intensive, especially with + `type="theta"`. bssm 0.1.3 (Release date: 2018-01-07) ============== * Streamlined the output of the print method for MCMC results. - * Fixed major bugs in predict method which caused wrong values for the prediction intervals. + * Fixed major bugs in predict method which caused wrong values for the + prediction intervals. * Fixed some package dependencies. - * Sampling for standard deviation parameters of BSM and their non-Gaussian counterparts - is now done in logarithmic scale for slightly increased efficiency. - * Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1) processes. - * MCMC output now includes posterior predictive distribution of states for one step ahead - to the future. + * Sampling for standard deviation parameters of BSM and their non-Gaussian + counterparts is now done in logarithmic scale for slightly increased + efficiency. + * Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1) + processes. + * MCMC output now includes posterior predictive distribution of states for + one step ahead to the future. bssm 0.1.2 (Release date: 2017-11-21) ============== - * API change for run_mcmc: All MCMC methods are now under the argument method, - instead of having separate arguments for delayed acceptance and IS schemes. - * summary method for MCMC output now omits the computation of SE and ESS in order - to speed up the function. + * API change for run_mcmc: All MCMC methods are now under the argument + method, instead of having separate arguments for delayed acceptance and IS + schemes. + * summary method for MCMC output now omits the computation of SE and ESS in + order to speed up the function. * Added new model class lgg_ssm, which is a linear-Gaussian model defined directly via C++ like non-linear ssm_nlg models. This allows more flexible prior definitions and complex system matrix constructions. @@ -194,8 +228,10 @@ bssm 0.1.2 (Release date: 2017-11-21) state dynamics defined as SDE. These too are defined via couple simple C++ functions. * Added non-gaussian AR(1) model class. - * Added argument nsim for predict method, which allows multiple draws per MCMC iteration. - * The noise multiplier matrices H and R in ssm_nlg models can now depend on states. + * Added argument nsim for predict method, which allows multiple draws per + MCMC iteration. + * The noise multiplier matrices H and R in ssm_nlg models can now depend on + states. bssm 0.1.1-1 (Release date: 2017-06-27) ============== From 7a2ada7ed555ce87d1f654b9a43255eed6857f37 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 22:28:37 +0300 Subject: [PATCH 055/180] bsf tests --- tests/testthat/test_ekpf.R | 8 +++++++- tests/testthat/test_mcmc.R | 3 +++ tests/testthat/test_particle_smoother.R | 8 ++++++++ tests/testthat/test_post_correct.R | 2 +- 4 files changed, 19 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index 11893336..07e3f77f 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -51,7 +51,7 @@ test_that("EKF and IEKF work", { x[i+1] <- rnorm(1, sin(x[i]), 0.1) y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - + y[2:3] <- NA pntrs <- nlg_example_models("sin_exp") expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, @@ -62,6 +62,8 @@ test_that("EKF and IEKF work", { n_states = 1, n_etas = 1, state_names = "state"), NA) expect_equal(ekf(model_nlg)$logLik, 3.55814184565819) + expect_equal(ekf(model_nlg, iekf_iter = 2)$logLik, + logLik(model_nlg, method = "ekf", iekf_iter = 2)) expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 3.69550903344128) expect_equal(ekf(model_nlg, iekf_iter = 1), ekf(model_nlg, iekf_iter = 2)) @@ -71,6 +73,10 @@ test_that("EKF and IEKF work", { expect_equal(out_ekf1$alphahat[9:10], c(0.0333634309012196, 0.0797729159367873), tol = 0.1) expect_equal(out_ekf1$alphahat, out_ekf2) + expect_equal( + ekf_fast_smoother(model_nlg, iekf_iter = 2), + ekf_smoother(model_nlg)$alphahat, iekf_iter = 2) expect_error(ukf(model_nlg), NA) + expect_error(bootstrap_filter(model_nlg, 10), NA) }) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index c1fc8b10..ba01314e 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -137,6 +137,8 @@ test_that("MCMC for ssm_mng work", { } } } + + expect_error(boostrap_filter(model, 10), NA) }) test_that("MCMC results with psi-APF for Poisson model are correct", { @@ -183,6 +185,7 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { run_mcmc(model_bssm, mcmc_type = type, iter = 100, seed = 1, output_type = "theta", particles = 5)[-13 - z]) } + }) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 93b260d4..cb986629 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -43,6 +43,8 @@ test_that("Particle smoother for poisson bsm_ng returns finite values", { expect_error(model <- bsm_ng(1:10, sd_level = 2, sd_slope = 2, P1 = diag(2, 2), distribution = "poisson"), NA) expect_error(out <- particle_smoother(model, 10, seed = 1), NA) + expect_error(out <- particle_smoother(model, 10, method = "bsf", seed = 1), + NA) expect_true(is.finite(sum(out$alpha))) expect_true(is.finite(sum(out$alphahat))) @@ -69,8 +71,14 @@ test_that("Particle smoother for NB bsm_ng returns finite values", { sd_slope = halfnormal(0.1, 1), P1 = diag(2, 2), phi = gamma(1, 2, 2), distribution = "negative binomial"), NA) + expect_error(out <- particle_smoother(model, 10, seed = 1), NA) + expect_true(is.finite(sum(out$alpha))) + expect_true(is.finite(sum(out$alphahat))) + expect_true(is.finite(sum(out$Vt))) + expect_error(out <- particle_smoother(model, 10, method = "bsf", seed = 1), + NA) expect_true(is.finite(sum(out$alpha))) expect_true(is.finite(sum(out$alphahat))) expect_true(is.finite(sum(out$Vt))) diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index e7c2fd79..2012e66f 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -53,7 +53,7 @@ test_that("Test post correction for non-linear model", { x[i+1] <- rnorm(1, sin(x[i]), 0.1) y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - + y[2:3] <- NA pntrs <- nlg_example_models("sin_exp") expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, From 171b7361d291519f437472fb286654bb08bf1ea2 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 4 Sep 2021 23:01:59 +0300 Subject: [PATCH 056/180] fixed predict past to multivariate models --- NEWS | 6 ++- R/predict.R | 18 ++++--- README.md | 8 ++- tests/testthat/test_predict.R | 95 ++++++++++++++++++++++++++--------- 4 files changed, 94 insertions(+), 33 deletions(-) diff --git a/NEWS b/NEWS index 782b8533..f340b3f2 100644 --- a/NEWS +++ b/NEWS @@ -1,12 +1,14 @@ bssm 1.1.6 (Release date: ) ============== + * Cleaned some codes and added lots of tests in line with pkgcheck tests. * Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions. * Fixed a bug which caused an error in suggest_N for nlg_ssm. * Fixed a bug which caused incorrect sampling of smoothing distribution for ar1_lg model when predicting past or when using simulation smoother. - * Cleaned some codes and added more comprehensive tests in line with - pkgcheck tests. + * Fixed a bug which caused an error when predicting past values in + multivariate time series case. + bssm 1.1.5 (Release date: 2021-06-14) ============== diff --git a/R/predict.R b/R/predict.R index 24cf3cce..c9ec0a3f 100644 --- a/R/predict.R +++ b/R/predict.R @@ -127,7 +127,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } if (!identical(ncol(object$theta), length(model$theta))) { stop(paste("Number of unknown parameters 'theta' does not correspond to", - "the MCMC output. ", sep = " ")) + "the MCMC output. ", sep = " ")) } if (nsim < 1) stop("Number of samples 'nsim' should be at least one.") @@ -152,7 +152,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, ar1_lg = { if (!identical(length(model$a1), ncol(object$alpha))) { stop(paste("Model does not correspond to the MCMC output:", - "Wrong number of states. ", sep = " ")) + "Wrong number of states. ", sep = " ")) } pred <- gaussian_predict(model, theta, alpha, pmatch(type, c("response", "mean", "state")), @@ -215,10 +215,15 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } else { - if (!identical(nrow(object$alpha) - 1L, length(model$y))) { - stop("Number of observations of the model and MCMC output do not match.") + if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { + if (!identical(nrow(object$alpha) - 1L, nrow(model$y))) { + stop("Number of observations of the model and MCMC output do not match.") + } + } else { + if (!identical(nrow(object$alpha) - 1L, length(model$y))) { + stop("Number of observations of the model and MCMC output do not match.") + } } - w <- object$counts * (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, @@ -251,7 +256,6 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, theta <- t(object$theta[idx, ]) states <- aperm(states, c(2, 1, 3)) - switch(attr(object, "model_type"), ssm_mlg =, ssm_ulg =, @@ -304,7 +308,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } , stop("Not yet implemented for ssm_sde. ")) - + d <- data.frame(value = as.numeric(pred), variable = variables, time = rep(time(model$y), each = nrow(pred)), diff --git a/README.md b/README.md index f2b5b0bc..cd2741f1 100644 --- a/README.md +++ b/README.md @@ -34,9 +34,15 @@ Recent changes (For all changes, see NEWS file.) bssm 1.1.6 (Release date: ) ============== + * Cleaned codes and added more comprehensive tests in line with pkgcheck + tests. This resulted in finding and fixing multiple bugs: * Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions. - * Cleaned codes and added more comprehensive tests in line with pkgcheck tests. + * Fixed a bug which caused an error in suggest_N for nlg_ssm. + * Fixed a bug which caused incorrect sampling of smoothing distribution for + ar1_lg model when predicting past or when using simulation smoother. + * Fixed a bug which caused an error when predicting past values in + multivariate time series case. bssm 1.1.4 (Release date: 2021-04-13) ============== diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index bc39727c..99a69aac 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -21,10 +21,10 @@ test_that("Gaussian predictions work", { expect_lt(mean(pred$value[pred$time == 3]), 0.5) # Posterior predictions for past observations: - yrep <- predict(mcmc_results, model, type = "response", - future = FALSE, nsim = 100) - meanrep <- predict(mcmc_results, model, type = "mean", - future = FALSE, nsim = 100) + expect_error(yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100), NA) expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) @@ -59,15 +59,15 @@ test_that("Gaussian predictions work", { nsim = 100), NA) expect_equal(pred, pred2) # Posterior predictions for past observations: - yrep2 <- predict(mcmc_results2, model2, type = "response", - future = FALSE, nsim = 100) - meanrep2 <- predict(mcmc_results2, model2, type = "mean", - future = FALSE, nsim = 100) + expect_error(yrep2 <- predict(mcmc_results2, model2, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep2 <- predict(mcmc_results2, model2, type = "mean", + future = FALSE, nsim = 100), NA) expect_equal(yrep, yrep2) expect_equal(meanrep, meanrep2) expect_error(predict(mcmc_results2, model, type = "response", future = FALSE, nsim = 100)) - + }) test_that("Non-gaussian predictions work", { @@ -89,12 +89,12 @@ test_that("Non-gaussian predictions work", { expect_lt(mean(pred$value[pred$time == 3]), 2.5) # Posterior predictions for past observations: - yrep <- predict(mcmc_results, model, type = "response", - future = FALSE, nsim = 100) - meanrep <- predict(mcmc_results, model, type = "mean", - future = FALSE, nsim = 100) + expect_error(yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100), NA) - expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) update_fn <- function(x) { T <- array(x[1]) @@ -123,10 +123,10 @@ test_that("Non-gaussian predictions work", { nsim = 100), NA) expect_equal(pred, pred2) # Posterior predictions for past observations: - yrep2 <- predict(mcmc_results2, model2, type = "response", - future = FALSE, nsim = 100) - meanrep2 <- predict(mcmc_results2, model2, type = "mean", - future = FALSE, nsim = 100) + expect_error(yrep2 <- predict(mcmc_results2, model2, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep2 <- predict(mcmc_results2, model2, type = "mean", + future = FALSE, nsim = 100), NA) expect_equal(yrep, yrep2) expect_equal(meanrep, meanrep2) expect_error(predict(mcmc_results2, model, type = "response", @@ -164,10 +164,59 @@ test_that("Predictions for nlg_ssm work", { expect_lt(mean(pred$value[pred$time == 3]), 1.5) # Posterior predictions for past observations: - yrep <- predict(mcmc_results, model, type = "response", - future = FALSE, nsim = 100) - meanrep <- predict(mcmc_results, model, type = "mean", - future = FALSE, nsim = 100) + expect_error(yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100), NA) - expect_equal(mean(yrep$value-meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) }) + + +test_that("Predictions for mng_ssm work", { + set.seed(1) + n <- 20 + x <- cumsum(rnorm(n, sd = 0.5)) + phi <- 2 + y <- cbind(rnbinom(n, size = phi, mu = exp(x)), + rpois(n, exp(x))) + + Z <- matrix(1, 2, 1) + T <- 1 + R <- 0.5 + a1 <- 0 + P1 <- 1 + + update_fn <- function(theta) { + list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) + } + + prior_fn <- function(theta) { + ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) + } + + expect_error(model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), + init_theta = c(0.5, 2), + distribution = c("negative binomial", "poisson"), + update_fn = update_fn, prior_fn = prior_fn), NA) + + + expect_error(mcmc_results <- run_mcmc(model, iter = 5000, particles = 10), + NA) + future_model <- model + future_model$y <- matrix(NA, 3, 2) + expect_error(pred <- predict(mcmc_results, particles = 10, + future_model, type = "mean", nsim = 1000), NA) + + expect_gte(min(pred$value), 0) + expect_lt(max(pred$value), 1000) + + # Posterior predictions for past observations: + expect_error(yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100), NA) + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100), NA) + + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) +}) + From 215a6e14144eb2e05afaa45e3ef38bf8536f34ea Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 08:43:19 +0300 Subject: [PATCH 057/180] fix nb sampling in predict --- NEWS | 3 +++ R/predict.R | 2 +- README.md | 3 +++ src/model_ssm_mng.cpp | 28 +++++++++++++++------------- src/model_ssm_ung.cpp | 17 +++++++++++------ 5 files changed, 33 insertions(+), 20 deletions(-) diff --git a/NEWS b/NEWS index f340b3f2..3f560df8 100644 --- a/NEWS +++ b/NEWS @@ -8,6 +8,9 @@ bssm 1.1.6 (Release date: ) ar1_lg model when predicting past or when using simulation smoother. * Fixed a bug which caused an error when predicting past values in multivariate time series case. + * Fixed sampling of negative binomial distribution in predict method, which + used std::negative_binomial which converts non-integer phi to integer. + Sampling now uses Gamma-Poisson mixture for simulation. bssm 1.1.5 (Release date: 2021-06-14) diff --git a/R/predict.R b/R/predict.R index c9ec0a3f..2cdbb85c 100644 --- a/R/predict.R +++ b/R/predict.R @@ -141,7 +141,7 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, replace = TRUE) - theta <- t(object$theta[idx, ]) + theta <- t(object$theta[idx, , drop = FALSE]) alpha <- matrix(object$alpha[nrow(object$alpha), , idx], nrow = ncol(object$alpha)) diff --git a/README.md b/README.md index cd2741f1..4a6b6ef3 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,9 @@ bssm 1.1.6 (Release date: ) ar1_lg model when predicting past or when using simulation smoother. * Fixed a bug which caused an error when predicting past values in multivariate time series case. + * Fixed sampling of negative binomial distribution in predict method, which + used std::negative_binomial which converts non-integer phi to integer. + Sampling now uses Gamma-Poisson mixture for simulation. bssm 1.1.4 (Release date: 2021-04-13) ============== diff --git a/src/model_ssm_mng.cpp b/src/model_ssm_mng.cpp index 46ba6ea1..9833a0e1 100644 --- a/src/model_ssm_mng.cpp +++ b/src/model_ssm_mng.cpp @@ -731,13 +731,13 @@ arma::mat ssm_mng::sample_model(const unsigned int predict_type) { switch(distribution(j)) { case 1: { - std::poisson_distribution<> poisson(u(j,t) * y(j, t)); - if ((u(j,t) * y(j, t)) < poisson.max()) { - y(j, t) = poisson(engine); - } else { - y(j, t) = std::numeric_limits::quiet_NaN(); - } - } + std::poisson_distribution<> poisson(u(j,t) * y(j, t)); + if ((u(j,t) * y(j, t)) < poisson.max()) { + y(j, t) = poisson(engine); + } else { + y(j, t) = std::numeric_limits::quiet_NaN(); + } + } break; case 2: { std::binomial_distribution<> binomial(u(j,t), y(j, t)); @@ -745,9 +745,10 @@ arma::mat ssm_mng::sample_model(const unsigned int predict_type) { } break; case 3: { - std::negative_binomial_distribution<> - negative_binomial(phi(j), phi(j) / (phi(j) + u(j,t) * y(j, t))); - y(j, t) = negative_binomial(engine); + double prob = phi(j) / (phi(j) + u(j,t) * y(j, t)); + std::gamma_distribution<> gamma(phi(j), (1 - prob) / prob); + std::poisson_distribution<> poisson(gamma(engine)); + y(j, t) = poisson(engine); } break; case 4: { @@ -814,9 +815,10 @@ arma::cube ssm_mng::predict_past(const arma::mat& theta_posterior, } break; case 3: { - std::negative_binomial_distribution<> - negative_binomial(phi(j), phi(j) / (phi(j) + u(j,t) * y(j, t))); - y(j, t) = negative_binomial(engine); + double prob = phi(j) / (phi(j) + u(j,t) * y(j, t)); + std::gamma_distribution<> gamma(phi(j), (1 - prob) / prob); + std::poisson_distribution<> poisson(gamma(engine)); + y(j, t) = poisson(engine); } break; case 4: { diff --git a/src/model_ssm_ung.cpp b/src/model_ssm_ung.cpp index b3b24347..c3fc707c 100644 --- a/src/model_ssm_ung.cpp +++ b/src/model_ssm_ung.cpp @@ -809,9 +809,13 @@ arma::mat ssm_ung::sample_model(const unsigned int predict_type) { break; case 3: for (unsigned int t = 0; t < n; t++) { - std::negative_binomial_distribution<> - negative_binomial(phi, phi / (phi + u(t) * y(0, t))); - y(0, t) = negative_binomial(engine); + // std::negative_binomial_distribution<> + // negative_binomial(phi, phi / (phi + u(t) * y(0, t))); + // y(0, t) = negative_binomial(engine); + double prob = phi / (phi + u(t) * y(0, t)); + std::gamma_distribution<> gamma(phi, (1 - prob) / prob); + std::poisson_distribution<> poisson(gamma(engine)); + y(0, t) = poisson(engine); } break; case 4: @@ -897,9 +901,10 @@ arma::cube ssm_ung::predict_past(const arma::mat& theta_posterior, break; case 3: for (unsigned int t = 0; t < n; t++) { - std::negative_binomial_distribution<> - negative_binomial(phi, phi / (phi + u(t) * y(0, t))); - y(0, t) = negative_binomial(engine); + double prob = phi / (phi + u(t) * y(0, t)); + std::gamma_distribution<> gamma(phi, (1 - prob) / prob); + std::poisson_distribution<> poisson(gamma(engine)); + y(0, t) = poisson(engine); } break; case 4: From 8d2d5d53f20f93cb3a212de291c93a32a928d8fa Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 09:01:51 +0300 Subject: [PATCH 058/180] typo in tests --- tests/testthat/test_mcmc.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index ba01314e..6e671362 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -138,7 +138,7 @@ test_that("MCMC for ssm_mng work", { } } - expect_error(boostrap_filter(model, 10), NA) + expect_error(bootstrap_filter(model, 10), NA) }) test_that("MCMC results with psi-APF for Poisson model are correct", { From 9a970f003a19dc7fea1e6e0df16239a9c4325c1e Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 11:57:20 +0300 Subject: [PATCH 059/180] update docs --- man/ar1_ng.Rd | 10 ++++++++++ man/bsm_lg.Rd | 3 ++- man/bsm_ng.Rd | 4 ++-- man/predict.mcmc_output.Rd | 4 ++-- man/run_mcmc.ssm_nlg.Rd | 2 +- man/run_mcmc.ssm_sde.Rd | 4 ++-- man/run_mcmc_ng.Rd | 2 +- man/ssm_mng.Rd | 35 +++++++++++++++++++++++++++++++++++ man/ssm_nlg.Rd | 24 ++++++++++++++++++++++++ 9 files changed, 79 insertions(+), 9 deletions(-) diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 2b5816db..798141e7 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -40,3 +40,13 @@ Object of class \code{ar1_ng}. Constructs a simple non-Gaussian model where the state dynamics follow an AR(1) process. } +\examples{ +model <- ar1_ng(discoveries, rho = uniform(0.5,-1,1), + sigma = halfnormal(0.1, 1), mu = normal(0, 0, 1), + distribution = "poisson") +out <- run_mcmc(model, iter = 1e4, mcmc_type = "approx", + output_type = "summary") + +ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) + +} diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 1853c6ae..0cdc5d75 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -40,7 +40,8 @@ If missing, the seasonal component is omitted from the model.} \item{xreg}{Matrix containing covariates.} -\item{period}{Length of the seasonal component i.e. the number of} +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be at least 3.} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 5276b5d7..c7b699cf 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -57,8 +57,8 @@ term. For binomial, this is the number of trials.} \item{xreg}{Matrix containing covariates.} -\item{period}{Length of the seasonal component i.e. the number of -observations per season. Default is \code{frequency(y)}.} +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be at least 3.} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 3b2e83ff..9be56c08 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -47,8 +47,8 @@ Data frame of predicted samples. \description{ Draw samples from the posterior predictive distribution for future time points given the posterior draws of hyperparameters \eqn{\theta} and -\eqn{alpha_{n+1}}. Function can also be used to draw samples from the -posterior predictive distribution +latent state \eqn{alpha_{n+1}}. Function can also be used to draw samples +from the posterior predictive distribution \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. } \examples{ diff --git a/man/run_mcmc.ssm_nlg.Rd b/man/run_mcmc.ssm_nlg.Rd index 4e7c9a61..1f9b259e 100644 --- a/man/run_mcmc.ssm_nlg.Rd +++ b/man/run_mcmc.ssm_nlg.Rd @@ -34,7 +34,7 @@ Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} \item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and +(default, returns posterior samples of latent states alpha and hyperparameters theta), \code{"theta"} (for marginal posterior of theta), or \code{"summary"} (return the mean and variance estimates of the states and posterior samples of theta). In case of \code{"summary"}, means and diff --git a/man/run_mcmc.ssm_sde.Rd b/man/run_mcmc.ssm_sde.Rd index 92ea6083..8213db6c 100644 --- a/man/run_mcmc.ssm_sde.Rd +++ b/man/run_mcmc.ssm_sde.Rd @@ -31,8 +31,8 @@ \item{particles}{Number of state samples per MCMC iteration.} \item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and hyperparameters -theta), \code{"theta"} (for marginal posterior of theta), +(default, returns posterior samples of latent states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), or \code{"summary"} (return the mean and variance estimates of the states and posterior samples of theta). In case of \code{"summary"}, means and covariances are computed using the full output of particle filter diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd index f388d96a..a42c8d80 100644 --- a/man/run_mcmc_ng.Rd +++ b/man/run_mcmc_ng.Rd @@ -34,7 +34,7 @@ Ignored if \code{mcmc_type} is \code{"approx"}.} \item{output_type}{Either \code{"full"} -(default, returns posterior samples of states alpha and +(default, returns posterior samples of latent states alpha and hyperparameters theta), \code{"theta"} (for marginal posterior of theta), or \code{"summary"} (return the mean and variance estimates of the states and posterior samples of theta). In case of \code{"summary"}, means and diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index 831a5f9c..7cec544e 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -97,3 +97,38 @@ negative binomial distribution for each observation series \eqn{i=1,...,p}. Here k is the number of disturbance terms (which can be less than m, the number of states). } +\examples{ + +set.seed(1) +n <- 20 +x <- cumsum(rnorm(n, sd = 0.5)) +phi <- 2 +y <- cbind( + rgamma(n, shape = phi, scale = exp(x) / phi), + rbinom(n, 10, plogis(x))) + +Z <- matrix(1, 2, 1) +T <- 1 +R <- 0.5 +a1 <- 0 +P1 <- 1 + +update_fn <- function(theta) { + list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) +} + +prior_fn <- function(theta) { + ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) +} + +model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), + init_theta = c(0.5, 2), + distribution = c("gamma", "binomial"), + u = cbind(1, rep(10, n)), + update_fn = update_fn, prior_fn = prior_fn) + +# smoothing based on approximating gaussian model +ts.plot(cbind(y, fast_smoother(model)), + col = 1:3, lty = c(1, 1, 2)) + +} diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index 44ea1195..ae68904d 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -84,3 +84,27 @@ Compared to other models, these general models need a bit more effort from the user, as you must provide the several small C++ snippets which define the model structure. See examples in the vignette. } +\examples{ +\dontrun{ +set.seed(1) +n <- 50 +x <- y <- numeric(n) +y[1] <- rnorm(1, exp(x[1]), 0.1) +for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +} + +pntrs <- nlg_example_models("sin_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out <- ekf(model_nlg, 100) +ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +} +} From 3323d2ec60b35b2048028bd6ae32409415b6e1f8 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 11:57:35 +0300 Subject: [PATCH 060/180] fix iekf for nlg loglik --- src/model_ssm_nlg.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/model_ssm_nlg.cpp b/src/model_ssm_nlg.cpp index 91acf2a1..15f8aee1 100644 --- a/src/model_ssm_nlg.cpp +++ b/src/model_ssm_nlg.cpp @@ -353,7 +353,7 @@ double ssm_nlg::ekf(arma::mat& at, arma::mat& att, arma::cube& Pt, arma::cube& P Kt = Pt.slice(t) * Zg.t() * inv_cholF * inv_cholF.t(); arma::vec atthat_new = at.col(t) + Kt * vt; - diff = arma::mean(arma::square(atthat-atthat_new)); + diff = arma::mean(arma::square(atthat - atthat_new)); atthat = atthat_new; } att.col(t) = atthat; @@ -448,14 +448,14 @@ double ssm_nlg::ekf_loglik() const { vt = y.col(t) - Z_fn(t, atthat, theta, known_params, known_tv_params) - - Zg * (at.col(t) - atthat); + Zg * (at - atthat); vt.rows(na_y).zeros(); inv_cholF = arma::inv(arma::trimatu(cholF)); Kt = Pt * Zg.t() * inv_cholF * inv_cholF.t(); arma::vec atthat_new = at + Kt * vt; - diff = arma::mean(arma::square(atthat-atthat_new)); + diff = arma::mean(arma::square(atthat - atthat_new)); atthat = atthat_new; } att = atthat; @@ -562,7 +562,7 @@ double ssm_nlg::ekf_smoother(arma::mat& at, arma::cube& Pt) const { arma::vec atthat_new = at.col(t) + Kt.slice(t) * vt.col(t); - diff = arma::mean(arma::square(atthat-atthat_new)); + diff = arma::mean(arma::square(atthat - atthat_new)); atthat = atthat_new; } att.col(t) = atthat; @@ -691,7 +691,7 @@ double ssm_nlg::ekf_fast_smoother(arma::mat& at) const { Kt.slice(t) = Pt.slice(t) * Zg.t() * inv_cholF * inv_cholF.t(); arma::vec atthat_new = at.col(t) + Kt.slice(t) * vt.col(t); - diff = arma::mean(arma::square(atthat-atthat_new)); + diff = arma::mean(arma::square(atthat - atthat_new)); atthat = atthat_new; } att.col(t) = atthat; From 10bf8f6e5b046bae3efe3ad990df8290ebce6127 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 11:58:24 +0300 Subject: [PATCH 061/180] remove p==1 warning --- R/models.R | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/R/models.R b/R/models.R index d1359483..ac0ce007 100644 --- a/R/models.R +++ b/R/models.R @@ -422,12 +422,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) - if (p == 1) { - warning(paste( - "Found univariate series as input but defining multivariate model.", - "It can be more efficient to use 'ssm_ulg' instead of 'ssm_mlg'.", - sep = " ")) - } + # create Z Z <- check_Z(Z, p, n, multivariate = TRUE) m <- dim(Z)[2] @@ -562,12 +557,7 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) - if (p == 1) { - warning(paste( - "Found univariate series as input but defining multivariate model.", - "It can be more efficient to use 'ssm_ung' instead of 'ssm_mng'.", - sep = " ")) - } + if(length(distribution) == 1) distribution <- rep(distribution, p) check_distribution(y, distribution) if(length(phi) == 1) phi <- rep(phi, p) From ba11142fe42a71fe14834eaa0f22469fd4f71251 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 11:58:49 +0300 Subject: [PATCH 062/180] update ekf test with missing data --- tests/testthat/test_ekpf.R | 8 ++++---- tests/testthat/test_predict.R | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index 07e3f77f..6f96efbe 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -61,10 +61,10 @@ test_that("EKF and IEKF work", { log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state"), NA) - expect_equal(ekf(model_nlg)$logLik, 3.55814184565819) + expect_equal(ekf(model_nlg)$logLik, 2.65163101109689) expect_equal(ekf(model_nlg, iekf_iter = 2)$logLik, - logLik(model_nlg, method = "ekf", iekf_iter = 2)) - expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 3.69550903344128) + logLik(model_nlg, method = "ekf", iekf_iter = 2, particles = 0)) + expect_equal(ekf(model_nlg, iekf_iter = 1)$logLik, 2.61650080342709) expect_equal(ekf(model_nlg, iekf_iter = 1), ekf(model_nlg, iekf_iter = 2)) @@ -75,7 +75,7 @@ test_that("EKF and IEKF work", { expect_equal(out_ekf1$alphahat, out_ekf2) expect_equal( ekf_fast_smoother(model_nlg, iekf_iter = 2), - ekf_smoother(model_nlg)$alphahat, iekf_iter = 2) + ekf_smoother(model_nlg, iekf_iter = 2)$alphahat) expect_error(ukf(model_nlg), NA) expect_error(bootstrap_filter(model_nlg, 10), NA) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 99a69aac..b8ea2fa1 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -40,11 +40,11 @@ test_that("Gaussian predictions work", { ifelse(x[1] > 1 | any(x < 0), -Inf, - sum(0.5 * x[2:3]^2)) } - expect_warning(model2 <- ssm_mlg(matrix(model$y, length(model$y), 1), + expect_error(model2 <- ssm_mlg(matrix(model$y, length(model$y), 1), Z = 1, H = model$H, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, init_theta = c(rho = 0.9, sigma = 0.1, sd_y = 0.1), - update_fn = ufun, prior_fn = pfun, state_names = "signal")) + update_fn = ufun, prior_fn = pfun, state_names = "signal"), NA) set.seed(123) expect_error(mcmc_results2 <- run_mcmc(model2, iter = 1000), From 196087272722d4c4678e53ffe1ae4a3e6b0009ca Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 5 Sep 2021 18:21:58 +0300 Subject: [PATCH 063/180] fix non-const phi --- NEWS | 2 ++ src/distr_consts.cpp | 4 ++-- src/model_bsm_ng.cpp | 2 +- src/model_ssm_mng.cpp | 6 +++--- src/model_ssm_ung.cpp | 6 +++--- tests/testthat/test_mcmc.R | 26 ++++++++++++++++++++++++++ 6 files changed, 37 insertions(+), 9 deletions(-) diff --git a/NEWS b/NEWS index 3f560df8..d2c3d285 100644 --- a/NEWS +++ b/NEWS @@ -8,6 +8,8 @@ bssm 1.1.6 (Release date: ) ar1_lg model when predicting past or when using simulation smoother. * Fixed a bug which caused an error when predicting past values in multivariate time series case. + * Fixed log-likelihood computation for gamma model with non-constant shape + parameter when using (intermediate) Gaussian approximation. * Fixed sampling of negative binomial distribution in predict method, which used std::negative_binomial which converts non-integer phi to integer. Sampling now uses Gamma-Poisson mixture for simulation. diff --git a/src/distr_consts.cpp b/src/distr_consts.cpp index 77729f4b..4856d5fb 100644 --- a/src/distr_consts.cpp +++ b/src/distr_consts.cpp @@ -26,7 +26,7 @@ double negbin_log_const(double y, double u, double phi) { } double gamma_log_const(double y, double u, double phi) { - return phi * std::log(phi) - std::lgamma(phi) + (phi - 1) * std::log(y) - std::log(u); + return phi * std::log(phi) - std::lgamma(phi) + (phi - 1) * std::log(y) - phi * std::log(u); } @@ -61,7 +61,7 @@ double negbin_log_const(const arma::vec& y, const arma::vec& u, double phi) { double gamma_log_const(const arma::vec& y, const arma::vec& u, double phi) { double res = 0; for(unsigned int i = 0; i < y.n_elem; i++) { - res += phi * std::log(phi) - std::lgamma(phi) + (phi - 1) * std::log(y(i)) - std::log(u(i)); + res += phi * std::log(phi) - std::lgamma(phi) + (phi - 1) * std::log(y(i)) - phi * std::log(u(i)); } return res; } diff --git a/src/model_bsm_ng.cpp b/src/model_bsm_ng.cpp index 18a41657..7427dc2c 100644 --- a/src/model_bsm_ng.cpp +++ b/src/model_bsm_ng.cpp @@ -91,7 +91,7 @@ double bsm_ng::log_prior_pdf(const arma::vec& x, const Rcpp::Function prior_fn) double log_prior = 0.0; arma::vec pars = x; - if (arma::accu(fixed) < 3 || noise) { + if (arma::accu(fixed) < 3 || noise || phi_est) { pars.subvec(0, pars.n_elem - xreg.n_cols - 1) = arma::exp(pars.subvec(0, pars.n_elem - xreg.n_cols - 1)); // add jacobian diff --git a/src/model_ssm_mng.cpp b/src/model_ssm_mng.cpp index 9833a0e1..11d368a1 100644 --- a/src/model_ssm_mng.cpp +++ b/src/model_ssm_mng.cpp @@ -378,10 +378,10 @@ arma::vec ssm_mng::log_weights(const unsigned int t, const arma::cube& alpha) c std::log(phi(j) + u(j,t) * std::exp(simsignal(j))); break; case 4 : - weights(i) += -phi(j) * simsignal(j) - (y(j,t) * phi(j) * exp(-simsignal(j)) / u(j,t)); + weights(i) -= phi(j) * (simsignal(j) + (y(j,t) * exp(-simsignal(j)) / u(j,t))); break; case 5 : - weights(i) += -0.5 * std::pow((y(j,t) - simsignal(j)) / phi(j), 2.0); + weights(i) -= 0.5 * std::pow((y(j,t) - simsignal(j)) / phi(j), 2.0); break; } weights(i) += @@ -429,7 +429,7 @@ arma::vec ssm_mng::log_obs_density(const unsigned int t, std::log(phi(j) + u(j,t) * std::exp(simsignal(j))); break; case 4 : - weights(i) += -phi(j) * simsignal(j) - (y(j,t) * phi(j) * exp(-simsignal(j)) / u(j,t)); + weights(i) += -phi(j) * (simsignal(j) + (y(j,t) * exp(-simsignal(j)) / u(j, t))); break; case 5 : weights(i) += -0.5 * std::pow((y(j,t) - simsignal(j)) / phi(j), 2.0); diff --git a/src/model_ssm_ung.cpp b/src/model_ssm_ung.cpp index c3fc707c..882e221f 100644 --- a/src/model_ssm_ung.cpp +++ b/src/model_ssm_ung.cpp @@ -283,7 +283,7 @@ void ssm_ung::update_scales() { case 4 : for(unsigned int t = 0; t < n; t++) { if (arma::is_finite(y(t))) { - scales(t) = -phi * mode_estimate(t) - (y(t) * phi * exp(-mode_estimate(t)) / u(t)) + + scales(t) = -phi * (mode_estimate(t) + (y(t) * exp(-mode_estimate(t)) / u(t))) + 0.5 * std::pow((approx_model.y(t) - mode_estimate(t)) / approx_model.H(t), 2.0); } } @@ -426,7 +426,7 @@ arma::vec ssm_ung::log_weights( for (unsigned int i = 0; i < alpha.n_slices; i++) { double simsignal = arma::as_scalar(D(t * Dtv) + Z.col(t * Ztv).t() * alpha.slice(i).col(t) + xbeta(t)); - weights(i) = -phi * simsignal - (y(t) * phi * exp(-simsignal) / u(t)) + + weights(i) = -phi * (simsignal + (y(t) * exp(-simsignal) / u(t))) + 0.5 * std::pow((approx_model.y(t) - simsignal) / approx_model.H(t), 2.0); } break; @@ -483,7 +483,7 @@ arma::vec ssm_ung::log_obs_density(const unsigned int t, for (unsigned int i = 0; i < alpha.n_slices; i++) { double simsignal = arma::as_scalar(D(t * Dtv) + Z.col(t * Ztv).t() * alpha.slice(i).col(t) + xbeta(t)); - weights(i) = -phi * simsignal - (y(t) * phi * exp(-simsignal) / u(t)); + weights(i) = -phi * (simsignal + (y(t) * exp(-simsignal) / u(t))); } break; diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 6e671362..432334ab 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -189,6 +189,32 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { }) + +test_that("MCMC using SPDK for Gamma model works", { + + set.seed(123) + n <- 20 + u <- rgamma(n, 3, 1) + phi <- 5 + x <- cumsum(rnorm(n, 0, 0.5)) + y <- rgamma(n, shape = phi, scale = u * exp(x) / phi) + model_bssm <- bsm_ng(y, + sd_level = gamma(0.1, 2, 10), u = u, phi = gamma(2, 2, 0.1), + distribution = "gamma", P1 = 2) + + expect_error(mcmc_gamma <- run_mcmc(model_bssm, sampling_method = "spdk", + iter = 1000, particles = 5, seed = 42), NA) + + expect_gt(mcmc_gamma$acceptance_rate, 0) + expect_gte(min(mcmc_gamma$theta), 0) + expect_lt(max(mcmc_gamma$theta), Inf) + expect_true(is.finite(sum(mcmc_gamma$alpha))) + + expect_lt(sum(abs(summary(mcmc_gamma)[,"Mean"] - + c(0.520146284042284, 2.17575390744017))), 0.3) + +}) + test_that("MCMC results for SV model using IS-correction are correct", { set.seed(123) expect_error(model_bssm <- svm(rnorm(10), rho = uniform(0.95, -0.999, 0.999), From aa7ba116e3aaa9680f2fb43a3727be06abca5916 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 6 Sep 2021 16:46:48 +0300 Subject: [PATCH 064/180] identical to equal due to CRAN test without long doubles --- tests/testthat/test_as_data_frame.R | 2 +- tests/testthat/test_sde.R | 162 ++++++++++++++-------------- 2 files changed, 81 insertions(+), 83 deletions(-) diff --git a/tests/testthat/test_as_data_frame.R b/tests/testthat/test_as_data_frame.R index 814318be..e720fd9b 100644 --- a/tests/testthat/test_as_data_frame.R +++ b/tests/testthat/test_as_data_frame.R @@ -42,7 +42,7 @@ test_that("expanded and not expanded data frame work equally for states", { expect_error(d <- as.data.frame(mcmc_bsm, variable = "theta"), NA) expect_error(sumr <- summary(mcmc_bsm, variable = "both", return_se = TRUE), NA) - expect_identical(mean(d$value[d$variable == "sd_y"]), + expect_equal(mean(d$value[d$variable == "sd_y"]), sumr$theta["sd_y", "Mean"]) }) diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index 39243747..677bd1a0 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -1,96 +1,95 @@ context("Test SDE") - -code <- ' - #include - // [[Rcpp::depends(RcppArmadillo)]] - // [[Rcpp::interfaces(r, cpp)]] - - // Drift function - // [[Rcpp::export]] - double drift(const double x, const arma::vec& theta) { - return theta(0) * x; - } - // diffusion function - // [[Rcpp::export]] - double diffusion(const double x, const arma::vec& theta) { - return std::max(0.0, theta(1) * x); - } - // Derivative of the diffusion function - // [[Rcpp::export]] - double ddiffusion(const double x, const arma::vec& theta) { - return theta(1) * (x > 0.0); - } - - // log-density of the prior - // [[Rcpp::export]] - double log_prior_pdf(const arma::vec& theta) { +test_that("MCMC for SDE works", { + skip_on_cran() + code <- ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] - double log_pdf = 0.0; - if(theta(0) <= -0.05 || theta(1) <= 0.0 || theta(2) <= 0.0) { - log_pdf = -std::numeric_limits::infinity(); + // Drift function + // [[Rcpp::export]] + double drift(const double x, const arma::vec& theta) { + return theta(0) * x; } - else { - log_pdf = R::dnorm(theta(0), 0, 0.05, 1) + - R::dnorm(theta(1), 0, 0.5, 1) + - R::dnorm(theta(2), 1, 0.1, 1); + // diffusion function + // [[Rcpp::export]] + double diffusion(const double x, const arma::vec& theta) { + return std::max(0.0, theta(1) * x); + } + // Derivative of the diffusion function + // [[Rcpp::export]] + double ddiffusion(const double x, const arma::vec& theta) { + return theta(1) * (x > 0.0); } - return log_pdf; - } - - // log-density of observations - // given vector of sampled states alpha - // [[Rcpp::export]] - arma::vec log_obs_density(const double y, - const arma::vec& alpha, const arma::vec& theta) { - arma::vec log_pdf(alpha.n_elem); - for (unsigned int i = 0; i < alpha.n_elem; i++) { - log_pdf(i) = R::dnorm(y, std::log(alpha(i)), theta(2), 1); + // log-density of the prior + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + + double log_pdf = 0.0; + if(theta(0) <= -0.05 || theta(1) <= 0.0 || theta(2) <= 0.0) { + log_pdf = -std::numeric_limits::infinity(); + } + else { + log_pdf = R::dnorm(theta(0), 0, 0.05, 1) + + R::dnorm(theta(1), 0, 0.5, 1) + + R::dnorm(theta(2), 1, 0.1, 1); + } + return log_pdf; } - return log_pdf; - } - - // [[Rcpp::export]] - Rcpp::List create_xptrs() { - // typedef for a pointer of drift/volatility function - typedef double (*fnPtr)(const double x, const arma::vec& theta); - // typedef for log_prior_pdf - typedef double (*prior_fnPtr)(const arma::vec& theta); - // typedef for log_obs_density - typedef arma::vec (*obs_fnPtr)(const double y, - const arma::vec& alpha, const arma::vec& theta); - return Rcpp::List::create( - Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), - Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), - Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), - Rcpp::Named("prior") = - Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), - Rcpp::Named("obs_density") = - Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + // log-density of observations + // given vector of sampled states alpha + // [[Rcpp::export]] + arma::vec log_obs_density(const double y, + const arma::vec& alpha, const arma::vec& theta) { + + arma::vec log_pdf(alpha.n_elem); + for (unsigned int i = 0; i < alpha.n_elem; i++) { + log_pdf(i) = R::dnorm(y, std::log(alpha(i)), theta(2), 1); + } + return log_pdf; + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + // typedef for a pointer of drift/volatility function + typedef double (*fnPtr)(const double x, const arma::vec& theta); + // typedef for log_prior_pdf + typedef double (*prior_fnPtr)(const arma::vec& theta); + // typedef for log_obs_density + typedef arma::vec (*obs_fnPtr)(const double y, + const arma::vec& alpha, const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), + Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), + Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), + Rcpp::Named("prior") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), + Rcpp::Named("obs_density") = + Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + } + ' + Rcpp::sourceCpp(code = code) + pntrs <- create_xptrs() + set.seed(1) + x <- 1 + y <- rep(NA, 10) + dt <- 1 + mu <- 0.05 + sigma_x <- 0.3 + sigma_y <- 1 + for (k in 1:10) { + x <- x*exp((mu-0.5*sigma_x^2)*dt + sqrt(dt) * rnorm(1, sd=sigma_x)) + y[k] <- rnorm(1, mean=log(x), sd=sigma_y) } -' -Rcpp::sourceCpp(code = code) -pntrs <- create_xptrs() -set.seed(1) -x <- 1 -y <- rep(NA, 10) -dt <- 1 -mu <- 0.05 -sigma_x <- 0.3 -sigma_y <- 1 -for (k in 1:10) { - x <- x*exp((mu-0.5*sigma_x^2)*dt + sqrt(dt) * rnorm(1, sd=sigma_x)) - y[k] <- rnorm(1, mean=log(x), sd=sigma_y) -} - - -test_that("MCMC for SDE works", { + set.seed(123) model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, c(0.05, 0.3, 1), x0 = 1, positive = TRUE) - + expect_error(ll <- logLik(model, 10000, L = 3), NA) expect_equal(ll, -17, tol = 1) expect_error(out_bsf <- bootstrap_filter(model, 1000, L = 3), NA) @@ -115,5 +114,4 @@ test_that("MCMC for SDE works", { expect_gt(out2$acceptance_rate, 0) expect_equal(mean(colMeans(out$theta)-colMeans(out2$theta)), 0, tol = 1) - }) From 6b51e280a0d0ca79d3e8a5a552913602378f3ac4 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 6 Sep 2021 16:47:10 +0300 Subject: [PATCH 065/180] remove few duplicate lines --- R/print_mcmc.R | 4 ---- 1 file changed, 4 deletions(-) diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 6cdf7716..b6e619fb 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -168,8 +168,6 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) if (return_se) { - mean_theta <- weighted_mean(theta, w) - sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) se_theta_is <- weighted_se(theta, w) se_theta <- sqrt(apply(theta, 2, function(x) asymptotic_var(x, w))) ess_theta <- (sd_theta / se_theta)^2 @@ -188,8 +186,6 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sd_theta <- apply(theta, 2, sd) if (return_se) { - mean_theta <- colMeans(theta) - sd_theta <- apply(theta, 2, sd) se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) ess_theta <- (sd_theta / se_theta)^2 summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), From 87151947268fb2ac1d99d1ec2353b2258e38118b Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 6 Sep 2021 20:35:36 +0300 Subject: [PATCH 066/180] check integer and real arguments --- R/approx.R | 11 ++++++----- R/as_bssm.R | 1 + R/bootstrap_filter.R | 16 +++++++++++++--- R/check_arguments.R | 20 ++++++++++++++++++++ R/ekpf_filter.R | 1 + R/importance_sample.R | 6 ++++-- R/kfilter.R | 2 ++ R/particle_smoother.R | 14 +++++++++++--- R/post_correction.R | 16 ++++++++++++++-- R/predict.R | 3 +++ R/print_mcmc.R | 9 +++++++++ R/run_mcmc.R | 42 ++++++++++++++++++++++++++++++++++++++++-- R/sim_smoother.R | 9 +++++++++ R/smoother.R | 5 +++++ 14 files changed, 138 insertions(+), 17 deletions(-) diff --git a/R/approx.R b/R/approx.R index 2f0c6cb9..3ec73d73 100644 --- a/R/approx.R +++ b/R/approx.R @@ -34,8 +34,9 @@ gaussian_approx <- function(model, max_iter, conv_tol, ...) { gaussian_approx.nongaussian <- function(model, max_iter = 100, conv_tol = 1e-8, ...) { - model$max_iter <- max_iter - model$conv_tol <- conv_tol + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$conv_tol <- check_positive_real(conv_tol, "conv_tol") + model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 @@ -67,9 +68,9 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, gaussian_approx.ssm_nlg <- function(model, max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { - model$max_iter <- max_iter - model$conv_tol <- conv_tol - model$iekf_iter <- iekf_iter + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$conv_tol <- check_positive_real(conv_tol, "conv_tol") + model$iekf_iter <- check_integer(iekf_iter, "iekf_iter") out <- gaussian_approx_model_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/as_bssm.R b/R/as_bssm.R index 5eaf5714..f6ffca92 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -22,6 +22,7 @@ #' as_bssm <- function(model, kappa = 100, ...) { + kappa <- check_positive_real(kappa, "kappa") if (!requireNamespace("KFAS", quietly = TRUE)) { stop("This function depends on the KFAS package. ", call. = FALSE) } diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 90f0473c..366d9182 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -36,9 +36,14 @@ bootstrap_filter.gaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { if (missing(particles)) { - particles <- match.call(expand.dots = TRUE)$particles - if (!is.null(particles)) particles <- particles + nsim <- eval(match.call(expand.dots = TRUE)$nsim) + if (!is.null(nsim)) { + warning(paste0("Argument `nsim` is deprecated. Use argument `particles`", + "instead.", sep = " ")) + particles <- nsim + } } + particles <- check_integer(particles, "particles") out <- bsf(model, particles, seed, TRUE, model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- @@ -74,6 +79,7 @@ bootstrap_filter.nongaussian <- function(model, particles, particles <- nsim } } + particles <- check_integer(particles, "particles") model$distribution <- pmatch(model$distribution, @@ -104,6 +110,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, particles <- nsim } } + particles <- check_integer(particles, "particles") out <- bsf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, @@ -126,7 +133,8 @@ bootstrap_filter.ssm_nlg <- function(model, particles, bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { - if (L < 1) stop("Discretization level L must be larger than 0.") + if (test_count(L, positive=TRUE)) + stop("Discretization level L must be a positive integer.") if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -137,6 +145,8 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, } } + particles <- check_integer(particles, "particles") + out <- bsf_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, diff --git a/R/check_arguments.R b/R/check_arguments.R index 89fd60a8..ab9e9691 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -339,3 +339,23 @@ check_H <- function(x, p, n, multivariate = FALSE) { } x } + + +check_integer <- function(x, name = "particles", positive = TRUE, max = 1e7) { + if (!test_count(x, positive)) { + stop(paste0("Argument '", name, "' should be a ", + ifelse(positive, "positive", "non-negative"), " integer. ")) + } + if (x > max) { + stop(paste0("I don't believe you want '", name, "' > ", max, + ". If you really do, file an issue at Github.")) + } + as.integer(x) +} + +check_positive_real <- function(x, name) { + if (!check_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { + stop(paste0("Argument '", name, "' should be positive real value.")) + } + x +} diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 51bb2984..26cc703e 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -54,6 +54,7 @@ ekpf_filter.ssm_nlg <- function(object, particles, particles <- nsim } } + particles <- check_integer(particles, "particles") out <- ekpf(t(object$y), object$Z, object$H, object$T, object$R, object$Z_gn, object$T_gn, object$a1, object$P1, diff --git a/R/importance_sample.R b/R/importance_sample.R index 1b60eeb5..3acba417 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -45,8 +45,10 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1), ...) { - model$max_iter <- max_iter - model$conv_tol <- conv_tol + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$conv_tol <- check_positive_real(conv_tol, "conv_tol") + nsim <- check_integer(nsim, "nsim") + model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 diff --git a/R/kfilter.R b/R/kfilter.R index 6892c952..e582620d 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -64,6 +64,8 @@ kfilter.nongaussian <- function(model, ...) { #' @export ekf <- function(model, iekf_iter = 0) { + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + out <- ekf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 473d89e9..dbd9eacf 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -84,6 +84,8 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", } } + particles <- check_integer(particles, "particles") + if (method == "psi") { out <- list() out$alpha <- gaussian_psi_smoother(model, particles, seed, @@ -128,11 +130,12 @@ particle_smoother.nongaussian <- function(model, particles, particles <- nsim } } + particles <- check_integer(particles, "particles") + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$conv_tol <- check_positive_real(conv_tol, "conv_tol") method <- match.arg(method, c("bsf", "psi")) - - model$max_iter <- max_iter - model$conv_tol <- conv_tol + model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 @@ -168,6 +171,10 @@ particle_smoother.ssm_nlg <- function(model, particles, particles <- nsim } } + particles <- check_integer(particles, "particles") + max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + conv_tol <- check_positive_real(conv_tol, "conv_tol") + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) method <- match.arg(method, c("bsf", "psi", "ekf")) @@ -218,6 +225,7 @@ particle_smoother.ssm_sde <- function(model, particles, L, particles <- nsim } } + particles <- check_integer(particles, "particles") out <- bsf_smoother_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, diff --git a/R/post_correction.R b/R/post_correction.R index f8eec817..539e25bb 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -73,6 +73,15 @@ get_map <- function(x) { suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), replications = 100, seed = sample(.Machine$integer.max, size = 1)) { + replications <- check_integer(replications, "replications") + if (!test_integerish(candidates, lower = 1, any.missing = FALSE, + min.len = 1)) { + stop("Argument 'candidates' should be vector of positive integers. ") + } + if (max(candidates) > 1e7) + stop(paste("I don't believe you want to use over 1e7 particles", + "If you really do, please file an issue at Github.", sep = " ")) + if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") theta <- get_map(mcmc_output) @@ -94,7 +103,7 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), theta, candidates, replications, seed) } else stop(paste("Function 'suggest_N' is only available for models of", - "class 'nongaussian' and 'nlg_ssm'.", sep = " ")) + "class 'nongaussian' and 'nlg_ssm'.", sep = " ")) } list(N = candidates[which(out < 1)[1]], results = data.frame(N = candidates, sd = out)) @@ -209,6 +218,9 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), post_correct <- function(model, mcmc_output, particles, threads = 1L, is_type = "is2", seed = sample(.Machine$integer.max, size = 1)) { + particles <- check_integer(particles, "particles") + threads <- check_integer(threads, "threads") + if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") is_type <- pmatch(match.arg(is_type, paste0("is", 1:3)), paste0("is", 1:3)) @@ -237,7 +249,7 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, mcmc_output$counts, t(mcmc_output$theta), mcmc_output$modes) } else stop(paste("Function 'post_correct' is only available for models of", - "class 'nongaussian' and 'ssm_nlg'.", sep = " ")) + "class 'nongaussian' and 'ssm_nlg'.", sep = " ")) } mcmc_output$weights <- out$weights mcmc_output$posterior <- mcmc_output$posterior + out$posterior diff --git a/R/predict.R b/R/predict.R index 2cdbb85c..56951cb1 100644 --- a/R/predict.R +++ b/R/predict.R @@ -117,6 +117,9 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, future = TRUE, seed = sample(.Machine$integer.max, size = 1), ...) { + nsim <- check_integer(nsim, "nsim") + if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") + type <- match.arg(type, c("response", "mean", "state")) if (object$output_type != 1) diff --git a/R/print_mcmc.R b/R/print_mcmc.R index b6e619fb..ce9f8282 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -153,6 +153,12 @@ print.mcmc_output <- function(x, ...) { summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) { + + if (!test_flag(return_se)) + stop("Argument 'return_se' should be TRUE or FALSE. ") + if (!test_flag(only_theta)) + stop("Argument 'only_theta' should be TRUE or FALSE. ") + if (only_theta) { variable <- "theta" warning(paste("Argument 'only_theta' is deprecated. Use argument", @@ -285,6 +291,9 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", expand_sample <- function(x, variable = "theta", times, states, by_states = TRUE) { + if (!test_flag(by_states)) + stop("Argument 'by_states' should be TRUE or FALSE. ") + variable <- match.arg(variable, c("theta", "states")) if (x$mcmc_type %in% paste0("is", 1:3)) warning(paste("Input is based on a IS-weighted MCMC, the results", diff --git a/R/run_mcmc.R b/R/run_mcmc.R index e87591df..bb20259c 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -85,6 +85,13 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { + if (!test_flag(end_adaptive_phase)) + stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + + threads <- check_integer(threads, "threads") + thin <- check_integer(thin, "thin", max = 100) + iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_integer(burnin, "burnin", max = 1e12) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -329,14 +336,26 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { + if (!test_flag(end_adaptive_phase)) + stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", "instead.", sep = " ")) particles <- nsim + particles <- check_integer(particles, "particles") } + } else { + particles <- check_integer(particles, "particles") } + threads <- check_integer(threads, "threads") + max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + conv_tol <- check_positive_real(conv_tol, "conv_tol") + thin <- check_integer(thin, "thin", max = 100) + iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_integer(burnin, "burnin", max = 1e12) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -507,14 +526,27 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { + if (!test_flag(end_adaptive_phase)) + stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste("Argument `nsim` is deprecated. Use argument `particles`", "instead.", sep = " ")) particles <- nsim + particles <- check_integer(particles, "particles") } + } else { + particles <- check_integer(particles, "particles") } + threads <- check_integer(threads, "threads") + max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + conv_tol <- check_positive_real(conv_tol, "conv_tol") + thin <- check_integer(thin, "thin", max = 100) + iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_integer(burnin, "burnin", max = 1e12) + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -692,6 +724,9 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", "and reconstruct the model.", sep = " ")) } + if (!test_flag(end_adaptive_phase)) + stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -699,9 +734,12 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", "instead.", sep = " ")) particles <- nsim } - } else { - if (particles <= 0) stop("particles should be positive integer.") } + particles <- check_integer(particles, "particles") + threads <- check_integer(threads, "threads") + thin <- check_integer(thin, "thin", max = 100) + iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_integer(burnin, "burnin", max = 1e12) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 37137c92..31b7998b 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -30,6 +30,10 @@ sim_smoother <- function(model, nsim, seed, use_antithetic = FALSE, ...) { sim_smoother.gaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = FALSE, ...) { + nsim <- check_integer(nsim, "nsim") + if (!test_flag(use_antithetic)) + stop("Argument 'use_antithetic' should be TRUE or FALSE. ") + out <- gaussian_sim_smoother(model, nsim, use_antithetic, seed, model_type(model)) rownames(out) <- names(model$a1) @@ -40,6 +44,11 @@ sim_smoother.gaussian <- function(model, nsim = 1, #' @export sim_smoother.nongaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = FALSE, ...) { + + nsim <- check_integer(nsim, "nsim") + if (!test_flag(use_antithetic)) + stop("Argument 'use_antithetic' should be TRUE or FALSE. ") + sim_smoother(gaussian_approx(model), nsim = nsim, use_antithetic = use_antithetic, seed = seed) } diff --git a/R/smoother.R b/R/smoother.R index 164a8fed..b06962d5 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -75,6 +75,8 @@ smoother.nongaussian <- function(model, ...) { #' @rdname ekf_smoother ekf_smoother <- function(model, iekf_iter = 0) { + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + out <- ekf_smoother_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, @@ -92,6 +94,9 @@ ekf_smoother <- function(model, iekf_iter = 0) { #' @export ekf_fast_smoother <- function(model, iekf_iter = 0) { + + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + out <- ekf_fast_smoother_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, From 824e289f84b7b117920383fa583a3403961ed960 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 6 Sep 2021 21:47:53 +0300 Subject: [PATCH 067/180] import checkmate --- DESCRIPTION | 96 +++++----- R/RcppExports.R | 454 ++++++++++++++++++++++---------------------- R/as_bssm.R | 13 +- R/bssm-package.R | 1 + R/check_arguments.R | 2 +- R/post_correction.R | 2 +- R/predict.R | 6 +- R/run_mcmc.R | 4 +- 8 files changed, 289 insertions(+), 289 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 34ff12f9..3e87717e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,49 +1,47 @@ -Package: bssm -Type: Package -Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space - Models -Version: 1.1.6 -Authors@R: - c(person(given = "Jouni", - family = "Helske", - role = c("aut", "cre"), - email = "jouni.helske@iki.fi", - comment = c(ORCID = "0000-0001-7130-793X")), - person(given = "Matti", - family = "Vihola", - role = "aut", - comment = c(ORCID = "0000-0002-8041-7222"))) -Description: Efficient methods for Bayesian inference of state space models - via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel - importance sampling type weighted estimators - (Vihola, Helske, and Franks, 2020, ). - Gaussian, Poisson, binomial, negative binomial, and Gamma - observation densities and basic stochastic volatility models - with linear-Gaussian state dynamics, - as well as general non-linear Gaussian models and discretised - diffusion models are supported. -License: GPL (>= 2) -Depends: R (>= 3.5.0) -Suggests: - covr, - dplyr, - ggplot2 (>= 2.0.0), - Hmisc, - KFAS (>= 1.2.1), - knitr (>= 1.11), - MASS, - ramcmc, - rmarkdown (>= 0.8.1), - sde, - sitmo, - testthat -Imports: coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) -LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo -SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -RoxygenNote: 7.1.1 -VignetteBuilder: knitr -BugReports: https://github.com/helske/bssm/issues -URL: https://github.com/helske/bssm -ByteCompile: true -Encoding: UTF-8 -NeedsCompilation: yes +Package: bssm +Type: Package +Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space + Models +Version: 1.1.6 +Authors@R: + c(person(given = "Jouni", + family = "Helske", + role = c("aut", "cre"), + email = "jouni.helske@iki.fi", + comment = c(ORCID = "0000-0001-7130-793X")), + person(given = "Matti", + family = "Vihola", + role = "aut", + comment = c(ORCID = "0000-0002-8041-7222"))) +Description: Efficient methods for Bayesian inference of state space models + via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel + importance sampling type weighted estimators + (Vihola, Helske, and Franks, 2020, ). + Gaussian, Poisson, binomial, negative binomial, and Gamma + observation densities and basic stochastic volatility models + with linear-Gaussian state dynamics, + as well as general non-linear Gaussian models and discretised + diffusion models are supported. +License: GPL (>= 2) +Depends: R (>= 3.5.0) +Suggests: + covr, + dplyr, + ggplot2 (>= 2.0.0), + Hmisc, + KFAS (>= 1.2.1), + knitr (>= 1.11), + MASS, + rmarkdown (>= 0.8.1), + sde, + testthat +Imports: checkmate, coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) +LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo +SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) +RoxygenNote: 7.1.1 +VignetteBuilder: knitr +BugReports: https://github.com/helske/bssm/issues +URL: https://github.com/helske/bssm +ByteCompile: true +Encoding: UTF-8 +NeedsCompilation: yes diff --git a/R/RcppExports.R b/R/RcppExports.R index 176f97a5..290bcbb6 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,227 +1,227 @@ -# Generated by using Rcpp::compileAttributes() -> do not edit by hand -# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -gaussian_approx_model <- function(model_, model_type) { - .Call('_bssm_gaussian_approx_model', PACKAGE = 'bssm', model_, model_type) -} - -gaussian_approx_model_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter) { - .Call('_bssm_gaussian_approx_model_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter) -} - -bsf <- function(model_, nsim, seed, gaussian, model_type) { - .Call('_bssm_bsf', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type) -} - -bsf_smoother <- function(model_, nsim, seed, gaussian, model_type) { - .Call('_bssm_bsf_smoother', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type) -} - -bsf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { - .Call('_bssm_bsf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) -} - -bsf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { - .Call('_bssm_bsf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) -} - -ekf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { - .Call('_bssm_ekf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) -} - -ekf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { - .Call('_bssm_ekf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) -} - -ekf_fast_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { - .Call('_bssm_ekf_fast_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) -} - -ekpf <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { - .Call('_bssm_ekpf', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) -} - -ekpf_smoother <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { - .Call('_bssm_ekpf_smoother', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) -} - -importance_sample_ng <- function(model_, nsim, use_antithetic, seed, model_type) { - .Call('_bssm_importance_sample_ng', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type) -} - -gaussian_kfilter <- function(model_, model_type) { - .Call('_bssm_gaussian_kfilter', PACKAGE = 'bssm', model_, model_type) -} - -gaussian_loglik <- function(model_, model_type) { - .Call('_bssm_gaussian_loglik', PACKAGE = 'bssm', model_, model_type) -} - -nongaussian_loglik <- function(model_, nsim, sampling_method, seed, model_type) { - .Call('_bssm_nongaussian_loglik', PACKAGE = 'bssm', model_, nsim, sampling_method, seed, model_type) -} - -nonlinear_loglik <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) { - .Call('_bssm_nonlinear_loglik', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) -} - -gaussian_mcmc <- function(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) { - .Call('_bssm_gaussian_mcmc', PACKAGE = 'bssm', model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) -} - -nongaussian_pm_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { - .Call('_bssm_nongaussian_pm_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) -} - -nongaussian_da_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { - .Call('_bssm_nongaussian_da_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) -} - -nongaussian_is_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) { - .Call('_bssm_nongaussian_is_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) -} - -nonlinear_pm_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { - .Call('_bssm_nonlinear_pm_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) -} - -nonlinear_da_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { - .Call('_bssm_nonlinear_da_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) -} - -nonlinear_ekf_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) { - .Call('_bssm_nonlinear_ekf_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) -} - -nonlinear_is_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) { - .Call('_bssm_nonlinear_is_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) -} - -R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) { - .Call('_bssm_R_milstein', PACKAGE = 'bssm', x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) -} - -suggest_n_nongaussian <- function(model_, theta, candidates, replications, seed, model_type) { - .Call('_bssm_suggest_n_nongaussian', PACKAGE = 'bssm', model_, theta, candidates, replications, seed, model_type) -} - -suggest_n_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed) { - .Call('_bssm_suggest_n_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed) -} - -postcorrection_nongaussian <- function(model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) { - .Call('_bssm_postcorrection_nongaussian', PACKAGE = 'bssm', model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) -} - -postcorrection_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) { - .Call('_bssm_postcorrection_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) -} - -gaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) { - .Call('_bssm_gaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) -} - -nongaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) { - .Call('_bssm_nongaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) -} - -nonlinear_predict <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) { - .Call('_bssm_nonlinear_predict', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) -} - -gaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) { - .Call('_bssm_gaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) -} - -nongaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) { - .Call('_bssm_nongaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) -} - -nonlinear_predict_past <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) { - .Call('_bssm_nonlinear_predict_past', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) -} - -gaussian_psi_smoother <- function(model_, nsim, seed, model_type) { - .Call('_bssm_gaussian_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type) -} - -psi_smoother <- function(model_, nsim, seed, model_type) { - .Call('_bssm_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type) -} - -psi_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter) { - .Call('_bssm_psi_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter) -} - -loglik_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { - .Call('_bssm_loglik_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) -} - -bsf_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { - .Call('_bssm_bsf_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) -} - -bsf_smoother_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { - .Call('_bssm_bsf_smoother_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) -} - -sde_pm_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { - .Call('_bssm_sde_pm_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) -} - -sde_da_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { - .Call('_bssm_sde_da_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) -} - -sde_is_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) { - .Call('_bssm_sde_is_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) -} - -sde_state_sampler_bsf_is2 <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) { - .Call('_bssm_sde_state_sampler_bsf_is2', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) -} - -gaussian_smoother <- function(model_, model_type) { - .Call('_bssm_gaussian_smoother', PACKAGE = 'bssm', model_, model_type) -} - -gaussian_ccov_smoother <- function(model_, model_type) { - .Call('_bssm_gaussian_ccov_smoother', PACKAGE = 'bssm', model_, model_type) -} - -gaussian_fast_smoother <- function(model_, model_type) { - .Call('_bssm_gaussian_fast_smoother', PACKAGE = 'bssm', model_, model_type) -} - -gaussian_sim_smoother <- function(model_, nsim, use_antithetic, seed, model_type) { - .Call('_bssm_gaussian_sim_smoother', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type) -} - -ukf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa) { - .Call('_bssm_ukf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa) -} - -conditional_cov <- function(Vt, Ct, use_svd) { - invisible(.Call('_bssm_conditional_cov', PACKAGE = 'bssm', Vt, Ct, use_svd)) -} - -dmvnorm <- function(x, mean, sigma, lwr, logd) { - .Call('_bssm_dmvnorm', PACKAGE = 'bssm', x, mean, sigma, lwr, logd) -} - -precompute_dmvnorm <- function(sigma, Linv, nonzero) { - .Call('_bssm_precompute_dmvnorm', PACKAGE = 'bssm', sigma, Linv, nonzero) -} - -fast_dmvnorm <- function(x, mean, Linv, nonzero, constant) { - .Call('_bssm_fast_dmvnorm', PACKAGE = 'bssm', x, mean, Linv, nonzero, constant) -} - -psd_chol <- function(x) { - .Call('_bssm_psd_chol', PACKAGE = 'bssm', x) -} - -stratified_sample <- function(p, r, N) { - .Call('_bssm_stratified_sample', PACKAGE = 'bssm', p, r, N) -} - +# Generated by using Rcpp::compileAttributes() -> do not edit by hand +# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +gaussian_approx_model <- function(model_, model_type) { + .Call('_bssm_gaussian_approx_model', PACKAGE = 'bssm', model_, model_type) +} + +gaussian_approx_model_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter) { + .Call('_bssm_gaussian_approx_model_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter) +} + +bsf <- function(model_, nsim, seed, gaussian, model_type) { + .Call('_bssm_bsf', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type) +} + +bsf_smoother <- function(model_, nsim, seed, gaussian, model_type) { + .Call('_bssm_bsf_smoother', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type) +} + +bsf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { + .Call('_bssm_bsf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) +} + +bsf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { + .Call('_bssm_bsf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) +} + +ekf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { + .Call('_bssm_ekf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) +} + +ekf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { + .Call('_bssm_ekf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) +} + +ekf_fast_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) { + .Call('_bssm_ekf_fast_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) +} + +ekpf <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { + .Call('_bssm_ekpf', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) +} + +ekpf_smoother <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) { + .Call('_bssm_ekpf_smoother', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) +} + +importance_sample_ng <- function(model_, nsim, use_antithetic, seed, model_type) { + .Call('_bssm_importance_sample_ng', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type) +} + +gaussian_kfilter <- function(model_, model_type) { + .Call('_bssm_gaussian_kfilter', PACKAGE = 'bssm', model_, model_type) +} + +gaussian_loglik <- function(model_, model_type) { + .Call('_bssm_gaussian_loglik', PACKAGE = 'bssm', model_, model_type) +} + +nongaussian_loglik <- function(model_, nsim, sampling_method, seed, model_type) { + .Call('_bssm_nongaussian_loglik', PACKAGE = 'bssm', model_, nsim, sampling_method, seed, model_type) +} + +nonlinear_loglik <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) { + .Call('_bssm_nonlinear_loglik', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) +} + +gaussian_mcmc <- function(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) { + .Call('_bssm_gaussian_mcmc', PACKAGE = 'bssm', model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) +} + +nongaussian_pm_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { + .Call('_bssm_nongaussian_pm_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) +} + +nongaussian_da_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { + .Call('_bssm_nongaussian_da_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) +} + +nongaussian_is_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) { + .Call('_bssm_nongaussian_is_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) +} + +nonlinear_pm_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { + .Call('_bssm_nonlinear_pm_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) +} + +nonlinear_da_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { + .Call('_bssm_nonlinear_da_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) +} + +nonlinear_ekf_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) { + .Call('_bssm_nonlinear_ekf_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) +} + +nonlinear_is_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) { + .Call('_bssm_nonlinear_is_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) +} + +R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) { + .Call('_bssm_R_milstein', PACKAGE = 'bssm', x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) +} + +suggest_n_nongaussian <- function(model_, theta, candidates, replications, seed, model_type) { + .Call('_bssm_suggest_n_nongaussian', PACKAGE = 'bssm', model_, theta, candidates, replications, seed, model_type) +} + +suggest_n_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed) { + .Call('_bssm_suggest_n_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed) +} + +postcorrection_nongaussian <- function(model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) { + .Call('_bssm_postcorrection_nongaussian', PACKAGE = 'bssm', model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) +} + +postcorrection_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) { + .Call('_bssm_postcorrection_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) +} + +gaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) { + .Call('_bssm_gaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) +} + +nongaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) { + .Call('_bssm_nongaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) +} + +nonlinear_predict <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) { + .Call('_bssm_nonlinear_predict', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) +} + +gaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) { + .Call('_bssm_gaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) +} + +nongaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) { + .Call('_bssm_nongaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type) +} + +nonlinear_predict_past <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) { + .Call('_bssm_nonlinear_predict_past', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) +} + +gaussian_psi_smoother <- function(model_, nsim, seed, model_type) { + .Call('_bssm_gaussian_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type) +} + +psi_smoother <- function(model_, nsim, seed, model_type) { + .Call('_bssm_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type) +} + +psi_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter) { + .Call('_bssm_psi_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter) +} + +loglik_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { + .Call('_bssm_loglik_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) +} + +bsf_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { + .Call('_bssm_bsf_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) +} + +bsf_smoother_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) { + .Call('_bssm_bsf_smoother_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) +} + +sde_pm_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { + .Call('_bssm_sde_pm_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) +} + +sde_da_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { + .Call('_bssm_sde_da_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) +} + +sde_is_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) { + .Call('_bssm_sde_is_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) +} + +sde_state_sampler_bsf_is2 <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) { + .Call('_bssm_sde_state_sampler_bsf_is2', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) +} + +gaussian_smoother <- function(model_, model_type) { + .Call('_bssm_gaussian_smoother', PACKAGE = 'bssm', model_, model_type) +} + +gaussian_ccov_smoother <- function(model_, model_type) { + .Call('_bssm_gaussian_ccov_smoother', PACKAGE = 'bssm', model_, model_type) +} + +gaussian_fast_smoother <- function(model_, model_type) { + .Call('_bssm_gaussian_fast_smoother', PACKAGE = 'bssm', model_, model_type) +} + +gaussian_sim_smoother <- function(model_, nsim, use_antithetic, seed, model_type) { + .Call('_bssm_gaussian_sim_smoother', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type) +} + +ukf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa) { + .Call('_bssm_ukf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa) +} + +conditional_cov <- function(Vt, Ct, use_svd) { + invisible(.Call('_bssm_conditional_cov', PACKAGE = 'bssm', Vt, Ct, use_svd)) +} + +dmvnorm <- function(x, mean, sigma, lwr, logd) { + .Call('_bssm_dmvnorm', PACKAGE = 'bssm', x, mean, sigma, lwr, logd) +} + +precompute_dmvnorm <- function(sigma, Linv, nonzero) { + .Call('_bssm_precompute_dmvnorm', PACKAGE = 'bssm', sigma, Linv, nonzero) +} + +fast_dmvnorm <- function(x, mean, Linv, nonzero, constant) { + .Call('_bssm_fast_dmvnorm', PACKAGE = 'bssm', x, mean, Linv, nonzero, constant) +} + +psd_chol <- function(x) { + .Call('_bssm_psd_chol', PACKAGE = 'bssm', x) +} + +stratified_sample <- function(p, r, N) { + .Call('_bssm_stratified_sample', PACKAGE = 'bssm', p, r, N) +} + diff --git a/R/as_bssm.R b/R/as_bssm.R index f6ffca92..e5494a84 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -13,12 +13,13 @@ #' \code{ssm_mng}. #' @export #' @examples -#' library("KFAS") -#' model_KFAS <- SSModel(Nile ~ -#' SSMtrend(1, Q = 2, P1 = 1e4), H = 2) -#' model_bssm <- as_bssm(model_KFAS) -#' logLik(model_KFAS) -#' logLik(model_bssm) +#' if (require("KFAS"))) { +#' model_KFAS <- SSModel(Nile ~ +#' SSMtrend(1, Q = 2, P1 = 1e4), H = 2) +#' model_bssm <- as_bssm(model_KFAS) +#' logLik(model_KFAS) +#' logLik(model_bssm) +#' } #' as_bssm <- function(model, kappa = 100, ...) { diff --git a/R/bssm-package.R b/R/bssm-package.R index d682c89c..951535eb 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -29,6 +29,7 @@ #' @importFrom coda mcmc #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit +#' @importFrom checkmate test_count test_double test_flag test_integerish #' @useDynLib bssm NULL #' Deaths by drowning in Finland in 1969-2019 diff --git a/R/check_arguments.R b/R/check_arguments.R index ab9e9691..cbbe5ff3 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -354,7 +354,7 @@ check_integer <- function(x, name = "particles", positive = TRUE, max = 1e7) { } check_positive_real <- function(x, name) { - if (!check_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { + if (!test_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { stop(paste0("Argument '", name, "' should be positive real value.")) } x diff --git a/R/post_correction.R b/R/post_correction.R index 539e25bb..451e7105 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -208,7 +208,7 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), #' group_by(time) %>% #' summarise(mean = mean(value)) #' -#' dplyr:: bind_rows(approx = p_approx, +#' dplyr::bind_rows(approx = p_approx, #' exact = p_exact, .id = "method") %>% #' filter(time > 200) %>% #' ggplot(aes(time, mean, colour = method)) + diff --git a/R/predict.R b/R/predict.R index 56951cb1..9099e501 100644 --- a/R/predict.R +++ b/R/predict.R @@ -29,7 +29,7 @@ #' @aliases predict predict.mcmc_output #' @export #' @examples -#' require("graphics") +#' library("graphics") #' y <- log10(JohnsonJohnson) #' prior <- uniform(0.01, 0, 1) #' model <- bsm_lg(window(y, end = c(1974, 4)), sd_y = prior, @@ -44,7 +44,7 @@ #' pred <- predict(mcmc_results, future_model, type = "state", #' nsim = 1000) #' -#' require("dplyr") +#' library("dplyr") #' sumr_fit <- as.data.frame(mcmc_results, variable = "states") %>% #' group_by(time, iter) %>% #' mutate(signal = @@ -72,7 +72,7 @@ #' # lwr = quantile(value, 0.025), #' # upr = quantile(value, 0.975)) #' -#' require("ggplot2") +#' library("ggplot2") #' rbind(sumr_fit, sumr_pred) %>% #' ggplot(aes(x = time, y = mean)) + #' geom_ribbon(aes(ymin = lwr, ymax = upr), diff --git a/R/run_mcmc.R b/R/run_mcmc.R index bb20259c..b87a08b1 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -68,13 +68,13 @@ run_mcmc <- function(model, iter, ...) { #' mcmc_results <- run_mcmc(model, iter = 2e4) #' summary(mcmc_results, return_se = TRUE) #' -#' require("dplyr") +#' library("dplyr") #' sumr <- as.data.frame(mcmc_results, variable = "states") %>% #' group_by(time) %>% #' summarise(mean = mean(value), #' lwr = quantile(value, 0.025), #' upr = quantile(value, 0.975)) -#' require("ggplot2") +#' library("ggplot2") #' sumr %>% ggplot(aes(time, mean)) + #' geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + #' geom_line() + theme_bw() + From 3fa83da370d3fddbbc769a13b88b55a613704b64 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 7 Sep 2021 00:01:19 +0300 Subject: [PATCH 068/180] update namespace with checkmate imports --- NAMESPACE | 4 ++++ man/as_bssm.Rd | 13 +++++++------ man/post_correct.Rd | 2 +- man/predict.mcmc_output.Rd | 6 +++--- man/run_mcmc_g.Rd | 4 ++-- 5 files changed, 17 insertions(+), 12 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index f4cdc427..a99e71a3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -69,6 +69,10 @@ export(tnormal) export(ukf) export(uniform) importFrom(Rcpp,evalCpp) +importFrom(checkmate,test_count) +importFrom(checkmate,test_double) +importFrom(checkmate,test_flag) +importFrom(checkmate,test_integerish) importFrom(coda,mcmc) importFrom(coda,spectrum0.ar) importFrom(diagis,ess) diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 150c2a8c..735e091b 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -25,11 +25,12 @@ model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. } \examples{ -library("KFAS") -model_KFAS <- SSModel(Nile ~ - SSMtrend(1, Q = 2, P1 = 1e4), H = 2) -model_bssm <- as_bssm(model_KFAS) -logLik(model_KFAS) -logLik(model_bssm) +if (require("KFAS"))) { + model_KFAS <- SSModel(Nile ~ + SSMtrend(1, Q = 2, P1 = 1e4), H = 2) + model_bssm <- as_bssm(model_KFAS) + logLik(model_KFAS) + logLik(model_bssm) +} } diff --git a/man/post_correct.Rd b/man/post_correct.Rd index 3b76f6d5..6260f3e3 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -111,7 +111,7 @@ p_exact <- predict(out_is2, model, type = "mean", group_by(time) \%>\% summarise(mean = mean(value)) -dplyr:: bind_rows(approx = p_approx, +dplyr::bind_rows(approx = p_approx, exact = p_exact, .id = "method") \%>\% filter(time > 200) \%>\% ggplot(aes(time, mean, colour = method)) + diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 9be56c08..65d3758f 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -52,7 +52,7 @@ from the posterior predictive distribution \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. } \examples{ -require("graphics") +library("graphics") y <- log10(JohnsonJohnson) prior <- uniform(0.01, 0, 1) model <- bsm_lg(window(y, end = c(1974, 4)), sd_y = prior, @@ -67,7 +67,7 @@ future_model$y <- ts(rep(NA, 25), pred <- predict(mcmc_results, future_model, type = "state", nsim = 1000) -require("dplyr") +library("dplyr") sumr_fit <- as.data.frame(mcmc_results, variable = "states") \%>\% group_by(time, iter) \%>\% mutate(signal = @@ -95,7 +95,7 @@ sumr_pred <- pred \%>\% # lwr = quantile(value, 0.025), # upr = quantile(value, 0.975)) -require("ggplot2") +library("ggplot2") rbind(sumr_fit, sumr_pred) \%>\% ggplot(aes(x = time, y = mean)) + geom_ribbon(aes(ymin = lwr, ymax = upr), diff --git a/man/run_mcmc_g.Rd b/man/run_mcmc_g.Rd index dda7283d..40b973c9 100644 --- a/man/run_mcmc_g.Rd +++ b/man/run_mcmc_g.Rd @@ -75,13 +75,13 @@ model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), mcmc_results <- run_mcmc(model, iter = 2e4) summary(mcmc_results, return_se = TRUE) -require("dplyr") +library("dplyr") sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% group_by(time) \%>\% summarise(mean = mean(value), lwr = quantile(value, 0.025), upr = quantile(value, 0.975)) -require("ggplot2") +library("ggplot2") sumr \%>\% ggplot(aes(time, mean)) + geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + geom_line() + theme_bw() + From 1557aeb9648d3e9e7a5d3d2b34f9a07095c2694b Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 7 Sep 2021 00:53:19 +0300 Subject: [PATCH 069/180] typos, put ramcmc and sitmo back to suggests --- DESCRIPTION | 2 ++ R/as_bssm.R | 3 +-- R/bootstrap_filter.R | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3e87717e..e9be7911 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,7 +33,9 @@ Suggests: knitr (>= 1.11), MASS, rmarkdown (>= 0.8.1), + ramcmc, sde, + sitmo, testthat Imports: checkmate, coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo diff --git a/R/as_bssm.R b/R/as_bssm.R index e5494a84..a66865ea 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -13,13 +13,12 @@ #' \code{ssm_mng}. #' @export #' @examples -#' if (require("KFAS"))) { +#' library("KFAS") #' model_KFAS <- SSModel(Nile ~ #' SSMtrend(1, Q = 2, P1 = 1e4), H = 2) #' model_bssm <- as_bssm(model_KFAS) #' logLik(model_KFAS) #' logLik(model_bssm) -#' } #' as_bssm <- function(model, kappa = 100, ...) { diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 366d9182..66171acf 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -133,7 +133,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { - if (test_count(L, positive=TRUE)) + if (!test_count(L, positive=TRUE)) stop("Discretization level L must be a positive integer.") if (missing(particles)) { From 30110a133b950219114a8bae1c40255cf6c52cc8 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 7 Sep 2021 09:01:10 +0300 Subject: [PATCH 070/180] update docs --- R/models.R | 25 ++++++++++++++++++++----- man/ar1_lg.Rd | 21 ++++++++++++++++++--- man/as_bssm.Rd | 3 +-- man/ssm_mlg.Rd | 2 +- 4 files changed, 40 insertions(+), 11 deletions(-) diff --git a/R/models.R b/R/models.R index ac0ce007..3b9ac3a4 100644 --- a/R/models.R +++ b/R/models.R @@ -412,8 +412,8 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, #' data("GlobalTemp", package = "KFAS") #' model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), #' R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) -#' ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) -#' +#' ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) +#' ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { @@ -1307,9 +1307,24 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, #' @export #' @rdname ar1_lg #' @examples -#' model <- ar1_lg(BJsales, rho = uniform(0.5,-1,1), -#' sigma = halfnormal(1, 10), mu = normal(200, 200, 100), -#' sd_y = halfnormal(1, 10)) +#' set.seed(1) +#' mu <- 2 +#' phi <- 0.7 +#' sd_y <- 0.1 +#' sigma <- 0.5 +#' beta <- -1 +#' x <- rnorm(30) +#' z <- y <- numeric(30) +#' z[1] <- rnorm(1, mu, sigma / sqrt(1 - phi^2)) +#' y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) +#' for(i in 2:30) { +#' z[i] <- rnorm(1, mu * (1 - phi) + phi * z[i-1], sigma) +#' y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) +#' } +#' model <- ar1_lg(y, rho = uniform(0.5,-1,1), +#' sigma = halfnormal(1, 10), mu = normal(0, 0, 1), +#' sd_y = halfnormal(1, 10), +#' xreg = x, beta = normal(0, 0, 1)) #' out <- run_mcmc(model, iter = 2e4) #' summary(out, return_se = TRUE) ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index dc61a32d..fd172aff 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -30,9 +30,24 @@ Constructs a simple Gaussian model where the state dynamics follow an AR(1) process. } \examples{ -model <- ar1_lg(BJsales, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(200, 200, 100), - sd_y = halfnormal(1, 10)) +set.seed(1) +mu <- 2 +phi <- 0.7 +sd_y <- 0.1 +sigma <- 0.5 +beta <- -1 +x <- rnorm(30) +z <- y <- numeric(30) +z[1] <- rnorm(1, mu, sigma / sqrt(1 - phi^2)) +y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) +for(i in 2:30) { + z[i] <- rnorm(1, mu * (1 - phi) + phi * z[i-1], sigma) + y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) +} +model <- ar1_lg(y, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(0, 0, 1), + sd_y = halfnormal(1, 10), + xreg = x, beta = normal(0, 0, 1)) out <- run_mcmc(model, iter = 2e4) summary(out, return_se = TRUE) } diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 735e091b..7577aad9 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -25,12 +25,11 @@ model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. } \examples{ -if (require("KFAS"))) { +library("KFAS") model_KFAS <- SSModel(Nile ~ SSMtrend(1, Q = 2, P1 = 1e4), H = 2) model_bssm <- as_bssm(model_KFAS) logLik(model_KFAS) logLik(model_bssm) -} } diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index 9b0af57f..eb0d4d76 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -97,6 +97,6 @@ It might be useful to first construct the model without updating function data("GlobalTemp", package = "KFAS") model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) -ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat),col=1:3) +ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) } From 51fb0c134eafb37798b427dc6c7d588468c262f6 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 9 Sep 2021 18:36:13 +0300 Subject: [PATCH 071/180] add few examples --- R/models.R | 29 +++++++++++++++++++++++++---- man/ar1_lg.Rd | 9 +++++---- man/ar1_ng.Rd | 20 ++++++++++++++++++++ 3 files changed, 50 insertions(+), 8 deletions(-) diff --git a/R/models.R b/R/models.R index 3b9ac3a4..d4f71b1a 100644 --- a/R/models.R +++ b/R/models.R @@ -1203,6 +1203,26 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' #' ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) #' +#' set.seed(1) +#' n <- 30 +#' phi <- 2 +#' rho <- 0.9 +#' sigma <- 0.1 +#' beta <- 0.5 +#' x <- rnorm(n) +#' z <- y <- numeric(n) +#' z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) +#' y[1] <- rnbinom(1, mu = exp(beta * x[1] + z[1]), size = phi) +#' for(i in 2:n) { +#' z[i] <- rnorm(1, rho * z[i - 1], sigma) +#' y[i] <- rnbinom(1, mu = exp(beta * x[i] + z[i]), size = phi) +#' } +#' +#' model <- ar1_ng(y, rho = uniform(0.9, 0, 1), +#' sigma = gamma(0.1, 2, 10), mu = 0, +#' phi = gamma(2, 2, 1), distribution = "negative binomial", +#' xreg = x, beta = normal(0.5, 0, 1)) +#' ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) { @@ -1309,24 +1329,25 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, #' @examples #' set.seed(1) #' mu <- 2 -#' phi <- 0.7 +#' rho <- 0.7 #' sd_y <- 0.1 #' sigma <- 0.5 #' beta <- -1 #' x <- rnorm(30) #' z <- y <- numeric(30) -#' z[1] <- rnorm(1, mu, sigma / sqrt(1 - phi^2)) +#' z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2)) #' y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) #' for(i in 2:30) { -#' z[i] <- rnorm(1, mu * (1 - phi) + phi * z[i-1], sigma) +#' z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma) #' y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) #' } -#' model <- ar1_lg(y, rho = uniform(0.5,-1,1), +#' model <- ar1_lg(y, rho = uniform(0.5, -1, 1), #' sigma = halfnormal(1, 10), mu = normal(0, 0, 1), #' sd_y = halfnormal(1, 10), #' xreg = x, beta = normal(0, 0, 1)) #' out <- run_mcmc(model, iter = 2e4) #' summary(out, return_se = TRUE) +#' ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { y <- check_y(y) diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index fd172aff..90c9f809 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -32,22 +32,23 @@ follow an AR(1) process. \examples{ set.seed(1) mu <- 2 -phi <- 0.7 +rho <- 0.7 sd_y <- 0.1 sigma <- 0.5 beta <- -1 x <- rnorm(30) z <- y <- numeric(30) -z[1] <- rnorm(1, mu, sigma / sqrt(1 - phi^2)) +z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2)) y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) for(i in 2:30) { - z[i] <- rnorm(1, mu * (1 - phi) + phi * z[i-1], sigma) + z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma) y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) } -model <- ar1_lg(y, rho = uniform(0.5,-1,1), +model <- ar1_lg(y, rho = uniform(0.5, -1, 1), sigma = halfnormal(1, 10), mu = normal(0, 0, 1), sd_y = halfnormal(1, 10), xreg = x, beta = normal(0, 0, 1)) out <- run_mcmc(model, iter = 2e4) summary(out, return_se = TRUE) + } diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 798141e7..8dcff5b9 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -49,4 +49,24 @@ out <- run_mcmc(model, iter = 1e4, mcmc_type = "approx", ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) +set.seed(1) +n <- 30 +phi <- 2 +rho <- 0.9 +sigma <- 0.1 +beta <- 0.5 +x <- rnorm(n) +z <- y <- numeric(n) +z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) +y[1] <- rnbinom(1, mu = exp(beta * x[1] + z[1]), size = phi) +for(i in 2:n) { + z[i] <- rnorm(1, rho * z[i - 1], sigma) + y[i] <- rnbinom(1, mu = exp(beta * x[i] + z[i]), size = phi) +} + +model <- ar1_ng(y, rho = uniform(0.9, 0, 1), + sigma = gamma(0.1, 2, 10), mu = 0, + phi = gamma(2, 2, 1), distribution = "negative binomial", + xreg = x, beta = normal(0.5, 0, 1)) + } From 38ae1b37fe48e244d296a00bc08413a845b415ec Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 9 Sep 2021 18:37:11 +0300 Subject: [PATCH 072/180] use transformed scale for AR models, make deep copy in predict method --- R/predict.R | 22 +++++++++-- R/run_mcmc.R | 70 +++++++++++++++++++++++------------ man/run_mcmc_g.Rd | 6 +-- man/run_mcmc_ng.Rd | 6 +-- src/R_predict.cpp | 12 +++--- src/R_predict_past.cpp | 11 +++--- src/model_ar1_lg.cpp | 65 ++++++++++++++++++-------------- src/model_ar1_ng.cpp | 60 +++++++++++++++++++----------- tests/testthat/test_mcmc.R | 4 +- tests/testthat/test_predict.R | 33 +++++++++++------ 10 files changed, 183 insertions(+), 106 deletions(-) diff --git a/R/predict.R b/R/predict.R index 9099e501..22d1493a 100644 --- a/R/predict.R +++ b/R/predict.R @@ -134,6 +134,24 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, } if (nsim < 1) stop("Number of samples 'nsim' should be at least one.") + if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { + object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- + log(object$theta[, 1:(ncol(object$theta) - length(model$beta))]) + } else { + if (attr(object, "model_type") == "ar1_lg") { + object$theta[, c("sigma", "sd_y")] <- + log(object$theta[, c("sigma", "sd_y")]) + } else { + if (attr(object, "model_type") == "ar1_ng") { + disp <- ifelse( + object$distribution %in% c("negative binomial", "gamma"), + "phi", NULL) + object$theta[, c("sigma", disp)] <- + log(object$theta[, c("sigma", disp)]) + } + } + } + if (future) { if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { @@ -252,10 +270,6 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, if (is.null(variables)) variables <- paste("Series", 1:max(1, ncol(model$y))) - if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { - object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- - log(object$theta[, 1:(ncol(object$theta) - length(model$beta))]) - } theta <- t(object$theta[idx, ]) states <- aperm(states, c(2, 1, 3)) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index b87a08b1..5094f19e 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -47,9 +47,9 @@ run_mcmc <- function(model, iter, ...) { #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of bsm_lg -#' models) the sampling is done for transformed parameters with -#' internal_theta = log(theta). +#' (currently the standard deviation, dispersion, and autoregressive parameters +#' of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter +#' space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient). #' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the #' burnin period. Default is \code{FALSE}. #' @param threads Number of threads for state simulation. The default is 1. @@ -103,9 +103,14 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", if (inherits(model, "bsm_lg")) { names_ind <- !model$fixed & c(TRUE, TRUE, model$slope, model$seasonal) - model$theta[c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]] <- - log(pmax(1e-8, model$theta[ - c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]])) + transformed <- + c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind] + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) + } else { + if (inherits(model, "ar1_lg")) { + transformed <- c("sigma", "sd_y") + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) + } } if (missing(S)) { @@ -132,9 +137,11 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", names(model$theta) if (inherits(model, "bsm_lg")) { - out$theta[, c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]] <- - exp(out$theta[, - c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind]]) + out$theta[, transformed] <- exp(out$theta[, transformed]) + } else { + if (inherits(model, "ar1_lg")) { + out$theta[, transformed] <- exp(out$theta[, transformed]) + } } out$call <- match.call() out$seed <- seed @@ -202,9 +209,9 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' @param S Initial value for the lower triangular matrix of RAM #' algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of -#' bsm_ng models) the sampling is done for transformed parameters with -#' internal_theta = log(theta). +#' (currently the standard deviation, dispersion, and autoregressive parameters +#' of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter +#' space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient). #' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the #' burnin period. Default is \code{FALSE}. #' @param local_approx If \code{TRUE} (default), Gaussian approximation @@ -351,12 +358,15 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", particles <- check_integer(particles, "particles") } threads <- check_integer(threads, "threads") - max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) - conv_tol <- check_positive_real(conv_tol, "conv_tol") + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$conv_tol <- check_positive_real(conv_tol, "conv_tol") thin <- check_integer(thin, "thin", max = 100) iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_integer(burnin, "burnin", max = 1e12) - + if (!test_flag(local_approx)) { + stop("Argument 'local_approx' should be TRUE or FALSE. ") + } else model$local_approx <- local_approx + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() @@ -373,26 +383,36 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", pmatch(match.arg(sampling_method, c("psi", "bsf", "spdk")), c("psi", "bsf", "spdk")) - model$max_iter <- max_iter - model$conv_tol <- conv_tol - model$local_approx <- local_approx + dists <- + c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian") + model$distribution <- + pmatch(model$distribution, dists, duplicates.ok = TRUE) - 1 if (inherits(model, "bsm_ng")) { + names_ind <- c(!model$fixed & c(TRUE, model$slope, model$seasonal), model$noise) + transformed <- c( c("sd_level", "sd_slope", "sd_seasonal", "sd_noise")[names_ind], - if (model$distribution == "negative binomial") "phi") - model$theta[transformed] <- log(pmax(1e-8, model$theta[transformed])) + if (dists[model$distribution + 1] %in% dists[4:5]) "phi") + + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) + } else { + if (inherits(model, "ar1_ng")) { + + transformed <- c("sigma", + if (dists[model$distribution + 1] %in% dists[4:5]) "phi") + + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) + } } if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - model$distribution <- pmatch(model$distribution, - c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), - duplicates.ok = TRUE) - 1 + switch(mcmc_type, "da" = { @@ -437,6 +457,10 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", names(model$theta) if (inherits(model, "bsm_ng")) { out$theta[, transformed] <- exp(out$theta[, transformed]) + } else { + if (inherits(model, "ar1_ng")) { + out$theta[, transformed] <- exp(out$theta[, transformed]) + } } out$iter <- iter out$burnin <- burnin diff --git a/man/run_mcmc_g.Rd b/man/run_mcmc_g.Rd index 40b973c9..91c08014 100644 --- a/man/run_mcmc_g.Rd +++ b/man/run_mcmc_g.Rd @@ -51,9 +51,9 @@ between 0 and 1 (not checked).} \item{S}{Initial value for the lower triangular matrix of RAM algorithm, so that the covariance matrix of the Gaussian proposal distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of bsm_lg -models) the sampling is done for transformed parameters with -internal_theta = log(theta).} +(currently the standard deviation, dispersion, and autoregressive parameters +of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter +space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} \item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd index a42c8d80..39b4cbdf 100644 --- a/man/run_mcmc_ng.Rd +++ b/man/run_mcmc_ng.Rd @@ -78,9 +78,9 @@ total acceptance rate will be smaller.} \item{S}{Initial value for the lower triangular matrix of RAM algorithm, so that the covariance matrix of the Gaussian proposal distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of -bsm_ng models) the sampling is done for transformed parameters with -internal_theta = log(theta).} +(currently the standard deviation, dispersion, and autoregressive parameters +of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter +space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} \item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} diff --git a/src/R_predict.cpp b/src/R_predict.cpp index a72054a8..695a3629 100644 --- a/src/R_predict.cpp +++ b/src/R_predict.cpp @@ -15,6 +15,7 @@ arma::cube gaussian_predict(const Rcpp::List model_, const unsigned int predict_type, const unsigned int seed, const int model_type) { + // needs a deep copy via cloning in ar1 and bsm case, I don't understand why switch (model_type) { case 0: { ssm_mlg model(model_, seed); @@ -25,11 +26,11 @@ arma::cube gaussian_predict(const Rcpp::List model_, return model.predict_sample(theta, alpha, predict_type, model_["update_fn"]); } break; case 2: { - bsm_lg model(model_, seed); + bsm_lg model(Rcpp::clone(model_), seed); return model.predict_sample(theta, alpha, predict_type); } break; case 3: { - ar1_lg model(model_, seed); + ar1_lg model(Rcpp::clone(model_), seed); return model.predict_sample(theta, alpha, predict_type); } break; } @@ -42,6 +43,7 @@ arma::cube nongaussian_predict(const Rcpp::List model_, const unsigned int predict_type,const unsigned int seed, const unsigned int model_type) { + // needs a deep copy via cloning in ar1, bsm and svm cases, I don't understand why switch (model_type) { case 0: { ssm_mng model(model_, seed); @@ -52,15 +54,15 @@ arma::cube nongaussian_predict(const Rcpp::List model_, return model.predict_sample(theta, alpha, predict_type, model_["update_fn"]); } break; case 2: { - bsm_ng model(model_, seed); + bsm_ng model(Rcpp::clone(model_), seed); return model.predict_sample(theta, alpha, predict_type); } break; case 3: { - svm model(model_, seed); + svm model(Rcpp::clone(model_), seed); return model.predict_sample(theta, alpha, predict_type); } break; case 4: { - ar1_ng model(model_, seed); + ar1_ng model(Rcpp::clone(model_), seed); return model.predict_sample(theta, alpha, predict_type); } break; } diff --git a/src/R_predict_past.cpp b/src/R_predict_past.cpp index 10363a8b..210d5256 100644 --- a/src/R_predict_past.cpp +++ b/src/R_predict_past.cpp @@ -15,6 +15,7 @@ arma::cube gaussian_predict_past(const Rcpp::List model_, const unsigned int predict_type, const unsigned int seed, const int model_type) { + // needs deep copy switch (model_type) { case 0: { ssm_mlg model(model_, seed); @@ -25,11 +26,11 @@ arma::cube gaussian_predict_past(const Rcpp::List model_, return model.predict_past(theta, alpha, predict_type, model_["update_fn"]); } break; case 2: { - bsm_lg model(model_, seed); + bsm_lg model(Rcpp::clone(model_), seed); return model.predict_past(theta, alpha, predict_type); } break; case 3: { - ar1_lg model(model_, seed); + ar1_lg model(Rcpp::clone(model_), seed); return model.predict_past(theta, alpha, predict_type); } break; } @@ -52,15 +53,15 @@ arma::cube nongaussian_predict_past(const Rcpp::List model_, return model.predict_past(theta, alpha, predict_type, model_["update_fn"]); } break; case 2: { - bsm_ng model(model_, seed); + bsm_ng model(Rcpp::clone(model_), seed); return model.predict_past(theta, alpha, predict_type); } break; case 3: { - svm model(model_, seed); + svm model(Rcpp::clone(model_), seed); return model.predict_past(theta, alpha, predict_type); } break; case 4: { - ar1_ng model(model_, seed); + ar1_ng model(Rcpp::clone(model_), seed); return model.predict_past(theta, alpha, predict_type); } break; } diff --git a/src/model_ar1_lg.cpp b/src/model_ar1_lg.cpp index d3a710b2..ab34113c 100644 --- a/src/model_ar1_lg.cpp +++ b/src/model_ar1_lg.cpp @@ -11,19 +11,19 @@ ar1_lg::ar1_lg(const Rcpp::List model, const unsigned int seed) : void ar1_lg::update_model(const arma::vec& new_theta) { - - T(0, 0, 0) = new_theta(0); - R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1), 2); + double rho = new_theta(0); + double sigma = std::exp(new_theta(1)); + T(0, 0, 0) = rho; + R(0, 0, 0) = sigma; + RR(0, 0, 0) = std::pow(sigma, 2); if (mu_est) { a1(0) = new_theta(2); - C.fill(new_theta(2) * (1.0 - new_theta(0))); + C.fill(new_theta(2) * (1.0 - rho)); } - P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); - + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(rho, 2)); if(sd_y_est) { - H(0) = new_theta(2 + mu_est); + H(0) = std::exp(new_theta(2 + mu_est)); HH(0) = std::pow(H(0), 2); } @@ -35,19 +35,19 @@ void ar1_lg::update_model(const arma::vec& new_theta) { } void ar1_lg::update_model(const arma::vec& new_theta, const Rcpp::Function update_fn) { - // sampling of all parameters is on constrained scale, would make sense to - // modify as in bsm models - T(0, 0, 0) = new_theta(0); - R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1), 2); + double rho = new_theta(0); + double sigma = std::exp(new_theta(1)); + T(0, 0, 0) = rho; + R(0, 0, 0) = sigma; + RR(0, 0, 0) = std::pow(sigma, 2); if (mu_est) { a1(0) = new_theta(2); - C.fill(new_theta(2) * (1.0 - new_theta(0))); + C.fill(new_theta(2) * (1.0 - rho)); } - P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(rho, 2)); if(sd_y_est) { - H(0) = new_theta(2 + mu_est); + H(0) = std::exp(new_theta(2 + mu_est)); HH(0) = std::pow(H(0), 2); } @@ -61,37 +61,48 @@ void ar1_lg::update_model(const arma::vec& new_theta, const Rcpp::Function updat double ar1_lg::log_prior_pdf(const arma::vec& x, const Rcpp::Function prior_fn) const { double log_prior = 0.0; - - for(unsigned int i = 0; i < x.n_elem; i++) { + arma::vec pars = x; + + // sigma + pars(1) = std::exp(pars(1)); + // sd_y + pars(2 + mu_est) = std::exp(pars(2 + mu_est)); + // add log-jacobians + log_prior += x(1) + x(2 + mu_est); + + for(unsigned int i = 0; i < pars.n_elem; i++) { switch(prior_distributions(i)) { case 0 : - if (x(i) < prior_parameters(0, i) || x(i) > prior_parameters(1, i)) { + if (pars(i) < prior_parameters(0, i) || pars(i) > prior_parameters(1, i)) { return -std::numeric_limits::infinity(); } break; case 1 : - if (x(i) < 0) { + if (pars(i) < 0) { return -std::numeric_limits::infinity(); } else { - log_prior -= 0.5 * std::pow(x(i) / prior_parameters(0, i), 2); + log_prior -= 0.5 * std::pow(pars(i) / prior_parameters(0, i), 2); } break; case 2 : - log_prior -= 0.5 * std::pow((x(i) - prior_parameters(0, i)) / prior_parameters(1, i), 2); + log_prior -= 0.5 * std::pow((pars(i) - prior_parameters(0, i)) / + prior_parameters(1, i), 2); break; case 3 : // truncated normal - if (x(i) < prior_parameters(2, i) || x(i) > prior_parameters(3, i)) { + if (pars(i) < prior_parameters(2, i) || pars(i) > prior_parameters(3, i)) { return -std::numeric_limits::infinity(); } else { - log_prior -= 0.5 * std::pow((x(i) - prior_parameters(0, i)) / prior_parameters(1, i), 2); + log_prior -= 0.5 * std::pow((pars(i) - prior_parameters(0, i)) / + prior_parameters(1, i), 2); } break; case 4 : // gamma - if (x(i) < 0) { + if (pars(i) < 0) { return -std::numeric_limits::infinity(); } else { - log_prior += (prior_parameters(0, i) - 1) * log(x(i)) - prior_parameters(1, i) * x(i); - + log_prior += (prior_parameters(0, i) - 1) * + log(pars(i)) - prior_parameters(1, i) * pars(i); + } break; } diff --git a/src/model_ar1_ng.cpp b/src/model_ar1_ng.cpp index f76142b2..f2a0ae29 100644 --- a/src/model_ar1_ng.cpp +++ b/src/model_ar1_ng.cpp @@ -11,17 +11,19 @@ ar1_ng::ar1_ng(const Rcpp::List model, const unsigned int seed) : void ar1_ng::update_model(const arma::vec& new_theta) { - T(0, 0, 0) = new_theta(0); - R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1), 2); + double rho = new_theta(0); + double sigma = std::exp(new_theta(1)); + T(0, 0, 0) = rho; + R(0, 0, 0) = sigma; + RR(0, 0, 0) = std::pow(sigma, 2); if (mu_est) { a1(0) = new_theta(2); - C.fill(new_theta(2) * (1.0 - new_theta(0))); + C.fill(new_theta(2) * (1.0 - rho)); } - P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(rho, 2)); if(phi_est) { - phi = new_theta(2 + mu_est); + phi = std::exp(new_theta(2 + mu_est)); } if(xreg.n_cols > 0) { @@ -34,17 +36,19 @@ void ar1_ng::update_model(const arma::vec& new_theta) { } void ar1_ng::update_model(const arma::vec& new_theta, const Rcpp::Function update_fn) { - T(0, 0, 0) = new_theta(0); - R(0, 0, 0) = new_theta(1); - RR(0, 0, 0) = std::pow(new_theta(1), 2); + double rho = new_theta(0); + double sigma = std::exp(new_theta(1)); + T(0, 0, 0) = rho; + R(0, 0, 0) = sigma; + RR(0, 0, 0) = std::pow(sigma, 2); if (mu_est) { a1(0) = new_theta(2); - C.fill(new_theta(2) * (1.0 - new_theta(0))); + C.fill(new_theta(2) * (1.0 - rho)); } - P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(new_theta(0), 2)); + P1(0, 0) = RR(0, 0, 0) / (1.0 - std::pow(rho, 2)); if(phi_est) { - phi = new_theta(2 + mu_est); + phi = std::exp(new_theta(2 + mu_est)); } if(xreg.n_cols > 0) { @@ -59,36 +63,50 @@ void ar1_ng::update_model(const arma::vec& new_theta, const Rcpp::Function updat double ar1_ng::log_prior_pdf(const arma::vec& x, const Rcpp::Function prior_fn) const { double log_prior = 0.0; + arma::vec pars = x; + + // sigma + pars(1) = std::exp(pars(1)); + // add log-jacobian + log_prior += x(1); + // phi + if (phi_est) { + pars(2 + mu_est) = std::exp(pars(2 + mu_est)); + log_prior += x(2 + mu_est); + } - for(unsigned int i = 0; i < x.n_elem; i++) { + for(unsigned int i = 0; i < pars.n_elem; i++) { switch(prior_distributions(i)) { case 0 : - if (x(i) < prior_parameters(0, i) || x(i) > prior_parameters(1, i)) { + if (pars(i) < prior_parameters(0, i) || pars(i) > prior_parameters(1, i)) { return -std::numeric_limits::infinity(); } break; case 1 : - if (x(i) < 0) { + if (pars(i) < 0) { return -std::numeric_limits::infinity(); } else { - log_prior -= 0.5 * std::pow(x(i) / prior_parameters(0, i), 2); + log_prior -= 0.5 * std::pow(pars(i) / prior_parameters(0, i), 2); } break; case 2 : - log_prior -= 0.5 * std::pow((x(i) - prior_parameters(0, i)) / prior_parameters(1, i), 2); + log_prior -= 0.5 * std::pow((pars(i) - prior_parameters(0, i)) / + prior_parameters(1, i), 2); break; case 3 : // truncated normal - if (x(i) < prior_parameters(2, i) || x(i) > prior_parameters(3, i)) { + if (pars(i) < prior_parameters(2, i) || pars(i) > prior_parameters(3, i)) { return -std::numeric_limits::infinity(); } else { - log_prior -= 0.5 * std::pow((x(i) - prior_parameters(0, i)) / prior_parameters(1, i), 2); + log_prior -= 0.5 * std::pow((pars(i) - prior_parameters(0, i)) / + prior_parameters(1, i), 2); } break; case 4 : // gamma - if (x(i) < 0) { + if (pars(i) < 0) { return -std::numeric_limits::infinity(); } else { - log_prior += (prior_parameters(0, i) - 1) * log(x(i)) - prior_parameters(1, i) * x(i); + log_prior += (prior_parameters(0, i) - 1) * + log(pars(i)) - prior_parameters(1, i) * pars(i); } break; diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 432334ab..f1fca794 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -211,7 +211,7 @@ test_that("MCMC using SPDK for Gamma model works", { expect_true(is.finite(sum(mcmc_gamma$alpha))) expect_lt(sum(abs(summary(mcmc_gamma)[,"Mean"] - - c(0.520146284042284, 2.17575390744017))), 0.3) + c(0.542149368711246, 12.353642743311))), 0.3) }) @@ -280,8 +280,6 @@ test_that("MCMC results for SV model using IS-correction are correct", { threads = 2L)[-17]) }) - - test_that("MCMC for nonlinear models work", { skip_on_cran() set.seed(1) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index b8ea2fa1..640ad8f5 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -23,36 +23,39 @@ test_that("Gaussian predictions work", { # Posterior predictions for past observations: expect_error(yrep <- predict(mcmc_results, model, type = "response", future = FALSE, nsim = 100), NA) - expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100), NA) expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) ufun <- function(x) { T <- array(x[1]) - R <- array(x[2]) - H <- array(x[3]) + R <- array(exp(x[2])) + H <- array(exp(x[3])) dim(T) <- dim(R) <- dim(H) <- c(1, 1, 1) - P1 <- matrix(x[2]^2) / (1 - x[1]^2) + P1 <- matrix(exp(x[2])^2) / (1 - x[1]^2) list(T = T, R = R, P1 = P1, H = H) } pfun <- function(x) { - ifelse(x[1] > 1 | any(x < 0), -Inf, - sum(0.5 * x[2:3]^2)) + ifelse(x[1] > 1 | x[1] < 0, -Inf, sum(-0.5 * exp(x[2:3])^2 + x[2:3])) } expect_error(model2 <- ssm_mlg(matrix(model$y, length(model$y), 1), Z = 1, H = model$H, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, - init_theta = c(rho = 0.9, sigma = 0.1, sd_y = 0.1), + init_theta = c(rho = 0.9, sigma = log(0.1), sd_y = log(0.1)), update_fn = ufun, prior_fn = pfun, state_names = "signal"), NA) set.seed(123) expect_error(mcmc_results2 <- run_mcmc(model2, iter = 1000), NA) + # transform manually + mcmc_results2$theta[, 2:3] <- exp(mcmc_results2$theta[, 2:3]) expect_equal(mcmc_results$theta, mcmc_results2$theta) expect_equal(mcmc_results$alpha, mcmc_results2$alpha) expect_equal(mcmc_results$posterior, mcmc_results2$posterior) - + # transform back to predict... + mcmc_results2$theta[, 2:3] <- log(mcmc_results2$theta[, 2:3]) future_model2 <- model2 future_model2$y <- matrix(NA, 3, 1) expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", @@ -86,7 +89,7 @@ test_that("Non-gaussian predictions work", { nsim = 100), NA) expect_gt(mean(pred$value[pred$time == 3]), 1) - expect_lt(mean(pred$value[pred$time == 3]), 2.5) + expect_lt(mean(pred$value[pred$time == 3]), 1.5) # Posterior predictions for past observations: expect_error(yrep <- predict(mcmc_results, model, type = "response", @@ -98,25 +101,31 @@ test_that("Non-gaussian predictions work", { update_fn <- function(x) { T <- array(x[1]) - R <- array(x[2]) + R <- array(exp(x[2])) dim(T) <- dim(R) <- c(1, 1, 1) - P1 <- matrix(x[2]^2) / (1 - x[1]^2) + P1 <- matrix(exp(x[2])^2) / (1 - x[1]^2) list(T = T, R = R, P1 = P1) } prior_fn <- function(x) { - ifelse(x[1] < 0 | x[1] > 1 | x[2] < 0, -Inf, - 0.5 * x[2]^2) + ifelse(x[1] < 0 | x[1] > 1, -Inf, - 0.5 * exp(x[2])^2 + x[2]) } model2 <- ssm_ung(model$y, Z = 1, T = model$T, R = model$R, a1 = model$a1, P1 = model$P1, distribution = "poisson", update_fn = update_fn, - prior_fn = prior_fn, init_theta = model$theta, state_names = "signal") + prior_fn = prior_fn, init_theta = c(rho = 0.9, log(model$theta[2])), + state_names = "signal") set.seed(123) expect_error(mcmc_results2 <- run_mcmc(model2, iter = 1000, particles = 5), NA) + # transform manually + mcmc_results2$theta[, 2] <- exp(mcmc_results2$theta[, 2]) expect_equal(mcmc_results$theta, mcmc_results2$theta) expect_equal(mcmc_results$alpha, mcmc_results2$alpha) expect_equal(mcmc_results$posterior, mcmc_results2$posterior) + # transform back for predict + mcmc_results2$theta[, 2] <- log(mcmc_results2$theta[, 2]) + future_model2 <- model2 future_model2$y <- rep(NA, 3) expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", From d3264a5f29e68584b3f5c39db5028a870b26eaea Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 9 Sep 2021 22:56:28 +0300 Subject: [PATCH 073/180] remove extra transformation from predict method, improve docs --- R/approx.R | 7 +++++++ R/predict.R | 4 ---- R/sim_smoother.R | 15 +++++++++------ man/gaussian_approx.Rd | 9 +++++++++ man/sim_smoother.Rd | 11 +++++++---- 5 files changed, 32 insertions(+), 14 deletions(-) diff --git a/R/approx.R b/R/approx.R index 3ec73d73..ec6ec2b0 100644 --- a/R/approx.R +++ b/R/approx.R @@ -11,6 +11,9 @@ #' @param iekf_iter For non-linear models, number of iterations in iterated EKF #' (defaults to 0). #' @param ... Ignored. +#' @return Returns linear-Gaussian SSM of class \code{ssm_ulg} or +#' \code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as +#' the original model. #' @references #' Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space #' Methods. Second edition. Oxford: Oxford University Press. @@ -25,6 +28,10 @@ #' model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, #' distribution = "poisson") #' out <- gaussian_approx(model) +#' for(i in 1:7) +#' cat("Number of iterations used: ", i, ", y[1] = ", +#' gaussian_approx(model, max_iter = i, conv_tol = 0)$y[1], "\n", sep ="") +#' gaussian_approx <- function(model, max_iter, conv_tol, ...) { UseMethod("gaussian_approx", model) } diff --git a/R/predict.R b/R/predict.R index 22d1493a..50bc1b9b 100644 --- a/R/predict.R +++ b/R/predict.R @@ -154,10 +154,6 @@ predict.mcmc_output <- function(object, model, type = "response", nsim, if (future) { - if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { - object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- - log(object$theta[, 1:(ncol(object$theta) - length(model$beta))]) - } w <- object$counts * (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 31b7998b..655bf245 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -8,19 +8,22 @@ #' approximating Gaussian model. #' #' @param model Model object. -#' @param nsim Number of independent samples. -#' @param use_antithetic Use an antithetic variable for location. -#' Default is \code{FALSE}. Ignored for multivariate models. +#' @param nsim Positive integer defining the number of independent samples. +#' @param use_antithetic Use an antithetic variable for location? +#' Default is \code{FALSE}. Currently not used for multivariate models. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @return An array containing the generated samples. #' @export #' @rdname sim_smoother #' @examples -#' model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), -#' sd_y = uniform(1,0,5)) -#' sim <- sim_smoother(model, 12) +#' # only missing data, simulates from prior +#' model <- bsm_lg(rep(NA, 25), sd_level = 1, +#' sd_y = 1) +#' # use antithetic variable for location +#' sim <- sim_smoother(model, nsim = 4, use_antithetic = TRUE, seed = 1) #' ts.plot(sim[, 1, ]) +#' cor(sim[, 1, ]) sim_smoother <- function(model, nsim, seed, use_antithetic = FALSE, ...) { UseMethod("sim_smoother", model) } diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 7571740e..b8a0b0a0 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -24,6 +24,11 @@ gaussian_approx(model, max_iter, conv_tol, ...) \item{iekf_iter}{For non-linear models, number of iterations in iterated EKF (defaults to 0).} } +\value{ +Returns linear-Gaussian SSM of class \code{ssm_ulg} or +\code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as + the original model. +} \description{ Returns the approximating Gaussian model which has the same conditional mode of p(alpha|y, theta) as the original model. @@ -35,6 +40,10 @@ data("poisson_series") model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, distribution = "poisson") out <- gaussian_approx(model) +for(i in 1:7) + cat("Number of iterations used: ", i, ", y[1] = ", + gaussian_approx(model, max_iter = i, conv_tol = 0)$y[1], "\n", sep ="") + } \references{ Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 8f240d84..87fbb97d 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -32,7 +32,7 @@ sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) \item{seed}{Seed for the random number generator.} \item{use_antithetic}{Use an antithetic variable for location. -Default is \code{FALSE}. Ignored for multivariate models.} +Default is \code{FALSE}. Currently not used for multivariate models.} \item{...}{Ignored.} } @@ -49,8 +49,11 @@ For non-Gaussian/non-linear models, the simulation is based on the approximating Gaussian model. } \examples{ -model <- bsm_lg(rep(NA, 50), sd_level = uniform(1,0,5), - sd_y = uniform(1,0,5)) -sim <- sim_smoother(model, 12) +# only missing data, simulates from prior +model <- bsm_lg(rep(NA, 25), sd_level = 1, + sd_y = 1) +# use antithetic variable for location +sim <- sim_smoother(model, nsim = 4, use_antithetic = TRUE, seed = 1) ts.plot(sim[, 1, ]) +cor(sim[, 1, ]) } From 9d3a14123b6a8ca7afdbf7b3ec6f54efbf660a72 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 10 Sep 2021 23:49:23 +0300 Subject: [PATCH 074/180] add longer prior names to avoid clash with gamma primitive, use utf-8 on Rd files --- man/bssm_prior.Rd | 79 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 man/bssm_prior.Rd diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd new file mode 100644 index 00000000..1d8dc95f --- /dev/null +++ b/man/bssm_prior.Rd @@ -0,0 +1,79 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/priors.R +\name{uniform_prior} +\alias{uniform_prior} +\alias{bssm_prior_list} +\alias{uniform} +\alias{halfnormal_prior} +\alias{halfnormal} +\alias{normal_prior} +\alias{normal} +\alias{tnormal_prior} +\alias{tnormal} +\alias{gamma_prior} +\alias{gamma} +\title{Prior objects for bssm models} +\usage{ +uniform_prior(init, min, max) + +uniform(init, min, max) + +halfnormal_prior(init, sd) + +halfnormal(init, sd) + +normal_prior(init, mean, sd) + +normal(init, mean, sd) + +tnormal_prior(init, mean, sd, min = -Inf, max = Inf) + +tnormal(init, mean, sd, min = -Inf, max = Inf) + +gamma_prior(init, shape, rate) + +gamma(init, shape, rate) +} +\arguments{ +\item{init}{Initial value for the parameter, used in initializing the model +components and as a starting values in MCMC.} + +\item{min}{Lower bound of the uniform and truncated normal prior.} + +\item{max}{Upper bound of the uniform and truncated normal prior.} + +\item{sd}{Standard deviation of the (underlying i.e. non-truncated) +Normal distribution.} + +\item{mean}{Mean of the Normal prior.} + +\item{shape}{Shape parameter of the Gamma prior.} + +\item{rate}{Rate parameter of the Gamma prior.} +} +\value{ +object of class \code{bssm_prior} or \code{bssm_prior_list} in case +of multiple priors (i.e. multiple regression coefficients). +} +\description{ +These simple objects of class \code{bssm_prior} are used to construct a +prior distributions for the some of the model objects of \code{bssm} +package. Currently supported priors are uniform +(\code{uniform()}), half-normal (\code{halfnormal()}), +normal (\code{normal()}), gamma (\code{gamma}), and +truncated normal distribution (\code{tnormal()}). All parameters are +vectorized so for regression coefficient vector beta you can define prior +for example as \code{normal(0, 0, c(10, 20))}. +} +\details{ +The longer name versions of the prior functions with \code{_prior} ending +are identical with shorter versions and they are available only to +avoid clash with R's primitive function \code{gamma} (other long prior names +are just for consistent naming). +} +\examples{ +# create uniform prior on [-1, 1] for one parameter with initial value 0.2: +uniform(0.2, -1, 1) +# two normal priors at once i.e. for coefficients beta: +normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) +} From b5e33ca66a6014446119c2036a5f504a0e80b2ec Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 10 Sep 2021 23:56:37 +0300 Subject: [PATCH 075/180] remember to add files... --- NAMESPACE | 5 + R/approx.R | 6 +- R/bootstrap_filter.R | 6 +- R/bssm-package.R | 12 +- R/check_arguments.R | 12 +- R/models.R | 127 ++++----- R/particle_smoother.R | 16 +- R/post_correction.R | 20 +- R/print_mcmc.R | 2 +- R/priors.R | 96 ++++--- R/run_mcmc.R | 15 +- man/ar1_lg.Rd | 110 ++++---- man/ar1_ng.Rd | 151 ++++++----- man/as.data.frame.mcmc_output.Rd | 124 ++++----- man/as_bssm.Rd | 70 ++--- man/bootstrap_filter.Rd | 170 ++++++------ man/bsm_lg.Rd | 150 +++++------ man/bsm_ng.Rd | 214 +++++++-------- man/bssm.Rd | 58 ++-- man/drownings.Rd | 66 ++--- man/ekf_smoother.Rd | 58 ++-- man/ekpf_filter.Rd | 124 ++++----- man/exchange.Rd | 56 ++-- man/expand_sample.Rd | 62 ++--- man/gaussian_approx.Rd | 110 ++++---- man/importance_sample.Rd | 122 ++++----- man/kfilter.Rd | 86 +++--- man/logLik.Rd | 42 +-- man/logLik.ssm_nlg.Rd | 94 +++---- man/logLik.ssm_sde.Rd | 58 ++-- man/particle_smoother.Rd | 256 +++++++++--------- man/post_correct.Rd | 10 +- man/priors.Rd | 56 ---- man/run_mcmc.Rd | 55 ++-- man/run_mcmc.ssm_nlg.Rd | 220 +++++++-------- man/run_mcmc.ssm_sde.Rd | 190 ++++++------- man/run_mcmc_g.Rd | 190 ++++++------- man/run_mcmc_ng.Rd | 443 ++++++++++++++++--------------- man/sim_smoother.Rd | 118 ++++---- man/smoother.Rd | 58 ++-- man/ssm_mlg.Rd | 204 +++++++------- man/ssm_mng.Rd | 269 +++++++++---------- man/ssm_nlg.Rd | 220 +++++++-------- man/ssm_sde.Rd | 178 ++++++------- man/ssm_ulg.Rd | 422 ++++++++++++++--------------- man/ssm_ung.Rd | 232 ++++++++-------- man/suggest_N.Rd | 10 +- man/summary.mcmc_output.Rd | 76 +++--- man/svm.Rd | 104 ++++---- 49 files changed, 2783 insertions(+), 2770 deletions(-) delete mode 100644 man/priors.Rd diff --git a/NAMESPACE b/NAMESPACE index a99e71a3..149ca286 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -46,12 +46,15 @@ export(ekpf_filter) export(expand_sample) export(fast_smoother) export(gamma) +export(gamma_prior) export(gaussian_approx) export(halfnormal) +export(halfnormal_prior) export(importance_sample) export(kfilter) export(nlg_example_models) export(normal) +export(normal_prior) export(particle_smoother) export(post_correct) export(run_mcmc) @@ -66,8 +69,10 @@ export(ssm_ung) export(suggest_N) export(svm) export(tnormal) +export(tnormal_prior) export(ukf) export(uniform) +export(uniform_prior) importFrom(Rcpp,evalCpp) importFrom(checkmate,test_count) importFrom(checkmate,test_double) diff --git a/R/approx.R b/R/approx.R index ec6ec2b0..665004dd 100644 --- a/R/approx.R +++ b/R/approx.R @@ -15,12 +15,12 @@ #' \code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as #' the original model. #' @references -#' Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space +#' Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space #' Methods. Second edition. Oxford: Oxford University Press. #' -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @rdname gaussian_approx #' @examples diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 66171acf..5447f7e3 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -12,9 +12,9 @@ #' \code{Ptt}), and estimated log-likelihood (\code{logLik}). #' @export #' @references -#' Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. -#' IEE Proceedings-F, 140, 107–113. +#' Gordon, NJ, Salmond, DJ, Smith, AFM (1993) Novel approach to +#' nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, +#' 140(2), p. 107-113. #' @rdname bootstrap_filter bootstrap_filter <- function(model, particles, ...) { UseMethod("bootstrap_filter", model) diff --git a/R/bssm-package.R b/R/bssm-package.R index 951535eb..5f75b405 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -14,13 +14,13 @@ #' vignettes. #' #' @references -#' Helske J, Vihola M (2021). “bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R.” 2101.08492, +#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. ArXiv 2101.08492, #' . #' -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @docType package #' @name bssm @@ -38,7 +38,7 @@ NULL #' corresponding population sizes (in hundreds of thousands), and #' yearly average summer temperatures (June to August), based on simple #' unweighted average of three weather stations: Helsinki (Southern Finland), -#' Jyväskylä (Central Finland), and Sodankylä (Northern Finland). +#' Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). #' #' @name drownings #' @docType data @@ -51,7 +51,7 @@ NULL #' model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], #' xreg = drownings[, "summer_temp"], distribution = "poisson", #' beta = normal(0, 0, 1), -#' sd_level = gamma(0.1,2, 10), sd_slope = gamma(0, 2, 10)) +#' sd_level = gamma_prior(0.1,2, 10), sd_slope = gamma_prior(0, 2, 10)) #' #' fit <- run_mcmc(model, iter = 5000, #' output_type = "summary", mcmc_type = "approx") diff --git a/R/check_arguments.R b/R/check_arguments.R index cbbe5ff3..6eb400c9 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -129,23 +129,33 @@ check_phi <- function(x, distribution) { stop("Parameter 'phi' must be non-negative.") } } -check_u <- function(x, multivariate = FALSE) { +check_u <- function(x, y, multivariate = FALSE) { if (any(x < 0)) { stop("All values of 'u' must be non-negative.") } if (multivariate) { + if (length(x) == 1) x <- matrix(x, nrow(y), ncol(y)) + if (!is.matrix(x) && !is.numeric(x)) { stop("Argument 'u' must be a numeric matrix or multivariate ts object.") } + if(!identical(dim(y), dim(x))) + stop("Dimensions of 'y' and 'u' do not match. ") } else { + if (length(x) == 1) u <- rep(x, length(y)) if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { stop("Argument 'u' must be a numeric vector or ts object.") } + if (length(x) != length(y)) + stop("Lengths of 'u' and 'y' do not match.") + dim(x) <- NULL } if (any(is.infinite(x))) { stop("Argument 'u' must contain only finite values.") } + x } + check_prior <- function(x, name) { if (!is_prior(x) && !is_prior_list(x)) { stop(paste(name, "must be of class 'bssm_prior' or 'bssm_prior_list'.")) diff --git a/R/models.R b/R/models.R index d4f71b1a..2c7e9fd8 100644 --- a/R/models.R +++ b/R/models.R @@ -37,7 +37,8 @@ default_update_fn <- function(theta) { #' and then check the expected structure of the model components from the #' output. #' -#' @param y Observations as time series (or vector) of length \eqn{n}. +#' @param y Observations as numeric vector or univariate \code{ts} object of +#' length n. #' @param Z System matrix Z of the observation equation as m x 1 or #' m x n matrix. #' @param H Vector of standard deviations. Either a scalar or a vector of @@ -95,7 +96,8 @@ default_update_fn <- function(theta) { #' #' model <- ssm_ulg(y, Z, H, T, R, a1, P1, #' init_theta = c(1, 0.1, 0.1), -#' update_fn = update_fn, prior_fn = prior_fn) +#' update_fn = update_fn, prior_fn = prior_fn, +#' state_names = c("level", "b1", "b2")) #' #' out <- run_mcmc(model, iter = 10000) #' out @@ -272,7 +274,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' For negative binomial distribution this is the dispersion term, for #' gamma distribution this is the shape parameter, and for other #' distributions this is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' @param u Vector of positive constants for non-Gaussian models. For Poisson, #' gamma, and negative binomial distribution, this corresponds to the offset #' term. For binomial, this is the number of trials. #' @param state_names Names for the states. @@ -297,16 +299,18 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' out <- smoother(model) #' ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, - u = 1, init_theta = numeric(0), D = NULL, C = NULL, state_names, + u, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { - distribution <- match.arg(distribution, + distribution <- match.arg(tolower(distribution), c("poisson", "binomial", "negative binomial", "gamma")) y <- check_y(y, distribution = distribution) n <- length(y) + if (missing(u)) u <- rep(1, n) + u <- check_u(u, y) Z <- check_Z(Z, 1L, n) m <- dim(Z)[1] @@ -324,11 +328,6 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, check_phi(phi) - if (length(u) == 1) { - u <- rep(u, length.out = n) - } - check_u(u) - initial_mode <- matrix(init_mode(y, u, distribution), ncol = 1) if (missing(state_names)) { @@ -495,9 +494,10 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' gamma distribution this is the shape parameter, for Gaussian this is #' standard deviation, #' and for other distributions this is ignored. -#' @param u Constant parameter for non-Gaussian models. For Poisson, gamma, -#' and negative binomial distribution, this corresponds to the offset term. -#' For binomial, this is the number of trials. +#' @param u Matrix of positive constants for non-Gaussian models +#' (of same dimensions as y). For Poisson, gamma, and negative binomial +#' distribution, this corresponds to the offset term. For binomial, this is the +#' number of trials. #' @param init_theta Initial values for the unknown hyperparameters theta. #' @param D Intercept terms for observation equation, given as p x n matrix. #' @param C Intercept terms for state equation, given as m x n matrix. @@ -549,7 +549,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' col = 1:3, lty = c(1, 1, 2)) #' ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, - phi = 1, u = 1, init_theta = numeric(0), D = NULL, C = NULL, state_names, + phi = 1, u, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { # create y @@ -557,12 +557,15 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, y <- check_y(y, multivariate = TRUE) n <- nrow(y) p <- ncol(y) - + + if (missing(u)) u <- matrix(1, n, p) + u <- check_u(u, y, multivariate = TRUE) + if(length(distribution) == 1) distribution <- rep(distribution, p) check_distribution(y, distribution) if(length(phi) == 1) phi <- rep(phi, p) for(i in 1:p) { - distribution[i] <- match.arg(distribution[i], + distribution[i] <- match.arg(tolower(distribution[i]), c("poisson", "binomial", "negative binomial", "gamma", "gaussian")) check_phi(phi[i]) } @@ -583,12 +586,6 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, if (p == 1) D <- as.matrix(D) C <- check_C(C, m, n) - if (length(u) == 1) { - u <- matrix(u, n, p) - } - check_u(u) - if(!identical(dim(y), dim(u))) - stop("Dimensions of 'y' and 'u' do not match. ") initial_mode <- y for(i in 1:p) { initial_mode[, i] <- init_mode(y[, i], u[, i], distribution[i]) @@ -628,7 +625,9 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' of the noise in seasonal equation. See \link[=uniform]{priors} for details. #' If missing, the seasonal component is omitted from the model. #' @param xreg Matrix containing covariates. -#' @param beta Prior for the regression coefficients. +#' @param beta Prior for the regression coefficients. +#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +#' (in case of multiple coefficients) or missing in case of no covariates. #' @param period Length of the seasonal pattern. #' Default is \code{frequency(y)}. Must be at least 3. #' @param a1 Prior means for the initial states (level, slope, seasonals). @@ -840,10 +839,12 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' For negative binomial distribution this is the dispersion term, for #' gamma distribution this is the shape parameter, and for other distributions #' this is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' @param u Vector of positive constants for non-Gaussian models. For Poisson, #' gamma, and negative binomial distribution, this corresponds to the offset #' term. For binomial, this is the number of trials. -#' @param beta Prior for the regression coefficients. +#' @param beta Prior for the regression coefficients. +#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +#' (in case of multiple coefficients) or missing in case of no covariates. #' @param xreg Matrix containing covariates. #' @param period Length of the seasonal pattern. #' Default is \code{frequency(y)}. Must be at least 3. @@ -882,16 +883,19 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' ggplot(aes(y = value, x = iter)) + geom_line() #' } bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, - distribution, phi, u = 1, beta, xreg = NULL, period = frequency(y), + distribution, phi, u, beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, C = NULL) { - distribution <- match.arg(distribution, + distribution <- match.arg(tolower(distribution), c("poisson", "binomial", "negative binomial", "gamma")) y <- check_y(y, multivariate = FALSE, distribution) n <- length(y) + if (missing(u)) u <- rep(1, n) + u <- check_u(u, y) + regression_part <- create_regression(beta, xreg, n) notfixed <- c("level" = 1, "slope" = 1, "seasonal" = 1) @@ -1025,12 +1029,6 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, phi <- 1 } - check_u(u) - if (length(u) != n) { - u <- rep(u, length.out = n) - } - - initial_mode <- matrix(init_mode(y, u, distribution), ncol = 1) dim(T) <- c(m, m, 1) @@ -1175,20 +1173,26 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' AR(1) process. #' #' @param y Vector or a \code{\link{ts}} object of observations. -#' @param rho prior for autoregressive coefficient. +#' @param rho Prior for autoregressive coefficient. +#' Should be an object of class \code{bssm_prior}. #' @param mu A fixed value or a prior for the stationary mean of the latent -#' AR(1) process. Parameter is omitted if this is set to 0. -#' @param sigma Prior for the standard deviation of noise of the AR-process. -#' @param beta Prior for the regression coefficients. -#' @param xreg Matrix containing covariates. +#' AR(1) process. Should be an object of class \code{bssm_prior} or scalar +#' value defining a fixed mean such as 0. +#' @param sigma Prior for the standard deviation of noise of the AR-process. +#' Should be an object of class \code{bssm_prior} +#' @param beta Prior for the regression coefficients. +#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +#' (in case of multiple coefficients) or missing in case of no covariates. +#' @param xreg Matrix containing covariates with number of rows matching the +#' length of \code{y}. #' @param distribution Distribution of the observed time series. Possible #' choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and #' \code{"negative binomial"}. #' @param phi Additional parameter relating to the non-Gaussian distribution. #' For negative binomial distribution this is the dispersion term, for gamma #' distribution this is the shape parameter, and for other distributions this -#' is ignored. -#' @param u Constant parameter vector for non-Gaussian models. For Poisson, +#' is ignored. Should be an object of class \code{bssm_prior} or fixed value. +#' @param u Vector of positive constants for non-Gaussian models. For Poisson, #' gamma, and negative binomial distribution, this corresponds to the offset #' term. For binomial, this is the number of trials. #' @return Object of class \code{ar1_ng}. @@ -1209,29 +1213,33 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' rho <- 0.9 #' sigma <- 0.1 #' beta <- 0.5 +#' u <- rexp(n, 0.1) #' x <- rnorm(n) #' z <- y <- numeric(n) #' z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) -#' y[1] <- rnbinom(1, mu = exp(beta * x[1] + z[1]), size = phi) +#' y[1] <- rnbinom(1, mu = u * exp(beta * x[1] + z[1]), size = phi) #' for(i in 2:n) { #' z[i] <- rnorm(1, rho * z[i - 1], sigma) -#' y[i] <- rnbinom(1, mu = exp(beta * x[i] + z[i]), size = phi) +#' y[i] <- rnbinom(1, mu = u * exp(beta * x[i] + z[i]), size = phi) #' } #' -#' model <- ar1_ng(y, rho = uniform(0.9, 0, 1), -#' sigma = gamma(0.1, 2, 10), mu = 0, -#' phi = gamma(2, 2, 1), distribution = "negative binomial", -#' xreg = x, beta = normal(0.5, 0, 1)) +#' model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1), +#' sigma = gamma_prior(0.1, 2, 10), mu = 0., +#' phi = gamma_prior(2, 2, 1), distribution = "negative binomial", +#' xreg = x, beta = normal_prior(0.5, 0, 1), u = u) #' -ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, +ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u, beta, xreg = NULL) { - distribution <- match.arg(distribution, + distribution <- match.arg(tolower(distribution), c("poisson", "binomial", "negative binomial", "gamma")) y <- check_y(y, multivariate = FALSE, distribution) - n <- length(y) + + if (missing(u)) u <- rep(1, n) + u <- check_u(u, y) + regression_part <- create_regression(beta, xreg, n) check_rho(rho$init) @@ -1248,7 +1256,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, a1 <- mu C <- matrix(mu * (1 - rho$init)) } - distribution <- match.arg(distribution, c("poisson", "binomial", + distribution <- match.arg(tolower(distribution), c("poisson", "binomial", "negative binomial", "gamma")) use_phi <- distribution %in% c("negative binomial", "gamma") @@ -1264,11 +1272,6 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, phi <- 1 } - check_u(u) - if (length(u) != n) { - u <- rep(u, length.out = n) - } - initial_mode <- matrix(init_mode(y, u, distribution), ncol = 1) P1 <- matrix(sigma$init^2 / (1 - rho$init^2)) @@ -1315,13 +1318,15 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u = 1, beta, #' Constructs a simple Gaussian model where the state dynamics #' follow an AR(1) process. #' -#' @param y Vector or a \code{\link{ts}} object of observations. -#' @param rho prior for autoregressive coefficient. -#' @param mu A fixed value or a prior for the stationary mean of the latent +#' @param y A numeric vector or a \code{\link{ts}} object of observations. +#' @param rho A prior (as \code{bssm_prior} object) defining the prior for autoregressive coefficient. +#' @param mu A fixed value or a bssm prior for the stationary mean of the latent #' AR(1) process. Parameter is omitted if this is set to 0. -#' @param sigma Prior for the standard deviation of noise of the AR-process. -#' @param sd_y Prior for the standard deviation of observation equation. -#' @param beta Prior for the regression coefficients. +#' @param sigma A prior for the standard deviation of noise of the AR-process. +#' @param sd_y A prior for the standard deviation of observation equation. +#' @param beta Prior for the regression coefficients. +#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +#' (in case of multiple coefficients) or missing in case of no covariates. #' @param xreg Matrix containing covariates. #' @return Object of class \code{ar1_lg}. #' @export diff --git a/R/particle_smoother.R b/R/particle_smoother.R index dbd9eacf..b65b689e 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -34,24 +34,24 @@ #' of the states and #' estimated log-likelihood (\code{logLik}). #' @references -#' [1] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). +#' [1] Gordon, NJ, Salmond, DJ, Smith, AFM (1993). #' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. -#' IEE Proceedings-F, 140, 107–113. +#' IEE Proceedings-F, 140, 107-113. #' #' [2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' -#' [3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). +#' [3] Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). #' The unscented particle filter. -#' In Advances in neural information processing systems (pp. 584-590). +#' In Advances in neural information processing systems, p 584-590. #' -#' [4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. +#' [4] Jazwinski, A 1970. Stochastic Processes and Filtering Theory. #' Academic Press. #' -#' [5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian +#' [5] Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian #' nonlinear state space models. -#' Journal of Computational and Graphical Statistics, 5, 1–25. +#' Journal of Computational and Graphical Statistics, 5, 1-25. #' @export #' @rdname particle_smoother particle_smoother <- function(model, particles, ...) { diff --git a/R/post_correction.R b/R/post_correction.R index 451e7105..30241cec 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -29,14 +29,14 @@ get_map <- function(x) { #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. #' @references -#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +#'Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). #' Efficient implementation of Markov chain Monte Carlo when using an -#' unbiased likelihood estimator, Biometrika, 102, 2, 2015, Pages 295–313, +#' unbiased likelihood estimator, Biometrika, 102(2) p. 295-313, #' https://doi.org/10.1093/biomet/asu075 #' -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples #' \dontrun{ @@ -58,7 +58,7 @@ get_map <- function(x) { #' ts.plot(y / u) #' #' model <- ar1_ng(y, distribution = "binomial", -#' rho = uniform(0.5, -1, 1), sigma = gamma(1, 2, 0.001), +#' rho = uniform(0.5, -1, 1), sigma = gamma_prior(1, 2, 0.001), #' mu = normal(0, 0, 10), #' xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), #' u = u) @@ -136,14 +136,14 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. #' @references -#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). #' Efficient implementation of Markov chain Monte Carlo when using an unbiased -#' likelihood estimator. Biometrika, 102, 2, 2015, Pages 295–313, +#' likelihood estimator. Biometrika, 102, 2, 295-313, #' https://doi.org/10.1093/biomet/asu075 #' -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples #' \dontrun{ @@ -165,7 +165,7 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), #' ts.plot(y / u) #' #' model <- ar1_ng(y, distribution = "binomial", -#' rho = uniform(0.5, -1, 1), sigma = gamma(1, 2, 0.001), +#' rho = uniform(0.5, -1, 1), sigma = gamma_prior(1, 2, 0.001), #' mu = normal(0, 0, 10), #' xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), #' u = u) diff --git a/R/print_mcmc.R b/R/print_mcmc.R index ce9f8282..8db8780b 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -148,7 +148,7 @@ print.mcmc_output <- function(x, ...) { #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @export summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) { diff --git a/R/priors.R b/R/priors.R index 6603a077..48496513 100644 --- a/R/priors.R +++ b/R/priors.R @@ -1,19 +1,21 @@ -## will add more choices later... -## add recycling of parameters later - #' Prior objects for bssm models #' #' These simple objects of class \code{bssm_prior} are used to construct a -#' prior distributions for the -#' MCMC runs of \code{bssm} package. Currently supported priors are uniform +#' prior distributions for the some of the model objects of \code{bssm} +#' package. Currently supported priors are uniform #' (\code{uniform()}), half-normal (\code{halfnormal()}), #' normal (\code{normal()}), gamma (\code{gamma}), and #' truncated normal distribution (\code{tnormal()}). All parameters are #' vectorized so for regression coefficient vector beta you can define prior #' for example as \code{normal(0, 0, c(10, 20))}. #' +#' The longer name versions of the prior functions with \code{_prior} ending +#' are identical with shorter versions and they are available only to +#' avoid clash with R's primitive function \code{gamma} (other long prior names +#' are just for consistent naming). #' -#' @rdname priors +#' @rdname bssm_prior +#' @aliases bssm_prior_list #' @param init Initial value for the parameter, used in initializing the model #' components and as a starting values in MCMC. #' @param min Lower bound of the uniform and truncated normal prior. @@ -23,14 +25,15 @@ #' @param mean Mean of the Normal prior. #' @param shape Shape parameter of the Gamma prior. #' @param rate Rate parameter of the Gamma prior. -#' @return object of class \code{bssm_prior}. +#' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case +#' of multiple priors (i.e. multiple regression coefficients). #' @export #' @examples #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: #' uniform(0.2, -1, 1) #' # two normal priors at once i.e. for coefficients beta: #' normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) -uniform <- function(init, min, max) { +uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") } @@ -55,10 +58,13 @@ uniform <- function(init, min, max) { min = min, max = max), class = "bssm_prior") } } +#' @rdname bssm_prior +#' @export +uniform <- uniform_prior -#' @rdname priors +#' @rdname bssm_prior #' @export -halfnormal <- function(init, sd) { +halfnormal_prior <- function(init, sd) { if (any(!is.numeric(init), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") @@ -83,11 +89,13 @@ halfnormal <- function(init, sd) { class = "bssm_prior") } } +#' @rdname bssm_prior +#' @export +halfnormal <- halfnormal_prior - -#' @rdname priors +#' @rdname bssm_prior #' @export -normal <- function(init, mean, sd) { +normal_prior <- function(init, mean, sd) { if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") @@ -110,9 +118,14 @@ normal <- function(init, mean, sd) { sd = sd), class = "bssm_prior") } } -#' @rdname priors + +#' @rdname bssm_prior +#' @export +normal <- normal_prior + +#' @rdname bssm_prior #' @export -tnormal <- function(init, mean, sd, min = -Inf, max = Inf) { +tnormal_prior <- function(init, mean, sd, min = -Inf, max = Inf) { if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") @@ -135,25 +148,14 @@ tnormal <- function(init, mean, sd, min = -Inf, max = Inf) { sd = sd, min = min, max = max), class = "bssm_prior") } } -combine_priors <- function(x) { - - if (length(x) == 0) - return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) - - prior_distributions <- vapply(x, "[[", "prior_distribution", - FUN.VALUE = character(1)) - parameters <- matrix(NA, 4, length(prior_distributions)) - for (i in seq_along(prior_distributions)) { - parameters[1:(length(x[[i]]) - 2), i] <- as.numeric(x[[i]][-(1:2)]) - } - list(prior_distributions = - pmatch(prior_distributions, c("uniform", "halfnormal", "normal", - "tnormal", "gamma"), duplicates.ok = TRUE) - 1, - parameters = parameters) -} -#' @rdname priors + +#' @rdname bssm_prior #' @export -gamma <- function(init, shape, rate) { +tnormal <- tnormal_prior + +#' @rdname bssm_prior +#' @export +gamma_prior <- function(init, shape, rate) { if (any(!is.numeric(init), !is.numeric(shape), !is.numeric(rate))) { stop("Parameters for priors must be numeric.") @@ -168,9 +170,9 @@ gamma <- function(init, shape, rate) { if (n > 1) { structure(lapply(1:n, function(i) structure(list(prior_distribution = "gamma", - init = safe_pick(init, i), shape = safe_pick(shape, i), + init = safe_pick(init, i), shape = safe_pick(shape, i), rate = safe_pick(rate, i)), - class = "bssm_prior")), class = "bssm_prior_list") + class = "bssm_prior")), class = "bssm_prior_list") } else { structure(list(prior_distribution = "gamma", init = init, @@ -178,6 +180,30 @@ gamma <- function(init, shape, rate) { class = "bssm_prior") } } +#' @rdname bssm_prior +#' @export +gamma <- gamma_prior + +combine_priors <- function(x) { + + if (length(x) == 0) + return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) + + prior_distributions <- vapply(x, "[[", "prior_distribution", + FUN.VALUE = character(1)) + parameters <- matrix(NA, 4, length(prior_distributions)) + for (i in seq_along(prior_distributions)) { + parameters[1:(length(x[[i]]) - 2), i] <- as.numeric(x[[i]][-(1:2)]) + } + list(prior_distributions = + pmatch(prior_distributions, c("uniform", "halfnormal", "normal", + "tnormal", "gamma"), duplicates.ok = TRUE) - 1, + parameters = parameters) +} + + + + is_prior <- function(x) { inherits(x, "bssm_prior") } diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 5094f19e..db631037 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -13,9 +13,8 @@ #' and \code{\link{run_mcmc.ssm_sde}} for details. #' @export #' @rdname run_mcmc -#' @references Matti Vihola (2012). "Robust adaptive Metropolis algorithm with -#' coerced acceptance rate". Statistics and Computing, Volume 22, Issue 5, -#' pages 997--1008. +#' @references Matti Vihola (2012). Robust adaptive Metropolis algorithm with +#' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. run_mcmc <- function(model, iter, ...) { UseMethod("run_mcmc", model) } @@ -58,7 +57,7 @@ run_mcmc <- function(model, iter, ...) { #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples #' model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), @@ -225,6 +224,10 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' @param max_iter Maximum number of iterations used in Gaussian approximation. #' @param conv_tol Tolerance parameter used in Gaussian approximation. #' @param ... Ignored. +#' @references +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @examples #' set.seed(1) #' n <- 50 @@ -542,7 +545,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "bsf", burnin = floor(iter / 2), thin = 1, @@ -735,7 +738,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", L_c, L_f, burnin = floor(iter/2), thin = 1, diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index 90c9f809..3a22c86b 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -1,54 +1,56 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ar1_lg} -\alias{ar1_lg} -\title{Univariate Gaussian model with AR(1) latent process} -\usage{ -ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sigma}{Prior for the standard deviation of noise of the AR-process.} - -\item{mu}{A fixed value or a prior for the stationary mean of the latent -AR(1) process. Parameter is omitted if this is set to 0.} - -\item{sd_y}{Prior for the standard deviation of observation equation.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} -} -\value{ -Object of class \code{ar1_lg}. -} -\description{ -Constructs a simple Gaussian model where the state dynamics -follow an AR(1) process. -} -\examples{ -set.seed(1) -mu <- 2 -rho <- 0.7 -sd_y <- 0.1 -sigma <- 0.5 -beta <- -1 -x <- rnorm(30) -z <- y <- numeric(30) -z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2)) -y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) -for(i in 2:30) { - z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma) - y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) -} -model <- ar1_lg(y, rho = uniform(0.5, -1, 1), - sigma = halfnormal(1, 10), mu = normal(0, 0, 1), - sd_y = halfnormal(1, 10), - xreg = x, beta = normal(0, 0, 1)) -out <- run_mcmc(model, iter = 2e4) -summary(out, return_se = TRUE) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ar1_lg} +\alias{ar1_lg} +\title{Univariate Gaussian model with AR(1) latent process} +\usage{ +ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) +} +\arguments{ +\item{y}{A numeric vector or a \code{\link{ts}} object of observations.} + +\item{rho}{A prior (as \code{bssm_prior} object) defining the prior for autoregressive coefficient.} + +\item{sigma}{A prior for the standard deviation of noise of the AR-process.} + +\item{mu}{A fixed value or a bssm prior for the stationary mean of the latent +AR(1) process. Parameter is omitted if this is set to 0.} + +\item{sd_y}{A prior for the standard deviation of observation equation.} + +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +(in case of multiple coefficients) or missing in case of no covariates.} + +\item{xreg}{Matrix containing covariates.} +} +\value{ +Object of class \code{ar1_lg}. +} +\description{ +Constructs a simple Gaussian model where the state dynamics +follow an AR(1) process. +} +\examples{ +set.seed(1) +mu <- 2 +rho <- 0.7 +sd_y <- 0.1 +sigma <- 0.5 +beta <- -1 +x <- rnorm(30) +z <- y <- numeric(30) +z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2)) +y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) +for(i in 2:30) { + z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma) + y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) +} +model <- ar1_lg(y, rho = uniform(0.5, -1, 1), + sigma = halfnormal(1, 10), mu = normal(0, 0, 1), + sd_y = halfnormal(1, 10), + xreg = x, beta = normal(0, 0, 1)) +out <- run_mcmc(model, iter = 2e4) +summary(out, return_se = TRUE) + +} diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 8dcff5b9..80a6ec3a 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -1,72 +1,79 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ar1_ng} -\alias{ar1_ng} -\title{Non-Gaussian model with AR(1) latent process} -\usage{ -ar1_ng(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sigma}{Prior for the standard deviation of noise of the AR-process.} - -\item{mu}{A fixed value or a prior for the stationary mean of the latent -AR(1) process. Parameter is omitted if this is set to 0.} - -\item{distribution}{Distribution of the observed time series. Possible -choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and -\code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma -distribution this is the shape parameter, and for other distributions this -is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} -} -\value{ -Object of class \code{ar1_ng}. -} -\description{ -Constructs a simple non-Gaussian model where the state dynamics follow an -AR(1) process. -} -\examples{ -model <- ar1_ng(discoveries, rho = uniform(0.5,-1,1), - sigma = halfnormal(0.1, 1), mu = normal(0, 0, 1), - distribution = "poisson") -out <- run_mcmc(model, iter = 1e4, mcmc_type = "approx", - output_type = "summary") - -ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) - -set.seed(1) -n <- 30 -phi <- 2 -rho <- 0.9 -sigma <- 0.1 -beta <- 0.5 -x <- rnorm(n) -z <- y <- numeric(n) -z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) -y[1] <- rnbinom(1, mu = exp(beta * x[1] + z[1]), size = phi) -for(i in 2:n) { - z[i] <- rnorm(1, rho * z[i - 1], sigma) - y[i] <- rnbinom(1, mu = exp(beta * x[i] + z[i]), size = phi) -} - -model <- ar1_ng(y, rho = uniform(0.9, 0, 1), - sigma = gamma(0.1, 2, 10), mu = 0, - phi = gamma(2, 2, 1), distribution = "negative binomial", - xreg = x, beta = normal(0.5, 0, 1)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ar1_ng} +\alias{ar1_ng} +\title{Non-Gaussian model with AR(1) latent process} +\usage{ +ar1_ng(y, rho, sigma, mu, distribution, phi, u, beta, xreg = NULL) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{rho}{Prior for autoregressive coefficient. +Should be an object of class \code{bssm_prior}.} + +\item{sigma}{Prior for the standard deviation of noise of the AR-process. +Should be an object of class \code{bssm_prior}} + +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Should be an object of class \code{bssm_prior} or scalar +value defining a fixed mean such as 0.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should be an object of class \code{bssm_prior} or fixed value.} + +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +(in case of multiple coefficients) or missing in case of no covariates.} + +\item{xreg}{Matrix containing covariates with number of rows matching the +length of \code{y}.} +} +\value{ +Object of class \code{ar1_ng}. +} +\description{ +Constructs a simple non-Gaussian model where the state dynamics follow an +AR(1) process. +} +\examples{ +model <- ar1_ng(discoveries, rho = uniform(0.5,-1,1), + sigma = halfnormal(0.1, 1), mu = normal(0, 0, 1), + distribution = "poisson") +out <- run_mcmc(model, iter = 1e4, mcmc_type = "approx", + output_type = "summary") + +ts.plot(cbind(discoveries, exp(out$alphahat)), col = 1:2) + +set.seed(1) +n <- 30 +phi <- 2 +rho <- 0.9 +sigma <- 0.1 +beta <- 0.5 +u <- rexp(n, 0.1) +x <- rnorm(n) +z <- y <- numeric(n) +z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) +y[1] <- rnbinom(1, mu = u * exp(beta * x[1] + z[1]), size = phi) +for(i in 2:n) { + z[i] <- rnorm(1, rho * z[i - 1], sigma) + y[i] <- rnbinom(1, mu = u * exp(beta * x[i] + z[i]), size = phi) +} + +model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1), + sigma = gamma_prior(0.1, 2, 10), mu = 0., + phi = gamma_prior(2, 2, 1), distribution = "negative binomial", + xreg = x, beta = normal_prior(0.5, 0, 1), u = u) + +} diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index a5a4bf47..7f594f62 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -1,62 +1,62 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/as.data.frame.mcmc_output.R -\name{as.data.frame.mcmc_output} -\alias{as.data.frame.mcmc_output} -\title{Convert MCMC chain to data.frame} -\usage{ -\method{as.data.frame}{mcmc_output}( - x, - row.names, - optional, - variable = c("theta", "states"), - times, - states, - expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), - ... -) -} -\arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} - -\item{row.names}{Ignored.} - -\item{optional}{Ignored.} - -\item{variable}{Return samples of \code{"theta"} (default) or -\code{"states"}?} - -\item{times}{Vector of indices. In case of states, -what time points to return? Default is all.} - -\item{states}{Vector of indices. In case of states, -what states to return? Default is all.} - -\item{expand}{Should the jump-chain be expanded? -Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. -For \code{expand = FALSE} and always for IS-MCMC, -the resulting data.frame contains variable weight (= counts * IS-weights).} - -\item{...}{Ignored.} -} -\description{ -Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. -} -\examples{ -data("poisson_series") -model <- bsm_ng(y = poisson_series, -sd_slope = halfnormal(0.1, 0.1), -sd_level = halfnormal(0.1, 1), - distribution = "poisson") - -out <- run_mcmc(model, iter = 2000, particles = 10) -head(as.data.frame(out, variable = "theta")) -head(as.data.frame(out, variable = "state")) - -# don't expand the jump chain: -head(as.data.frame(out, variable = "theta", expand = FALSE)) - -# IS-weighted version: -out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") -head(as.data.frame(out_is, variable = "theta")) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as.data.frame.mcmc_output.R +\name{as.data.frame.mcmc_output} +\alias{as.data.frame.mcmc_output} +\title{Convert MCMC chain to data.frame} +\usage{ +\method{as.data.frame}{mcmc_output}( + x, + row.names, + optional, + variable = c("theta", "states"), + times, + states, + expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), + ... +) +} +\arguments{ +\item{x}{Output from \code{\link{run_mcmc}}.} + +\item{row.names}{Ignored.} + +\item{optional}{Ignored.} + +\item{variable}{Return samples of \code{"theta"} (default) or +\code{"states"}?} + +\item{times}{Vector of indices. In case of states, +what time points to return? Default is all.} + +\item{states}{Vector of indices. In case of states, +what states to return? Default is all.} + +\item{expand}{Should the jump-chain be expanded? +Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. +For \code{expand = FALSE} and always for IS-MCMC, +the resulting data.frame contains variable weight (= counts * IS-weights).} + +\item{...}{Ignored.} +} +\description{ +Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. +} +\examples{ +data("poisson_series") +model <- bsm_ng(y = poisson_series, +sd_slope = halfnormal(0.1, 0.1), +sd_level = halfnormal(0.1, 1), + distribution = "poisson") + +out <- run_mcmc(model, iter = 2000, particles = 10) +head(as.data.frame(out, variable = "theta")) +head(as.data.frame(out, variable = "state")) + +# don't expand the jump chain: +head(as.data.frame(out, variable = "theta", expand = FALSE)) + +# IS-weighted version: +out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") +head(as.data.frame(out_is, variable = "theta")) + +} diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 7577aad9..dd678871 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -1,35 +1,35 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/as_bssm.R -\name{as_bssm} -\alias{as_bssm} -\title{Convert KFAS Model to bssm Model} -\usage{ -as_bssm(model, kappa = 100, ...) -} -\arguments{ -\item{model}{Object of class \code{SSModel}.} - -\item{kappa}{For \code{SSModel} object, a prior variance for initial state -used to replace exact diffuse elements of the original model.} - -\item{...}{Additional arguments to model building functions of \code{bssm} -(such as prior and updating functions).} -} -\value{ -Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or -\code{ssm_mng}. -} -\description{ -Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} -model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or -\code{ssm_mng}. -} -\examples{ -library("KFAS") - model_KFAS <- SSModel(Nile ~ - SSMtrend(1, Q = 2, P1 = 1e4), H = 2) - model_bssm <- as_bssm(model_KFAS) - logLik(model_KFAS) - logLik(model_bssm) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as_bssm.R +\name{as_bssm} +\alias{as_bssm} +\title{Convert KFAS Model to bssm Model} +\usage{ +as_bssm(model, kappa = 100, ...) +} +\arguments{ +\item{model}{Object of class \code{SSModel}.} + +\item{kappa}{For \code{SSModel} object, a prior variance for initial state +used to replace exact diffuse elements of the original model.} + +\item{...}{Additional arguments to model building functions of \code{bssm} +(such as prior and updating functions).} +} +\value{ +Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +\code{ssm_mng}. +} +\description{ +Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} +model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +\code{ssm_mng}. +} +\examples{ +library("KFAS") + model_KFAS <- SSModel(Nile ~ + SSMtrend(1, Q = 2, P1 = 1e4), H = 2) + model_bssm <- as_bssm(model_KFAS) + logLik(model_KFAS) + logLik(model_bssm) + +} diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 256a98f1..d45257e2 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -1,85 +1,85 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bootstrap_filter.R -\name{bootstrap_filter} -\alias{bootstrap_filter} -\alias{bootstrap_filter.gaussian} -\alias{bootstrap_filter.nongaussian} -\alias{bootstrap_filter.ssm_nlg} -\alias{bootstrap_filter.ssm_sde} -\title{Bootstrap Filtering} -\usage{ -bootstrap_filter(model, particles, ...) - -\method{bootstrap_filter}{gaussian}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{nongaussian}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{ssm_nlg}( - model, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{bootstrap_filter}{ssm_sde}( - model, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}.} - -\item{particles}{Number of particles.} - -\item{...}{Ignored.} - -\item{seed}{Seed for RNG.} - -\item{L}{Integer defining the discretization level for SDE models.} -} -\value{ -List with samples (\code{alpha}) from the filtering distribution and -corresponding weights (\code{weights}), as well as filtered and predicted -states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, -\code{Ptt}), and estimated log-likelihood (\code{logLik}). -} -\description{ -Function \code{bootstrap_filter} performs a bootstrap filtering with -stratification resampling. -} -\examples{ -set.seed(1) -x <- cumsum(rnorm(50)) -y <- rnorm(50, x, 0.5) -model <- bsm_lg(y, sd_y = 0.5, sd_level = 1, P1 = 1) - -out <- bootstrap_filter(model, particles = 1000) -ts.plot(cbind(y, x, out$att), col = 1:3) -ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3) - -data("poisson_series") -model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, - P1 = diag(1, 2), distribution = "poisson") - -out <- bootstrap_filter(model, particles = 100) -ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2) - -} -\references{ -Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. -IEE Proceedings-F, 140, 107–113. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bootstrap_filter.R +\name{bootstrap_filter} +\alias{bootstrap_filter} +\alias{bootstrap_filter.gaussian} +\alias{bootstrap_filter.nongaussian} +\alias{bootstrap_filter.ssm_nlg} +\alias{bootstrap_filter.ssm_sde} +\title{Bootstrap Filtering} +\usage{ +bootstrap_filter(model, particles, ...) + +\method{bootstrap_filter}{gaussian}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{nongaussian}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{ssm_nlg}( + model, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{bootstrap_filter}{ssm_sde}( + model, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}.} + +\item{particles}{Number of particles.} + +\item{...}{Ignored.} + +\item{seed}{Seed for RNG.} + +\item{L}{Integer defining the discretization level for SDE models.} +} +\value{ +List with samples (\code{alpha}) from the filtering distribution and +corresponding weights (\code{weights}), as well as filtered and predicted +states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, +\code{Ptt}), and estimated log-likelihood (\code{logLik}). +} +\description{ +Function \code{bootstrap_filter} performs a bootstrap filtering with +stratification resampling. +} +\examples{ +set.seed(1) +x <- cumsum(rnorm(50)) +y <- rnorm(50, x, 0.5) +model <- bsm_lg(y, sd_y = 0.5, sd_level = 1, P1 = 1) + +out <- bootstrap_filter(model, particles = 1000) +ts.plot(cbind(y, x, out$att), col = 1:3) +ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3) + +data("poisson_series") +model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, + P1 = diag(1, 2), distribution = "poisson") + +out <- bootstrap_filter(model, particles = 100) +ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2) + +} +\references{ +Gordon, NJ, Salmond, DJ, Smith, AFM (1993) Novel approach to +nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, +140(2), p. 107-113. +} diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 0cdc5d75..958d8b63 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -1,74 +1,76 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{bsm_lg} -\alias{bsm_lg} -\title{Basic Structural (Time Series) Model} -\usage{ -bsm_lg( - y, - sd_y, - sd_level, - sd_slope, - sd_seasonal, - beta, - xreg = NULL, - period = frequency(y), - a1 = NULL, - P1 = NULL, - D = NULL, - C = NULL -) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{sd_y}{A fixed value or prior for the standard error of -observation equation. See \link[=uniform]{priors} for details.} - -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} - -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} - -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} - -\item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be at least 3.} - -\item{a1}{Prior means for the initial states (level, slope, seasonals). -Defaults to vector of zeros.} - -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1000 on the diagonal.} - -\item{D, C}{Intercept terms for observation and -state equations, given as a length n vector and m times n matrix -respectively (or scalar and m times 1 matrix).} -} -\value{ -Object of class \code{bsm_lg}. -} -\description{ -Constructs a basic structural model with local level or local trend -component and seasonal component. -} -\examples{ - -prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) -model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, - sd_slope = prior, sd_seasonal = prior) - -mcmc_out <- run_mcmc(model, iter = 5000) -summary(expand_sample(mcmc_out, "theta"))$stat -mcmc_out$theta[which.max(mcmc_out$posterior), ] -sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{bsm_lg} +\alias{bsm_lg} +\title{Basic Structural (Time Series) Model} +\usage{ +bsm_lg( + y, + sd_y, + sd_level, + sd_slope, + sd_seasonal, + beta, + xreg = NULL, + period = frequency(y), + a1 = NULL, + P1 = NULL, + D = NULL, + C = NULL +) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{sd_y}{A fixed value or prior for the standard error of +observation equation. See \link[=uniform]{priors} for details.} + +\item{sd_level}{A fixed value or a prior for the standard error +of the noise in level equation. See \link[=uniform]{priors} for details.} + +\item{sd_slope}{A fixed value or a prior for the standard error +of the noise in slope equation. See \link[=uniform]{priors} for details. +If missing, the slope term is omitted from the model.} + +\item{sd_seasonal}{A fixed value or a prior for the standard error +of the noise in seasonal equation. See \link[=uniform]{priors} for details. +If missing, the seasonal component is omitted from the model.} + +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +(in case of multiple coefficients) or missing in case of no covariates.} + +\item{xreg}{Matrix containing covariates.} + +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be at least 3.} + +\item{a1}{Prior means for the initial states (level, slope, seasonals). +Defaults to vector of zeros.} + +\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +Default is diagonal matrix with 1000 on the diagonal.} + +\item{D, C}{Intercept terms for observation and +state equations, given as a length n vector and m times n matrix +respectively (or scalar and m times 1 matrix).} +} +\value{ +Object of class \code{bsm_lg}. +} +\description{ +Constructs a basic structural model with local level or local trend +component and seasonal component. +} +\examples{ + +prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, + sd_slope = prior, sd_seasonal = prior) + +mcmc_out <- run_mcmc(model, iter = 5000) +summary(expand_sample(mcmc_out, "theta"))$stat +mcmc_out$theta[which.max(mcmc_out$posterior), ] +sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] + +} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index c7b699cf..50eaa1e5 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -1,106 +1,108 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{bsm_ng} -\alias{bsm_ng} -\title{Non-Gaussian Basic Structural (Time Series) Model} -\usage{ -bsm_ng( - y, - sd_level, - sd_slope, - sd_seasonal, - sd_noise, - distribution, - phi, - u = 1, - beta, - xreg = NULL, - period = frequency(y), - a1 = NULL, - P1 = NULL, - C = NULL -) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} - -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} - -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} - -\item{sd_noise}{Prior for the standard error of the additional noise term. -See \link[=uniform]{priors} for details. If missing, no additional noise -term is used.} - -\item{distribution}{Distribution of the observed time series. Possible -choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and -\code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, and for other distributions -this is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} - -\item{beta}{Prior for the regression coefficients.} - -\item{xreg}{Matrix containing covariates.} - -\item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be at least 3.} - -\item{a1}{Prior means for the initial states (level, slope, seasonals). -Defaults to vector of zeros.} - -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1e5 on the diagonal.} - -\item{C}{Intercept terms for state equation, given as a -m times n matrix.} -} -\value{ -Object of class \code{bsm_ng}. -} -\description{ -Constructs a non-Gaussian basic structural model with local level or -local trend component, a seasonal component, and regression component -(or subset of these components). -} -\examples{ -model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", - sd_level = halfnormal(0.01, 1), - sd_seasonal = halfnormal(0.01, 1), - beta = normal(0, 0, 10), - xreg = Seatbelts[, "law"]) -\dontrun{ -set.seed(123) -mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) -mcmc_out$acceptance_rate -theta <- expand_sample(mcmc_out, "theta") -plot(theta) -summary(theta) - -library("ggplot2") -ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + - geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), - geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + - guides(alpha = "none") - -# Traceplot using as.data.frame method for MCMC output: -library("dplyr") -as.data.frame(mcmc_out) \%>\% - filter(variable == "sd_level") \%>\% - ggplot(aes(y = value, x = iter)) + geom_line() -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{bsm_ng} +\alias{bsm_ng} +\title{Non-Gaussian Basic Structural (Time Series) Model} +\usage{ +bsm_ng( + y, + sd_level, + sd_slope, + sd_seasonal, + sd_noise, + distribution, + phi, + u, + beta, + xreg = NULL, + period = frequency(y), + a1 = NULL, + P1 = NULL, + C = NULL +) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{sd_level}{A fixed value or a prior for the standard error +of the noise in level equation. See \link[=uniform]{priors} for details.} + +\item{sd_slope}{A fixed value or a prior for the standard error +of the noise in slope equation. See \link[=uniform]{priors} for details. +If missing, the slope term is omitted from the model.} + +\item{sd_seasonal}{A fixed value or a prior for the standard error +of the noise in seasonal equation. See \link[=uniform]{priors} for details. +If missing, the seasonal component is omitted from the model.} + +\item{sd_noise}{Prior for the standard error of the additional noise term. +See \link[=uniform]{priors} for details. If missing, no additional noise +term is used.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are +\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, and for other distributions +this is ignored.} + +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +(in case of multiple coefficients) or missing in case of no covariates.} + +\item{xreg}{Matrix containing covariates.} + +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be at least 3.} + +\item{a1}{Prior means for the initial states (level, slope, seasonals). +Defaults to vector of zeros.} + +\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +Default is diagonal matrix with 1e5 on the diagonal.} + +\item{C}{Intercept terms for state equation, given as a +m times n matrix.} +} +\value{ +Object of class \code{bsm_ng}. +} +\description{ +Constructs a non-Gaussian basic structural model with local level or +local trend component, a seasonal component, and regression component +(or subset of these components). +} +\examples{ +model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", + sd_level = halfnormal(0.01, 1), + sd_seasonal = halfnormal(0.01, 1), + beta = normal(0, 0, 10), + xreg = Seatbelts[, "law"]) +\dontrun{ +set.seed(123) +mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) +mcmc_out$acceptance_rate +theta <- expand_sample(mcmc_out, "theta") +plot(theta) +summary(theta) + +library("ggplot2") +ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + + geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), + geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + + guides(alpha = "none") + +# Traceplot using as.data.frame method for MCMC output: +library("dplyr") +as.data.frame(mcmc_out) \%>\% + filter(variable == "sd_level") \%>\% + ggplot(aes(y = value, x = iter)) + geom_line() +} +} diff --git a/man/bssm.Rd b/man/bssm.Rd index bc6042ac..a0cbaf9e 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -1,29 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{package} -\name{bssm} -\alias{bssm} -\title{Bayesian Inference of State Space Models} -\description{ -This package contains functions for efficient Bayesian inference of state -space models, where model is assumed to be either -* Exponential family state space models, where the state equation is linear - Gaussian, and the conditional observation density is either Gaussian, - Poisson, binomial, negative binomial or Gamma density. -* Basic stochastic volatility model. -* General non-linear model with Gaussian noise terms. -* Model with continuous SDE dynamics. -For formal definition of the currently supported models and methods, as -well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, -see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package -vignettes. -} -\references{ -Helske J, Vihola M (2021). “bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R.” 2101.08492, -. - -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{package} +\name{bssm} +\alias{bssm} +\title{Bayesian Inference of State Space Models} +\description{ +This package contains functions for efficient Bayesian inference of state +space models, where model is assumed to be either +* Exponential family state space models, where the state equation is linear + Gaussian, and the conditional observation density is either Gaussian, + Poisson, binomial, negative binomial or Gamma density. +* Basic stochastic volatility model. +* General non-linear model with Gaussian noise terms. +* Model with continuous SDE dynamics. +For formal definition of the currently supported models and methods, as +well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, +see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package +vignettes. +} +\references{ +Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. ArXiv 2101.08492, +. + +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/drownings.Rd b/man/drownings.Rd index 28c969d5..2357edb0 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -1,33 +1,33 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{data} -\name{drownings} -\alias{drownings} -\title{Deaths by drowning in Finland in 1969-2019} -\format{ -A time series object containing 51 observations. -} -\source{ -Statistics Finland -\url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. -} -\description{ -Dataset containing number of deaths by drowning in Finland in 1969-2019, -corresponding population sizes (in hundreds of thousands), and -yearly average summer temperatures (June to August), based on simple -unweighted average of three weather stations: Helsinki (Southern Finland), -Jyväskylä (Central Finland), and Sodankylä (Northern Finland). -} -\examples{ -data("drownings") -model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], - xreg = drownings[, "summer_temp"], distribution = "poisson", - beta = normal(0, 0, 1), - sd_level = gamma(0.1,2, 10), sd_slope = gamma(0, 2, 10)) - -fit <- run_mcmc(model, iter = 5000, - output_type = "summary", mcmc_type = "approx") -fit -ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{drownings} +\alias{drownings} +\title{Deaths by drowning in Finland in 1969-2019} +\format{ +A time series object containing 51 observations. +} +\source{ +Statistics Finland +\url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. +} +\description{ +Dataset containing number of deaths by drowning in Finland in 1969-2019, +corresponding population sizes (in hundreds of thousands), and +yearly average summer temperatures (June to August), based on simple +unweighted average of three weather stations: Helsinki (Southern Finland), +Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). +} +\examples{ +data("drownings") +model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], + xreg = drownings[, "summer_temp"], distribution = "poisson", + beta = normal(0, 0, 1), + sd_level = gamma_prior(0.1,2, 10), sd_slope = gamma_prior(0, 2, 10)) + +fit <- run_mcmc(model, iter = 5000, + output_type = "summary", mcmc_type = "approx") +fit +ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) +} +\keyword{datasets} diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index c3b4c57b..f25d85c8 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -1,29 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smoother.R -\name{ekf_smoother} -\alias{ekf_smoother} -\alias{ekf_fast_smoother} -\title{Extended Kalman Smoothing} -\usage{ -ekf_smoother(model, iekf_iter = 0) - -ekf_fast_smoother(model, iekf_iter = 0) -} -\arguments{ -\item{model}{Model model} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is -used with \code{iekf_iter} iterations.} -} -\value{ -List containing the log-likelihood, -smoothed state estimates \code{alphahat}, and the corresponding variances -\code{Vt} and \code{Ptt}. -} -\description{ -Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother -for the given non-linear Gaussian model of class \code{ssm_nlg}, -and returns the smoothed estimates of the states and the corresponding -variances. Function \code{ekf_fast_smoother} computes only smoothed -estimates of the states. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smoother.R +\name{ekf_smoother} +\alias{ekf_smoother} +\alias{ekf_fast_smoother} +\title{Extended Kalman Smoothing} +\usage{ +ekf_smoother(model, iekf_iter = 0) + +ekf_fast_smoother(model, iekf_iter = 0) +} +\arguments{ +\item{model}{Model model} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations.} +} +\value{ +List containing the log-likelihood, +smoothed state estimates \code{alphahat}, and the corresponding variances +\code{Vt} and \code{Ptt}. +} +\description{ +Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother +for the given non-linear Gaussian model of class \code{ssm_nlg}, +and returns the smoothed estimates of the states and the corresponding +variances. Function \code{ekf_fast_smoother} computes only smoothed +estimates of the states. +} diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index f15a8397..f970d40c 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -1,62 +1,62 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ekpf_filter.R -\name{ekpf_filter} -\alias{ekpf_filter} -\alias{ekpf_filter.ssm_nlg} -\title{Extended Kalman Particle Filtering} -\usage{ -ekpf_filter(object, particles, ...) - -\method{ekpf_filter}{ssm_nlg}( - object, - particles, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{of class \code{ssm_nlg}.} - -\item{particles}{Number of particles.} - -\item{...}{Ignored.} - -\item{seed}{Seed for RNG.} -} -\value{ -A list containing samples, filtered estimates and the -corresponding covariances, weights, and an estimate of log-likelihood. -} -\description{ -Function \code{ekpf_filter} performs a extended Kalman particle filtering -with stratification resampling, based on Van Der Merwe et al (2001). -} -\examples{ -\dontrun{ -set.seed(1) -n <- 50 -x <- y <- numeric(n) -y[1] <- rnorm(1, exp(x[1]), 0.1) -for(i in 1:(n-1)) { - x[i+1] <- rnorm(1, sin(x[i]), 0.1) - y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) -} - -pntrs <- nlg_example_models("sin_exp") - -model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, - Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, - Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, - theta = c(log_H = log(0.1), log_R = log(0.1)), - log_prior_pdf = pntrs$log_prior_pdf, - n_states = 1, n_etas = 1, state_names = "state") - -out <- ekpf_filter(model_nlg, 100) -ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -} -} -\references{ -Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. -(2001). The unscented particle filter. In Advances in neural -information processing systems (pp. 584-590). -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ekpf_filter.R +\name{ekpf_filter} +\alias{ekpf_filter} +\alias{ekpf_filter.ssm_nlg} +\title{Extended Kalman Particle Filtering} +\usage{ +ekpf_filter(object, particles, ...) + +\method{ekpf_filter}{ssm_nlg}( + object, + particles, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{of class \code{ssm_nlg}.} + +\item{particles}{Number of particles.} + +\item{...}{Ignored.} + +\item{seed}{Seed for RNG.} +} +\value{ +A list containing samples, filtered estimates and the +corresponding covariances, weights, and an estimate of log-likelihood. +} +\description{ +Function \code{ekpf_filter} performs a extended Kalman particle filtering +with stratification resampling, based on Van Der Merwe et al (2001). +} +\examples{ +\dontrun{ +set.seed(1) +n <- 50 +x <- y <- numeric(n) +y[1] <- rnorm(1, exp(x[1]), 0.1) +for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +} + +pntrs <- nlg_example_models("sin_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out <- ekpf_filter(model_nlg, 100) +ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +} +} +\references{ +Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. +(2001). The unscented particle filter. In Advances in neural +information processing systems (pp. 584-590). +} diff --git a/man/exchange.Rd b/man/exchange.Rd index 5990e5ea..cb9b93da 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -1,28 +1,28 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bssm-package.R -\docType{data} -\name{exchange} -\alias{exchange} -\title{Pound/Dollar daily exchange rates} -\format{ -A vector of length 945. -} -\source{ -\url{http://www.ssfpack.com/DKbook.html}. -} -\description{ -Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] -} -\examples{ -data("exchange") -model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), - sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) - -out <- particle_smoother(model, particles = 500) -plot.ts(cbind(model$y, exp(out$alphahat))) -} -\references{ -James Durbin, Siem Jan Koopman (2012). -Time Series Analysis by State Space Methods. Oxford University Press. -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{exchange} +\alias{exchange} +\title{Pound/Dollar daily exchange rates} +\format{ +A vector of length 945. +} +\source{ +\url{http://www.ssfpack.com/DKbook.html}. +} +\description{ +Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] +} +\examples{ +data("exchange") +model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), + sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) + +out <- particle_smoother(model, particles = 500) +plot.ts(cbind(model$y, exp(out$alphahat))) +} +\references{ +James Durbin, Siem Jan Koopman (2012). +Time Series Analysis by State Space Methods. Oxford University Press. +} +\keyword{datasets} diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index 4338bbd3..f306a242 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -1,31 +1,31 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{expand_sample} -\alias{expand_sample} -\title{Expand the Jump Chain representation} -\usage{ -expand_sample(x, variable = "theta", times, states, by_states = TRUE) -} -\arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} - -\item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} - -\item{times}{Vector of indices. In case of states, -what time points to expand? Default is all.} - -\item{states}{Vector of indices. In case of states, -what states to expand? Default is all.} - -\item{by_states}{If \code{TRUE} (default), return list by states. -Otherwise by time.} -} -\description{ -The MCMC algorithms of \code{bssm} use a jump chain representation where we -store the accepted values and the number of times we stayed in the current -value. Although this saves bit memory and is especially convenient for -IS-corrected MCMC, sometimes we want to have the usual sample paths. -Function \code{expand_sample} returns the expanded sample based on the -counts. Note that for IS-corrected output the expanded -sample corresponds to the approximate posterior. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print_mcmc.R +\name{expand_sample} +\alias{expand_sample} +\title{Expand the Jump Chain representation} +\usage{ +expand_sample(x, variable = "theta", times, states, by_states = TRUE) +} +\arguments{ +\item{x}{Output from \code{\link{run_mcmc}}.} + +\item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} + +\item{times}{Vector of indices. In case of states, +what time points to expand? Default is all.} + +\item{states}{Vector of indices. In case of states, +what states to expand? Default is all.} + +\item{by_states}{If \code{TRUE} (default), return list by states. +Otherwise by time.} +} +\description{ +The MCMC algorithms of \code{bssm} use a jump chain representation where we +store the accepted values and the number of times we stayed in the current +value. Although this saves bit memory and is especially convenient for +IS-corrected MCMC, sometimes we want to have the usual sample paths. +Function \code{expand_sample} returns the expanded sample based on the +counts. Note that for IS-corrected output the expanded +sample corresponds to the approximate posterior. +} diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index b8a0b0a0..b79e627b 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -1,55 +1,55 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/approx.R -\name{gaussian_approx} -\alias{gaussian_approx} -\alias{gaussian_approx.nongaussian} -\alias{gaussian_approx.ssm_nlg} -\title{Gaussian Approximation of Non-Gaussian/Non-linear State Space Model} -\usage{ -gaussian_approx(model, max_iter, conv_tol, ...) - -\method{gaussian_approx}{nongaussian}(model, max_iter = 100, conv_tol = 1e-08, ...) - -\method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) -} -\arguments{ -\item{model}{Model to be approximated.} - -\item{max_iter}{Maximum number of iterations.} - -\item{conv_tol}{Tolerance parameter.} - -\item{...}{Ignored.} - -\item{iekf_iter}{For non-linear models, number of iterations in iterated EKF -(defaults to 0).} -} -\value{ -Returns linear-Gaussian SSM of class \code{ssm_ulg} or -\code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as - the original model. -} -\description{ -Returns the approximating Gaussian model which has the same conditional -mode of p(alpha|y, theta) as the original model. -This function is rarely needed itself, and is mainly available for -testing and debugging purposes. -} -\examples{ -data("poisson_series") -model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, - distribution = "poisson") -out <- gaussian_approx(model) -for(i in 1:7) - cat("Number of iterations used: ", i, ", y[1] = ", - gaussian_approx(model, max_iter = i, conv_tol = 0)$y[1], "\n", sep ="") - -} -\references{ -Koopman, S.J. and Durbin J. (2012). Time Series Analysis by State Space -Methods. Second edition. Oxford: Oxford University Press. - -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/approx.R +\name{gaussian_approx} +\alias{gaussian_approx} +\alias{gaussian_approx.nongaussian} +\alias{gaussian_approx.ssm_nlg} +\title{Gaussian Approximation of Non-Gaussian/Non-linear State Space Model} +\usage{ +gaussian_approx(model, max_iter, conv_tol, ...) + +\method{gaussian_approx}{nongaussian}(model, max_iter = 100, conv_tol = 1e-08, ...) + +\method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) +} +\arguments{ +\item{model}{Model to be approximated.} + +\item{max_iter}{Maximum number of iterations.} + +\item{conv_tol}{Tolerance parameter.} + +\item{...}{Ignored.} + +\item{iekf_iter}{For non-linear models, number of iterations in iterated EKF +(defaults to 0).} +} +\value{ +Returns linear-Gaussian SSM of class \code{ssm_ulg} or +\code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as + the original model. +} +\description{ +Returns the approximating Gaussian model which has the same conditional +mode of p(alpha|y, theta) as the original model. +This function is rarely needed itself, and is mainly available for +testing and debugging purposes. +} +\examples{ +data("poisson_series") +model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, + distribution = "poisson") +out <- gaussian_approx(model) +for(i in 1:7) + cat("Number of iterations used: ", i, ", y[1] = ", + gaussian_approx(model, max_iter = i, conv_tol = 0)$y[1], "\n", sep ="") + +} +\references{ +Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space +Methods. Second edition. Oxford: Oxford University Press. + +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index b9b45880..ec867821 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -1,61 +1,61 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/importance_sample.R -\name{importance_sample} -\alias{importance_sample} -\alias{importance_sample.nongaussian} -\title{Importance Sampling from non-Gaussian State Space Model} -\usage{ -importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) - -\method{importance_sample}{nongaussian}( - model, - nsim, - use_antithetic = TRUE, - max_iter = 100, - conv_tol = 1e-08, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, -\code{ssm_ung}, or \code{ssm_mng}.} - -\item{nsim}{Number of samples.} - -\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic -variable for location in simulation smoothing. Ignored for \code{ssm_mng} -models.} - -\item{max_iter}{Maximum number of iterations used for the approximation.} - -\item{conv_tol}{Convergence threshold for the approximation. Approximation -is claimed to be converged when the mean squared difference of the modes is -less than \code{conv_tol}.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Returns \code{nsim} samples from the approximating Gaussian model with -corresponding (scaled) importance weights. -Probably mostly useful for comparing KFAS and bssm packages. -} -\examples{ -data("sexratio", package = "KFAS") -model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, - u = sexratio[, "Total"], - distribution = "binomial") - -imp <- importance_sample(model, nsim = 1000) - -est <- matrix(NA, 3, nrow(sexratio)) -for(i in 1:ncol(est)) { - est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, - prob = c(0.05,0.5,0.95), normwt=TRUE) -} - -ts.plot(t(est),lty = c(2,1,2)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/importance_sample.R +\name{importance_sample} +\alias{importance_sample} +\alias{importance_sample.nongaussian} +\title{Importance Sampling from non-Gaussian State Space Model} +\usage{ +importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) + +\method{importance_sample}{nongaussian}( + model, + nsim, + use_antithetic = TRUE, + max_iter = 100, + conv_tol = 1e-08, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}.} + +\item{nsim}{Number of samples.} + +\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic +variable for location in simulation smoothing. Ignored for \code{ssm_mng} +models.} + +\item{max_iter}{Maximum number of iterations used for the approximation.} + +\item{conv_tol}{Convergence threshold for the approximation. Approximation +is claimed to be converged when the mean squared difference of the modes is +less than \code{conv_tol}.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Returns \code{nsim} samples from the approximating Gaussian model with +corresponding (scaled) importance weights. +Probably mostly useful for comparing KFAS and bssm packages. +} +\examples{ +data("sexratio", package = "KFAS") +model <- bsm_ng(sexratio[, "Male"], sd_level = 0.001, + u = sexratio[, "Total"], + distribution = "binomial") + +imp <- importance_sample(model, nsim = 1000) + +est <- matrix(NA, 3, nrow(sexratio)) +for(i in 1:ncol(est)) { + est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, + prob = c(0.05,0.5,0.95), normwt=TRUE) +} + +ts.plot(t(est),lty = c(2,1,2)) + +} diff --git a/man/kfilter.Rd b/man/kfilter.Rd index 7b8f7aa4..8049a822 100644 --- a/man/kfilter.Rd +++ b/man/kfilter.Rd @@ -1,43 +1,43 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/kfilter.R -\name{kfilter} -\alias{kfilter} -\alias{kfilter.gaussian} -\alias{kfilter.nongaussian} -\title{Kalman Filtering} -\usage{ -kfilter(model, ...) - -\method{kfilter}{gaussian}(model, ...) - -\method{kfilter}{nongaussian}(model, ...) -} -\arguments{ -\item{model}{Model Model object.} - -\item{...}{Ignored.} -} -\value{ -List containing the log-likelihood -(approximate in non-Gaussian case), one-step-ahead predictions \code{at} -and filtered estimates \code{att} of states, and the corresponding -variances \code{Pt} and \code{Ptt}. -} -\description{ -Function \code{kfilter} runs the Kalman filter for the given model, -and returns the filtered estimates and one-step-ahead predictions of the -states \eqn{\alpha_t} given the data up to time \eqn{t}. -} -\details{ -For non-Gaussian models, the filtering is based on the approximate -Gaussian model. -} -\examples{ -x <- cumsum(rnorm(20)) -y <- x + rnorm(20, sd = 0.1) -model <- bsm_lg(y, sd_level = 1, sd_y = 0.1) -ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) -} -\seealso{ -\code{\link{bootstrap_filter}} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/kfilter.R +\name{kfilter} +\alias{kfilter} +\alias{kfilter.gaussian} +\alias{kfilter.nongaussian} +\title{Kalman Filtering} +\usage{ +kfilter(model, ...) + +\method{kfilter}{gaussian}(model, ...) + +\method{kfilter}{nongaussian}(model, ...) +} +\arguments{ +\item{model}{Model Model object.} + +\item{...}{Ignored.} +} +\value{ +List containing the log-likelihood +(approximate in non-Gaussian case), one-step-ahead predictions \code{at} +and filtered estimates \code{att} of states, and the corresponding +variances \code{Pt} and \code{Ptt}. +} +\description{ +Function \code{kfilter} runs the Kalman filter for the given model, +and returns the filtered estimates and one-step-ahead predictions of the +states \eqn{\alpha_t} given the data up to time \eqn{t}. +} +\details{ +For non-Gaussian models, the filtering is based on the approximate +Gaussian model. +} +\examples{ +x <- cumsum(rnorm(20)) +y <- x + rnorm(20, sd = 0.1) +model <- bsm_lg(y, sd_level = 1, sd_y = 0.1) +ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) +} +\seealso{ +\code{\link{bootstrap_filter}} +} diff --git a/man/logLik.Rd b/man/logLik.Rd index 6ca9c392..feeeaf40 100644 --- a/man/logLik.Rd +++ b/man/logLik.Rd @@ -1,21 +1,21 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.gaussian} -\alias{logLik.gaussian} -\title{Log-likelihood of a Gaussian State Space Model} -\usage{ -\method{logLik}{gaussian}(object, ...) -} -\arguments{ -\item{object}{Model model.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a linear-Gaussian state space model of -\code{bssm} package. -} -\examples{ -model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) -logLik(model) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.gaussian} +\alias{logLik.gaussian} +\title{Log-likelihood of a Gaussian State Space Model} +\usage{ +\method{logLik}{gaussian}(object, ...) +} +\arguments{ +\item{object}{Model model.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a linear-Gaussian state space model of +\code{bssm} package. +} +\examples{ +model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) +logLik(model) +} diff --git a/man/logLik.ssm_nlg.Rd b/man/logLik.ssm_nlg.Rd index 2e9603cc..cdfdbfa9 100644 --- a/man/logLik.ssm_nlg.Rd +++ b/man/logLik.ssm_nlg.Rd @@ -1,47 +1,47 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_nlg} -\alias{logLik.ssm_nlg} -\title{Log-likelihood of a Non-linear State Space Model} -\usage{ -\method{logLik}{ssm_nlg}( - object, - particles, - method = "bsf", - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter. If 0, -approximate log-likelihood is returned either based on the Gaussian -approximation or EKF, depending on the \code{method} argument.} - -\item{method}{Sampling method. Default is the bootstrap particle filter -(\code{"bsf"}). Other choices are \code{"psi"} which uses -psi-auxiliary filter (or approximating Gaussian model in the case of -\code{particles = 0}), and \code{"ekf"} which uses EKF-based particle -filter (or just EKF approximation in the case of \code{particles = 0}).} - -\item{max_iter}{Maximum number of iterations for the gaussian approximation -algorithm.} - -\item{conv_tol}{Tolerance parameter for the approximation algorithm.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter -is used with -\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class -\code{ssm_nlg} package. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.ssm_nlg} +\alias{logLik.ssm_nlg} +\title{Log-likelihood of a Non-linear State Space Model} +\usage{ +\method{logLik}{ssm_nlg}( + object, + particles, + method = "bsf", + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{Model model.} + +\item{particles}{Number of samples for particle filter. If 0, +approximate log-likelihood is returned either based on the Gaussian +approximation or EKF, depending on the \code{method} argument.} + +\item{method}{Sampling method. Default is the bootstrap particle filter +(\code{"bsf"}). Other choices are \code{"psi"} which uses +psi-auxiliary filter (or approximating Gaussian model in the case of +\code{particles = 0}), and \code{"ekf"} which uses EKF-based particle +filter (or just EKF approximation in the case of \code{particles = 0}).} + +\item{max_iter}{Maximum number of iterations for the gaussian approximation +algorithm.} + +\item{conv_tol}{Tolerance parameter for the approximation algorithm.} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter +is used with +\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a state space model of class +\code{ssm_nlg} package. +} diff --git a/man/logLik.ssm_sde.Rd b/man/logLik.ssm_sde.Rd index 58ce148e..8129d19c 100644 --- a/man/logLik.ssm_sde.Rd +++ b/man/logLik.ssm_sde.Rd @@ -1,29 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_sde} -\alias{logLik.ssm_sde} -\title{Log-likelihood of a State Space Model with SDE dynamics} -\usage{ -\method{logLik}{ssm_sde}( - object, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter.} - -\item{L}{Integer defining the discretization level defined as (2^L).} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class -\code{ssm_sde} package. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.ssm_sde} +\alias{logLik.ssm_sde} +\title{Log-likelihood of a State Space Model with SDE dynamics} +\usage{ +\method{logLik}{ssm_sde}( + object, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{Model model.} + +\item{particles}{Number of samples for particle filter.} + +\item{L}{Integer defining the discretization level defined as (2^L).} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Computes the log-likelihood of a state space model of class +\code{ssm_sde} package. +} diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 4d079ada..6539b7e8 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -1,128 +1,128 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/particle_smoother.R -\name{particle_smoother} -\alias{particle_smoother} -\alias{particle_smoother.gaussian} -\alias{particle_smoother.nongaussian} -\alias{particle_smoother.ssm_nlg} -\alias{particle_smoother.ssm_sde} -\title{Particle Smoothing} -\usage{ -particle_smoother(model, particles, ...) - -\method{particle_smoother}{gaussian}( - model, - particles, - method = "psi", - seed = sample(.Machine$integer.max, size = 1), - ... -) - -\method{particle_smoother}{nongaussian}( - model, - particles, - method = "psi", - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - ... -) - -\method{particle_smoother}{ssm_nlg}( - model, - particles, - method = "bsf", - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - ... -) - -\method{particle_smoother}{ssm_sde}( - model, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model.} - -\item{particles}{Number of samples for particle filter.} - -\item{...}{Ignored.} - -\item{method}{Choice of particle filter algorithm. -For Gaussian and non-Gaussian models with linear dynamics, -options are \code{"bsf"} (bootstrap particle filter, default for -non-linear models) -and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and -for non-linear models options \code{"ekf"} (extended Kalman particle filter) -is also available.} - -\item{seed}{Seed for RNG.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation. -Used \eqn{\psi}-APF.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation. -Used \eqn{\psi}-APF.} - -\item{iekf_iter}{If zero (default), first approximation for non-linear -Gaussian models is obtained from extended Kalman filter. If -\code{iekf_iter > 0}, iterated extended Kalman filter is used with -\code{iekf_iter} iterations.} - -\item{L}{Integer defining the discretization level.} -} -\value{ -List with samples (\code{alpha}) from the smoothing distribution -and corresponding weights (\code{weights}), - as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) - of the states and - estimated log-likelihood (\code{logLik}). -} -\description{ -Function \code{particle_smoother} performs particle smoothing -based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary -particle filter (\eqn{\psi}-APF) [2], -or extended Kalman particle filter [3] (or its iterated version [4]). -The smoothing phase is based on the filter-smoother algorithm by [5]. -} -\details{ -See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. -} -\examples{ -set.seed(1) -x <- cumsum(rnorm(100)) -y <- rnorm(100, x) -model <- ssm_ulg(y, Z = 1, T = 1, R = 1, H = 1, P1 = 1) -system.time(out <- particle_smoother(model, particles = 1000)) -# same with simulation smoother: -system.time(out2 <- sim_smoother(model, particles = 1000, - use_antithetic = TRUE)) -ts.plot(out$alphahat, rowMeans(out2), col = 1:2) - -} -\references{ -[1] Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. -IEE Proceedings-F, 140, 107–113. - -[2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators -based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 - -[3] Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. (2001). -The unscented particle filter. -In Advances in neural information processing systems (pp. 584-590). - -[4] Jazwinski, A. 1970. Stochastic Processes and Filtering Theory. -Academic Press. - -[5] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian -nonlinear state space models. -Journal of Computational and Graphical Statistics, 5, 1–25. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/particle_smoother.R +\name{particle_smoother} +\alias{particle_smoother} +\alias{particle_smoother.gaussian} +\alias{particle_smoother.nongaussian} +\alias{particle_smoother.ssm_nlg} +\alias{particle_smoother.ssm_sde} +\title{Particle Smoothing} +\usage{ +particle_smoother(model, particles, ...) + +\method{particle_smoother}{gaussian}( + model, + particles, + method = "psi", + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{particle_smoother}{nongaussian}( + model, + particles, + method = "psi", + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + ... +) + +\method{particle_smoother}{ssm_nlg}( + model, + particles, + method = "bsf", + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + ... +) + +\method{particle_smoother}{ssm_sde}( + model, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model.} + +\item{particles}{Number of samples for particle filter.} + +\item{...}{Ignored.} + +\item{method}{Choice of particle filter algorithm. +For Gaussian and non-Gaussian models with linear dynamics, +options are \code{"bsf"} (bootstrap particle filter, default for +non-linear models) +and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and +for non-linear models options \code{"ekf"} (extended Kalman particle filter) +is also available.} + +\item{seed}{Seed for RNG.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation. +Used \eqn{\psi}-APF.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation. +Used \eqn{\psi}-APF.} + +\item{iekf_iter}{If zero (default), first approximation for non-linear +Gaussian models is obtained from extended Kalman filter. If +\code{iekf_iter > 0}, iterated extended Kalman filter is used with +\code{iekf_iter} iterations.} + +\item{L}{Integer defining the discretization level.} +} +\value{ +List with samples (\code{alpha}) from the smoothing distribution +and corresponding weights (\code{weights}), + as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) + of the states and + estimated log-likelihood (\code{logLik}). +} +\description{ +Function \code{particle_smoother} performs particle smoothing +based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary +particle filter (\eqn{\psi}-APF) [2], +or extended Kalman particle filter [3] (or its iterated version [4]). +The smoothing phase is based on the filter-smoother algorithm by [5]. +} +\details{ +See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. +} +\examples{ +set.seed(1) +x <- cumsum(rnorm(100)) +y <- rnorm(100, x) +model <- ssm_ulg(y, Z = 1, T = 1, R = 1, H = 1, P1 = 1) +system.time(out <- particle_smoother(model, particles = 1000)) +# same with simulation smoother: +system.time(out2 <- sim_smoother(model, particles = 1000, + use_antithetic = TRUE)) +ts.plot(out$alphahat, rowMeans(out2), col = 1:2) + +} +\references{ +[1] Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +IEE Proceedings-F, 140, 107-113. + +[2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 + +[3] Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +The unscented particle filter. +In Advances in neural information processing systems, p 584-590. + +[4] Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +Academic Press. + +[5] Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +nonlinear state space models. +Journal of Computational and Graphical Statistics, 5, 1-25. +} diff --git a/man/post_correct.Rd b/man/post_correct.Rd index 6260f3e3..49c42475 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -68,7 +68,7 @@ y <- rbinom(n, size = u, plogis(0.5 * x1 + x2 + alpha)) ts.plot(y / u) model <- ar1_ng(y, distribution = "binomial", - rho = uniform(0.5, -1, 1), sigma = gamma(1, 2, 0.001), + rho = uniform(0.5, -1, 1), sigma = gamma_prior(1, 2, 0.001), mu = normal(0, 0, 10), xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), u = u) @@ -120,12 +120,12 @@ ggplot(aes(time, mean, colour = method)) + } } \references{ -A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). Efficient implementation of Markov chain Monte Carlo when using an unbiased -likelihood estimator. Biometrika, 102, 2, 2015, Pages 295–313, +likelihood estimator. Biometrika, 102, 2, 295-313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/priors.Rd b/man/priors.Rd deleted file mode 100644 index 3a5d66c3..00000000 --- a/man/priors.Rd +++ /dev/null @@ -1,56 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/priors.R -\name{uniform} -\alias{uniform} -\alias{halfnormal} -\alias{normal} -\alias{tnormal} -\alias{gamma} -\title{Prior objects for bssm models} -\usage{ -uniform(init, min, max) - -halfnormal(init, sd) - -normal(init, mean, sd) - -tnormal(init, mean, sd, min = -Inf, max = Inf) - -gamma(init, shape, rate) -} -\arguments{ -\item{init}{Initial value for the parameter, used in initializing the model -components and as a starting values in MCMC.} - -\item{min}{Lower bound of the uniform and truncated normal prior.} - -\item{max}{Upper bound of the uniform and truncated normal prior.} - -\item{sd}{Standard deviation of the (underlying i.e. non-truncated) -Normal distribution.} - -\item{mean}{Mean of the Normal prior.} - -\item{shape}{Shape parameter of the Gamma prior.} - -\item{rate}{Rate parameter of the Gamma prior.} -} -\value{ -object of class \code{bssm_prior}. -} -\description{ -These simple objects of class \code{bssm_prior} are used to construct a -prior distributions for the -MCMC runs of \code{bssm} package. Currently supported priors are uniform -(\code{uniform()}), half-normal (\code{halfnormal()}), -normal (\code{normal()}), gamma (\code{gamma}), and -truncated normal distribution (\code{tnormal()}). All parameters are -vectorized so for regression coefficient vector beta you can define prior -for example as \code{normal(0, 0, c(10, 20))}. -} -\examples{ -# create uniform prior on [-1, 1] for one parameter with initial value 0.2: -uniform(0.2, -1, 1) -# two normal priors at once i.e. for coefficients beta: -normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) -} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index e692b9e7..38dd1cca 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -1,28 +1,27 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc} -\alias{run_mcmc} -\title{Bayesian Inference of State Space Models} -\usage{ -run_mcmc(model, iter, ...) -} -\arguments{ -\item{model}{State space model model of \code{bssm} package.} - -\item{iter}{Number of MCMC iterations.} - -\item{...}{Parameters to specific methods. See -\code{\link{run_mcmc.gaussian}}, -\code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, -and \code{\link{run_mcmc.ssm_sde}} for details.} -} -\description{ -Adaptive Markov chain Monte Carlo simulation of state space models using -Robust Adaptive Metropolis algorithm by Vihola (2012). -See specific methods for various model types for details. -} -\references{ -Matti Vihola (2012). "Robust adaptive Metropolis algorithm with -coerced acceptance rate". Statistics and Computing, Volume 22, Issue 5, -pages 997--1008. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc} +\alias{run_mcmc} +\title{Bayesian Inference of State Space Models} +\usage{ +run_mcmc(model, iter, ...) +} +\arguments{ +\item{model}{State space model model of \code{bssm} package.} + +\item{iter}{Number of MCMC iterations.} + +\item{...}{Parameters to specific methods. See +\code{\link{run_mcmc.gaussian}}, +\code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, +and \code{\link{run_mcmc.ssm_sde}} for details.} +} +\description{ +Adaptive Markov chain Monte Carlo simulation of state space models using +Robust Adaptive Metropolis algorithm by Vihola (2012). +See specific methods for various model types for details. +} +\references{ +Matti Vihola (2012). Robust adaptive Metropolis algorithm with +coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. +} diff --git a/man/run_mcmc.ssm_nlg.Rd b/man/run_mcmc.ssm_nlg.Rd index 1f9b259e..14df035e 100644 --- a/man/run_mcmc.ssm_nlg.Rd +++ b/man/run_mcmc.ssm_nlg.Rd @@ -1,110 +1,110 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_nlg} -\alias{run_mcmc.ssm_nlg} -\title{Bayesian Inference of non-linear state space models} -\usage{ -\method{run_mcmc}{ssm_nlg}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "bsf", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -\code{"approx"} for approximate inference based on the Gaussian -approximation of the model, -\code{"ekf"} for approximate inference using extended Kalman filter, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of -particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"bsf"} (default), bootstrap filter is used -for state sampling. -If \code{"ekf"}, particle filter based on EKF-proposals are used. -If \code{"psi"}, \eqn{\psi}-APF is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the -computations of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of -bsm_ng models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin -period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is -used with \code{iekf_iter} iterations in place of standard EKF. -Defaults to zero.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.ssm_nlg} +\alias{run_mcmc.ssm_nlg} +\title{Bayesian Inference of non-linear state space models} +\usage{ +\method{run_mcmc}{ssm_nlg}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "bsf", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration. +Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of latent states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, +\code{"approx"} for approximate inference based on the Gaussian +approximation of the model, +\code{"ekf"} for approximate inference using extended Kalman filter, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of +particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{sampling_method}{If \code{"bsf"} (default), bootstrap filter is used +for state sampling. +If \code{"ekf"}, particle filter based on EKF-proposals are used. +If \code{"psi"}, \eqn{\psi}-APF is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger +value can also be statistically more effective. +Note: With \code{output_type = "summary"}, the thinning does not affect the +computations of the summary statistics in case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of +bsm_ng models) the sampling +is done for transformed parameters with internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin +period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation.} + +\item{seed}{Seed for the random number generator.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} + +\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations in place of standard EKF. +Defaults to zero.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc.ssm_sde.Rd b/man/run_mcmc.ssm_sde.Rd index 8213db6c..3d558280 100644 --- a/man/run_mcmc.ssm_sde.Rd +++ b/man/run_mcmc.ssm_sde.Rd @@ -1,95 +1,95 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_sde} -\alias{run_mcmc.ssm_sde} -\title{Bayesian Inference of SDE} -\usage{ -\method{run_mcmc}{ssm_sde}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - L_c, - L_f, - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}. -If \code{particles = 0}, this is argument ignored and set to \code{"theta"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of - particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{L_c, L_f}{Integer values defining the discretization levels for first -and second stages (defined as 2^L). For PM methods, maximum of these is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the -computations of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., -the total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of -bsm_ng models) the sampling is done for transformed parameters with -internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin -period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.ssm_sde} +\alias{run_mcmc.ssm_sde} +\title{Bayesian Inference of SDE} +\usage{ +\method{run_mcmc}{ssm_sde}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + L_c, + L_f, + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of latent states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}. +If \code{particles = 0}, this is argument ignored and set to \code{"theta"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of + particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{L_c, L_f}{Integer values defining the discretization levels for first +and second stages (defined as 2^L). For PM methods, maximum of these is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger +value can also be statistically more effective. +Note: With \code{output_type = "summary"}, the thinning does not affect the +computations of the summary statistics in case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., +the total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation and dispersion parameters of +bsm_ng models) the sampling is done for transformed parameters with +internal_theta = log(theta).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin +period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc_g.Rd b/man/run_mcmc_g.Rd index 91c08014..b83458ae 100644 --- a/man/run_mcmc_g.Rd +++ b/man/run_mcmc_g.Rd @@ -1,95 +1,95 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.gaussian} -\alias{run_mcmc.gaussian} -\title{Bayesian Inference of Linear-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{gaussian}( - model, - iter, - output_type = "full", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{output_type}{Type of output. Default is \code{"full"}, which returns -samples from the posterior \eqn{p(\alpha, \theta)}. -Option \code{"summary"} does not simulate -states directly but computes the posterior means and variances of states -using fast Kalman smoothing. This is slightly faster, -more memory efficient and more accurate than calculations based on -simulation smoother. Using option \code{"theta"} will only -return samples from the marginal posterior of the hyperparameters -\eqn{\theta}.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of -\code{bssm} used adaptive MCMC during the burn-in period in order to find -good proposal.} - -\item{thin}{Thinning rate. All MCMC algorithms in \code{bssm} use the -jump chain representation, and the thinning is applied to these blocks. -Defaults to 1.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation, dispersion, and autoregressive parameters -of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter -space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the -burnin period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation. The default is 1.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Bayesian Inference of Linear-Gaussian State Space Models -} -\examples{ -model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(500, 500, 500), - sd_y = halfnormal(1, 10)) - -mcmc_results <- run_mcmc(model, iter = 2e4) -summary(mcmc_results, return_se = TRUE) - -library("dplyr") -sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% - group_by(time) \%>\% - summarise(mean = mean(value), - lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) -library("ggplot2") -sumr \%>\% ggplot(aes(time, mean)) + - geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + - geom_line() + theme_bw() + - geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), - col = 2) -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.gaussian} +\alias{run_mcmc.gaussian} +\title{Bayesian Inference of Linear-Gaussian State Space Models} +\usage{ +\method{run_mcmc}{gaussian}( + model, + iter, + output_type = "full", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{output_type}{Type of output. Default is \code{"full"}, which returns +samples from the posterior \eqn{p(\alpha, \theta)}. +Option \code{"summary"} does not simulate +states directly but computes the posterior means and variances of states +using fast Kalman smoothing. This is slightly faster, +more memory efficient and more accurate than calculations based on +simulation smoother. Using option \code{"theta"} will only +return samples from the marginal posterior of the hyperparameters +\eqn{\theta}.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of +\code{bssm} used adaptive MCMC during the burn-in period in order to find +good proposal.} + +\item{thin}{Thinning rate. All MCMC algorithms in \code{bssm} use the +jump chain representation, and the thinning is applied to these blocks. +Defaults to 1.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation, dispersion, and autoregressive parameters +of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter +space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the +burnin period. Default is \code{FALSE}.} + +\item{threads}{Number of threads for state simulation. The default is 1.} + +\item{seed}{Seed for the random number generator.} + +\item{...}{Ignored.} +} +\description{ +Bayesian Inference of Linear-Gaussian State Space Models +} +\examples{ +model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(500, 500, 500), + sd_y = halfnormal(1, 10)) + +mcmc_results <- run_mcmc(model, iter = 2e4) +summary(mcmc_results, return_se = TRUE) + +library("dplyr") +sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% + group_by(time) \%>\% + summarise(mean = mean(value), + lwr = quantile(value, 0.025), + upr = quantile(value, 0.975)) +library("ggplot2") +sumr \%>\% ggplot(aes(time, mean)) + + geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + + geom_line() + theme_bw() + + geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), + col = 2) +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd index 39b4cbdf..32975c72 100644 --- a/man/run_mcmc_ng.Rd +++ b/man/run_mcmc_ng.Rd @@ -1,219 +1,224 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.nongaussian} -\alias{run_mcmc.nongaussian} -\title{Bayesian Inference of Non-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{nongaussian}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "psi", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - local_approx = TRUE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of PMCMC , -\code{"approx"} for approximate inference based on the Gaussian -approximation of the model, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of -particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"psi"}, \eqn{\psi}-APF is used for state -sampling (default). If \code{"spdk"}, non-sequential importance sampling - based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger value can also be -statistically more effective. Note: With \code{output_type = "summary"}, -the thinning does not affect the computations of the summary statistics in -case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation, dispersion, and autoregressive parameters -of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter -space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the -burnin period. Default is \code{FALSE}.} - -\item{local_approx}{If \code{TRUE} (default), Gaussian approximation -needed for importance sampling is performed at each iteration. -If \code{FALSE}, approximation is updated only once at the start of the -MCMC using the initial model.} - -\item{threads}{Number of threads for state simulation. The default is 1. -Note that parallel computing is only used in the post-correction phase of - IS-MCMC and when sampling the states in case of approximate models.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\examples{ -set.seed(1) -n <- 50 -slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) -level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) -y <- rpois(n, exp(level)) -poisson_model <- bsm_ng(y, - sd_level = halfnormal(0.01, 1), - sd_slope = halfnormal(0.01, 0.1), - P1 = diag(c(10, 0.1)), distribution = "poisson") - -# Note small number of iterations for CRAN checks -mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, - mcmc_type = "da") -summary(mcmc_out, what = "theta", return_se = TRUE) - -set.seed(123) -n <- 50 -sd_level <- 0.1 -drift <- 0.01 -beta <- -0.9 -phi <- 5 - -level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) -x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) -y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) - -model <- bsm_ng(y, xreg = x, - beta = normal(0, 0, 10), - phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), - sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), - distribution = "negative binomial") - -# run IS-MCMC -# Note small number of iterations for CRAN checks -fit <- run_mcmc(model, iter = 5000, - particles = 10, mcmc_type = "is2", seed = 1) - -# extract states -d_states <- as.data.frame(fit, variable = "states", time = 1:n) - -library("dplyr") -library("ggplot2") - - # compute summary statistics -level_sumr <- d_states \%>\% - filter(variable == "level") \%>\% - group_by(time) \%>\% - summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), - lwr = Hmisc::wtd.quantile(value, weight, - 0.025, normwt = TRUE), - upr = Hmisc::wtd.quantile(value, weight, - 0.975, normwt = TRUE)) - -# visualize -level_sumr \%>\% ggplot(aes(x = time, y = mean)) + - geom_line() + - geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + - geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + - theme_bw() + - theme(legend.title = element_blank()) + - xlab("Time") + ylab("Level") - -# theta -d_theta <- as.data.frame(fit, variable = "theta") -ggplot(d_theta, aes(x = value)) + - geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + - facet_wrap(~ variable, scales = "free") + - theme_bw() - - -# Bivariate Poisson model: - -set.seed(1) -x <- cumsum(c(3, rnorm(19, sd = 0.5))) -y <- cbind( - rpois(20, exp(x)), - rpois(20, exp(x))) - -prior_fn <- function(theta) { - # half-normal prior using transformation - dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term -} - -update_fn <- function(theta) { - list(R = array(exp(theta), c(1, 1, 1))) -} - -model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, - R = 0.1, P1 = 1, distribution = "poisson", - init_theta = log(0.1), - prior_fn = prior_fn, update_fn = update_fn) - -# Note small number of iterations for CRAN checks -out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") - -sumr <- as.data.frame(out, variable = "states") \%>\% - group_by(time) \%>\% mutate(value = exp(value)) \%>\% - summarise(mean = mean(value), - ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) -ggplot(sumr, aes(time, mean)) + -geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + -geom_line() + -geom_line(data = data.frame(mean = y[, 1], time = 1:20), - colour = "tomato") + -geom_line(data = data.frame(mean = y[, 2], time = 1:20), - colour = "tomato") + -theme_bw() - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/run_mcmc.R +\name{run_mcmc.nongaussian} +\alias{run_mcmc.nongaussian} +\title{Bayesian Inference of Non-Gaussian State Space Models} +\usage{ +\method{run_mcmc}{nongaussian}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "psi", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + local_approx = TRUE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + ... +) +} +\arguments{ +\item{model}{Model model.} + +\item{iter}{Number of MCMC iterations.} + +\item{particles}{Number of state samples per MCMC iteration. +Ignored if \code{mcmc_type} is \code{"approx"}.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples of latent states alpha and +hyperparameters theta), \code{"theta"} (for marginal posterior of theta), +or \code{"summary"} (return the mean and variance estimates of the states +and posterior samples of theta). In case of \code{"summary"}, means and +covariances are computed using the full output of particle filter +instead of sampling one of these as in case of \code{output_type = "full"}.} + +\item{mcmc_type}{What MCMC algorithm to use? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of PMCMC , +\code{"approx"} for approximate inference based on the Gaussian +approximation of the model, +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of +particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{sampling_method}{If \code{"psi"}, \eqn{\psi}-APF is used for state +sampling (default). If \code{"spdk"}, non-sequential importance sampling + based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter +is used.} + +\item{burnin}{Length of the burn-in period which is disregarded from the +results. Defaults to \code{iter / 2}.} + +\item{thin}{Thinning rate. Defaults to 1. Increase for large models in +order to save memory. For IS-corrected methods, larger value can also be +statistically more effective. Note: With \code{output_type = "summary"}, +the thinning does not affect the computations of the summary statistics in +case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1 (not checked).} + +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. +For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the +total acceptance rate will be smaller.} + +\item{S}{Initial value for the lower triangular matrix of RAM +algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation, dispersion, and autoregressive parameters +of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter +space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} + +\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the +burnin period. Default is \code{FALSE}.} + +\item{local_approx}{If \code{TRUE} (default), Gaussian approximation +needed for importance sampling is performed at each iteration. +If \code{FALSE}, approximation is updated only once at the start of the +MCMC using the initial model.} + +\item{threads}{Number of threads for state simulation. The default is 1. +Note that parallel computing is only used in the post-correction phase of + IS-MCMC and when sampling the states in case of approximate models.} + +\item{seed}{Seed for the random number generator.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} + +\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} + +\item{...}{Ignored.} +} +\description{ +Methods for posterior inference of states and parameters. +} +\examples{ +set.seed(1) +n <- 50 +slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) +level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) +y <- rpois(n, exp(level)) +poisson_model <- bsm_ng(y, + sd_level = halfnormal(0.01, 1), + sd_slope = halfnormal(0.01, 0.1), + P1 = diag(c(10, 0.1)), distribution = "poisson") + +# Note small number of iterations for CRAN checks +mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, + mcmc_type = "da") +summary(mcmc_out, what = "theta", return_se = TRUE) + +set.seed(123) +n <- 50 +sd_level <- 0.1 +drift <- 0.01 +beta <- -0.9 +phi <- 5 + +level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) +x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) +y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) + +model <- bsm_ng(y, xreg = x, + beta = normal(0, 0, 10), + phi = halfnormal(1, 10), + sd_level = halfnormal(0.1, 1), + sd_slope = halfnormal(0.01, 0.1), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + distribution = "negative binomial") + +# run IS-MCMC +# Note small number of iterations for CRAN checks +fit <- run_mcmc(model, iter = 5000, + particles = 10, mcmc_type = "is2", seed = 1) + +# extract states +d_states <- as.data.frame(fit, variable = "states", time = 1:n) + +library("dplyr") +library("ggplot2") + + # compute summary statistics +level_sumr <- d_states \%>\% + filter(variable == "level") \%>\% + group_by(time) \%>\% + summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), + lwr = Hmisc::wtd.quantile(value, weight, + 0.025, normwt = TRUE), + upr = Hmisc::wtd.quantile(value, weight, + 0.975, normwt = TRUE)) + +# visualize +level_sumr \%>\% ggplot(aes(x = time, y = mean)) + + geom_line() + + geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + + geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + + theme_bw() + + theme(legend.title = element_blank()) + + xlab("Time") + ylab("Level") + +# theta +d_theta <- as.data.frame(fit, variable = "theta") +ggplot(d_theta, aes(x = value)) + + geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + + facet_wrap(~ variable, scales = "free") + + theme_bw() + + +# Bivariate Poisson model: + +set.seed(1) +x <- cumsum(c(3, rnorm(19, sd = 0.5))) +y <- cbind( + rpois(20, exp(x)), + rpois(20, exp(x))) + +prior_fn <- function(theta) { + # half-normal prior using transformation + dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term +} + +update_fn <- function(theta) { + list(R = array(exp(theta), c(1, 1, 1))) +} + +model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, + R = 0.1, P1 = 1, distribution = "poisson", + init_theta = log(0.1), + prior_fn = prior_fn, update_fn = update_fn) + +# Note small number of iterations for CRAN checks +out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") + +sumr <- as.data.frame(out, variable = "states") \%>\% + group_by(time) \%>\% mutate(value = exp(value)) \%>\% + summarise(mean = mean(value), + ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) +ggplot(sumr, aes(time, mean)) + +geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + +geom_line() + +geom_line(data = data.frame(mean = y[, 1], time = 1:20), + colour = "tomato") + +geom_line(data = data.frame(mean = y[, 2], time = 1:20), + colour = "tomato") + +theme_bw() + +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 87fbb97d..5025763b 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -1,59 +1,59 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sim_smoother.R -\name{sim_smoother} -\alias{sim_smoother} -\alias{sim_smoother.gaussian} -\alias{sim_smoother.nongaussian} -\title{Simulation Smoothing} -\usage{ -sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) - -\method{sim_smoother}{gaussian}( - model, - nsim = 1, - seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, - ... -) - -\method{sim_smoother}{nongaussian}( - model, - nsim = 1, - seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, - ... -) -} -\arguments{ -\item{model}{Model object.} - -\item{nsim}{Number of independent samples.} - -\item{seed}{Seed for the random number generator.} - -\item{use_antithetic}{Use an antithetic variable for location. -Default is \code{FALSE}. Currently not used for multivariate models.} - -\item{...}{Ignored.} -} -\value{ -An array containing the generated samples. -} -\description{ -Function \code{sim_smoother} performs simulation smoothing i.e. simulates -the states from the conditional distribution \eqn{p(\alpha | y, \theta)} -for linear-Gaussian models. -} -\details{ -For non-Gaussian/non-linear models, the simulation is based on the -approximating Gaussian model. -} -\examples{ -# only missing data, simulates from prior -model <- bsm_lg(rep(NA, 25), sd_level = 1, - sd_y = 1) -# use antithetic variable for location -sim <- sim_smoother(model, nsim = 4, use_antithetic = TRUE, seed = 1) -ts.plot(sim[, 1, ]) -cor(sim[, 1, ]) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/sim_smoother.R +\name{sim_smoother} +\alias{sim_smoother} +\alias{sim_smoother.gaussian} +\alias{sim_smoother.nongaussian} +\title{Simulation Smoothing} +\usage{ +sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) + +\method{sim_smoother}{gaussian}( + model, + nsim = 1, + seed = sample(.Machine$integer.max, size = 1), + use_antithetic = FALSE, + ... +) + +\method{sim_smoother}{nongaussian}( + model, + nsim = 1, + seed = sample(.Machine$integer.max, size = 1), + use_antithetic = FALSE, + ... +) +} +\arguments{ +\item{model}{Model object.} + +\item{nsim}{Positive integer defining the number of independent samples.} + +\item{seed}{Seed for the random number generator.} + +\item{use_antithetic}{Use an antithetic variable for location? +Default is \code{FALSE}. Currently not used for multivariate models.} + +\item{...}{Ignored.} +} +\value{ +An array containing the generated samples. +} +\description{ +Function \code{sim_smoother} performs simulation smoothing i.e. simulates +the states from the conditional distribution \eqn{p(\alpha | y, \theta)} +for linear-Gaussian models. +} +\details{ +For non-Gaussian/non-linear models, the simulation is based on the +approximating Gaussian model. +} +\examples{ +# only missing data, simulates from prior +model <- bsm_lg(rep(NA, 25), sd_level = 1, + sd_y = 1) +# use antithetic variable for location +sim <- sim_smoother(model, nsim = 4, use_antithetic = TRUE, seed = 1) +ts.plot(sim[, 1, ]) +cor(sim[, 1, ]) +} diff --git a/man/smoother.Rd b/man/smoother.Rd index b0a4c554..f624ecce 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -1,29 +1,29 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smoother.R -\name{fast_smoother} -\alias{fast_smoother} -\alias{smoother} -\title{Kalman Smoothing} -\usage{ -fast_smoother(model, ...) - -smoother(model, ...) -} -\arguments{ -\item{model}{Model model.} - -\item{...}{Ignored.} -} -\value{ -Matrix containing the smoothed estimates of states, or a list -with the smoothed states and the variances. -} -\description{ -Methods for Kalman smoothing of the states. Function \code{fast_smoother} -computes only smoothed estimates of the states, and function -\code{smoother} computes also smoothed variances. -} -\details{ -For non-Gaussian models, the smoothing is based on the approximate Gaussian -model. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smoother.R +\name{fast_smoother} +\alias{fast_smoother} +\alias{smoother} +\title{Kalman Smoothing} +\usage{ +fast_smoother(model, ...) + +smoother(model, ...) +} +\arguments{ +\item{model}{Model model.} + +\item{...}{Ignored.} +} +\value{ +Matrix containing the smoothed estimates of states, or a list +with the smoothed states and the variances. +} +\description{ +Methods for Kalman smoothing of the states. Function \code{fast_smoother} +computes only smoothed estimates of the states, and function +\code{smoother} computes also smoothed variances. +} +\details{ +For non-Gaussian models, the smoothing is based on the approximate Gaussian +model. +} diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index eb0d4d76..3d82382f 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -1,102 +1,102 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_mlg} -\alias{ssm_mlg} -\title{General multivariate linear Gaussian state space models} -\usage{ -ssm_mlg( - y, - Z, - H, - T, - R, - a1 = NULL, - P1 = NULL, - init_theta = numeric(0), - D = NULL, - C = NULL, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as multivariate time series or matrix with -dimensions n x p.} - -\item{Z}{System matrix Z of the observation equation as p x m matrix or -p x m x n array.} - -\item{H}{Lower triangular matrix H of the observation. Either a scalar or -a vector of length n.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix -or a m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as a p x n matrix.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, -and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be -onstant wrt. theta.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_mlg}. -} -\description{ -Construct an object of class \code{ssm_mlg} by directly defining the -corresponding terms of the model. -} -\details{ -The general multivariate linear-Gaussian model is defined using the -following observational and state equations: - -\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, -(\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, -(\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here p is the number of time series and k is the number of disturbance terms -(which can be less than m, the number of states). - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, -and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be -constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_mlg}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -} -\examples{ - -data("GlobalTemp", package = "KFAS") -model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), - R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) -ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_mlg} +\alias{ssm_mlg} +\title{General multivariate linear Gaussian state space models} +\usage{ +ssm_mlg( + y, + Z, + H, + T, + R, + a1 = NULL, + P1 = NULL, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as multivariate time series or matrix with +dimensions n x p.} + +\item{Z}{System matrix Z of the observation equation as p x m matrix or +p x m x n array.} + +\item{H}{Lower triangular matrix H of the observation. Either a scalar or +a vector of length n.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix +or a m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as a p x n matrix.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be +onstant wrt. theta.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_mlg}. +} +\description{ +Construct an object of class \code{ssm_mlg} by directly defining the +corresponding terms of the model. +} +\details{ +The general multivariate linear-Gaussian model is defined using the +following observational and state equations: + +\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. +Here p is the number of time series and k is the number of disturbance terms +(which can be less than m, the number of states). + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be +constant wrt. theta. +Note that while you can input say R as m x k matrix for \code{ssm_mlg}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +} +\examples{ + +data("GlobalTemp", package = "KFAS") +model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), + R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) +ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) + +} diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index 7cec544e..064f09c3 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -1,134 +1,135 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_mng} -\alias{ssm_mng} -\title{General Non-Gaussian State Space Model} -\usage{ -ssm_mng( - y, - Z, - T, - R, - a1 = NULL, - P1 = NULL, - distribution, - phi = 1, - u = 1, - init_theta = numeric(0), - D = NULL, - C = NULL, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as multivariate time series or matrix with dimensions -n x p.} - -\item{Z}{System matrix Z of the observation equation as p x m matrix or -p x m x n array.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a m x k -matrix or a -m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{distribution}{vector of distributions of the observed series. -Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, -\code{"gamma"}, and \code{"gaussian"}.} - -\item{phi}{Additional parameters relating to the non-Gaussian distributions. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, for Gaussian this is -standard deviation, -and for other distributions this is ignored.} - -\item{u}{Constant parameter for non-Gaussian models. For Poisson, gamma, -and negative binomial distribution, this corresponds to the offset term. -For binomial, this is the number of trials.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as p x n matrix.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and -\code{phi}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. -theta.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_mng}. -} -\description{ -Construct an object of class \code{ssm_mng} by directly defining the -corresponding terms of the model. -} -\details{ -The general multivariate non-Gaussian model is defined using the following -observational and state equations: - -\deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, -(\textrm{transition equation})} - -where \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and -\eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or -negative binomial distribution for each observation series \eqn{i=1,...,p}. -Here k is the number of disturbance terms (which can be less than m, -the number of states). -} -\examples{ - -set.seed(1) -n <- 20 -x <- cumsum(rnorm(n, sd = 0.5)) -phi <- 2 -y <- cbind( - rgamma(n, shape = phi, scale = exp(x) / phi), - rbinom(n, 10, plogis(x))) - -Z <- matrix(1, 2, 1) -T <- 1 -R <- 0.5 -a1 <- 0 -P1 <- 1 - -update_fn <- function(theta) { - list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) -} - -prior_fn <- function(theta) { - ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) -} - -model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), - init_theta = c(0.5, 2), - distribution = c("gamma", "binomial"), - u = cbind(1, rep(10, n)), - update_fn = update_fn, prior_fn = prior_fn) - -# smoothing based on approximating gaussian model -ts.plot(cbind(y, fast_smoother(model)), - col = 1:3, lty = c(1, 1, 2)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_mng} +\alias{ssm_mng} +\title{General Non-Gaussian State Space Model} +\usage{ +ssm_mng( + y, + Z, + T, + R, + a1 = NULL, + P1 = NULL, + distribution, + phi = 1, + u, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as multivariate time series or matrix with dimensions +n x p.} + +\item{Z}{System matrix Z of the observation equation as p x m matrix or +p x m x n array.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a m x k +matrix or a +m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{distribution}{vector of distributions of the observed series. +Possible choices are +\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, +\code{"gamma"}, and \code{"gaussian"}.} + +\item{phi}{Additional parameters relating to the non-Gaussian distributions. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, for Gaussian this is +standard deviation, +and for other distributions this is ignored.} + +\item{u}{Matrix of positive constants for non-Gaussian models +(of same dimensions as y). For Poisson, gamma, and negative binomial +distribution, this corresponds to the offset term. For binomial, this is the +number of trials.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as p x n matrix.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and +\code{phi}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant wrt. +theta.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_mng}. +} +\description{ +Construct an object of class \code{ssm_mng} by directly defining the +corresponding terms of the model. +} +\details{ +The general multivariate non-Gaussian model is defined using the following +observational and state equations: + +\deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and +\eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or +negative binomial distribution for each observation series \eqn{i=1,...,p}. +Here k is the number of disturbance terms (which can be less than m, +the number of states). +} +\examples{ + +set.seed(1) +n <- 20 +x <- cumsum(rnorm(n, sd = 0.5)) +phi <- 2 +y <- cbind( + rgamma(n, shape = phi, scale = exp(x) / phi), + rbinom(n, 10, plogis(x))) + +Z <- matrix(1, 2, 1) +T <- 1 +R <- 0.5 +a1 <- 0 +P1 <- 1 + +update_fn <- function(theta) { + list(R = array(theta[1], c(1, 1, 1)), phi = c(theta[2], 1)) +} + +prior_fn <- function(theta) { + ifelse(all(theta > 0), sum(dnorm(theta, 0, 1, log = TRUE)), -Inf) +} + +model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), + init_theta = c(0.5, 2), + distribution = c("gamma", "binomial"), + u = cbind(1, rep(10, n)), + update_fn = update_fn, prior_fn = prior_fn) + +# smoothing based on approximating gaussian model +ts.plot(cbind(y, fast_smoother(model)), + col = 1:3, lty = c(1, 1, 2)) + +} diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index ae68904d..b19d5934 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -1,110 +1,110 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_nlg} -\alias{ssm_nlg} -\title{General multivariate nonlinear Gaussian state space models} -\usage{ -ssm_nlg( - y, - Z, - H, - T, - R, - Z_gn, - T_gn, - a1, - P1, - theta, - known_params = NA, - known_tv_params = matrix(NA), - n_states, - n_etas, - log_prior_pdf, - time_varying = rep(TRUE, 4), - state_names = paste0("state", 1:n_states) -) -} -\arguments{ -\item{y}{Observations as multivariate time series (or matrix) of length -\eqn{n}.} - -\item{Z, H, T, R}{An external pointers for the C++ functions which -define the corresponding model functions.} - -\item{Z_gn, T_gn}{An external pointers for the C++ functions which -define the gradients of the corresponding model functions.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{theta}{Parameter vector passed to all model functions.} - -\item{known_params}{Vector of known parameters passed to all model -functions.} - -\item{known_tv_params}{Matrix of known parameters passed to all model -functions.} - -\item{n_states}{Number of states in the model.} - -\item{n_etas}{Dimension of the noise term of the transition equation.} - -\item{log_prior_pdf}{An external pointer for the C++ function which -computes the log-prior density given theta.} - -\item{time_varying}{Optional logical vector of length 4, denoting whether -the values of -Z, H, T, and R vary with respect to time variable (given identical states). -If used, this can speed up some computations.} - -\item{state_names}{Names for the states.} -} -\value{ -Object of class \code{ssm_nlg}. -} -\description{ -Constructs an object of class \code{ssm_nlg} by defining the corresponding -terms of the observation and state equation. -} -\details{ -The nonlinear Gaussian model is defined as - -\deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, -(\textrm{observation equation})} -\deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, -(\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions -\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector -\eqn{\theta}. - -Compared to other models, these general models need a bit more effort from -the user, as you must provide the several small C++ snippets which define the -model structure. See examples in the vignette. -} -\examples{ -\dontrun{ -set.seed(1) -n <- 50 -x <- y <- numeric(n) -y[1] <- rnorm(1, exp(x[1]), 0.1) -for(i in 1:(n-1)) { - x[i+1] <- rnorm(1, sin(x[i]), 0.1) - y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) -} - -pntrs <- nlg_example_models("sin_exp") - -model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, - Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, - Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, - theta = c(log_H = log(0.1), log_R = log(0.1)), - log_prior_pdf = pntrs$log_prior_pdf, - n_states = 1, n_etas = 1, state_names = "state") - -out <- ekf(model_nlg, 100) -ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_nlg} +\alias{ssm_nlg} +\title{General multivariate nonlinear Gaussian state space models} +\usage{ +ssm_nlg( + y, + Z, + H, + T, + R, + Z_gn, + T_gn, + a1, + P1, + theta, + known_params = NA, + known_tv_params = matrix(NA), + n_states, + n_etas, + log_prior_pdf, + time_varying = rep(TRUE, 4), + state_names = paste0("state", 1:n_states) +) +} +\arguments{ +\item{y}{Observations as multivariate time series (or matrix) of length +\eqn{n}.} + +\item{Z, H, T, R}{An external pointers for the C++ functions which +define the corresponding model functions.} + +\item{Z_gn, T_gn}{An external pointers for the C++ functions which +define the gradients of the corresponding model functions.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{theta}{Parameter vector passed to all model functions.} + +\item{known_params}{Vector of known parameters passed to all model +functions.} + +\item{known_tv_params}{Matrix of known parameters passed to all model +functions.} + +\item{n_states}{Number of states in the model.} + +\item{n_etas}{Dimension of the noise term of the transition equation.} + +\item{log_prior_pdf}{An external pointer for the C++ function which +computes the log-prior density given theta.} + +\item{time_varying}{Optional logical vector of length 4, denoting whether +the values of +Z, H, T, and R vary with respect to time variable (given identical states). +If used, this can speed up some computations.} + +\item{state_names}{Names for the states.} +} +\value{ +Object of class \code{ssm_nlg}. +} +\description{ +Constructs an object of class \code{ssm_nlg} by defining the corresponding +terms of the observation and state equation. +} +\details{ +The nonlinear Gaussian model is defined as + +\deqn{y_t = Z(t, \alpha_t, \theta) + H(t, \theta) \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = T(t, \alpha_t, \theta) + R(t, \theta)\eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions +\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector +\eqn{\theta}. + +Compared to other models, these general models need a bit more effort from +the user, as you must provide the several small C++ snippets which define the +model structure. See examples in the vignette. +} +\examples{ +\dontrun{ +set.seed(1) +n <- 50 +x <- y <- numeric(n) +y[1] <- rnorm(1, exp(x[1]), 0.1) +for(i in 1:(n-1)) { + x[i+1] <- rnorm(1, sin(x[i]), 0.1) + y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) +} + +pntrs <- nlg_example_models("sin_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(log_H = log(0.1), log_R = log(0.1)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out <- ekf(model_nlg, 100) +ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) +} +} diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 7c732f1a..27bc4ccc 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -1,89 +1,89 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_sde} -\alias{ssm_sde} -\title{Univariate state space model with continuous SDE dynamics} -\usage{ -ssm_sde( - y, - drift, - diffusion, - ddiffusion, - obs_pdf, - prior_pdf, - theta, - x0, - positive -) -} -\arguments{ -\item{y}{Observations as univariate time series (or vector) of length -\eqn{n}.} - -\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions -which -define the drift, diffusion and derivative of diffusion functions of SDE.} - -\item{obs_pdf}{An external pointer for the C++ function which -computes the observational log-density given the the states and parameter -vector theta.} - -\item{prior_pdf}{An external pointer for the C++ function which -computes the prior log-density given the parameter vector theta.} - -\item{theta}{Parameter vector passed to all model functions.} - -\item{x0}{Fixed initial value for SDE at time 0.} - -\item{positive}{If \code{TRUE}, positivity constraint is -forced by \code{abs} in Milstein scheme.} -} -\value{ -Object of class \code{ssm_sde}. -} -\description{ -Constructs an object of class \code{ssm_sde} by defining the functions for -the drift, diffusion and derivative of diffusion terms of univariate SDE, -as well as the log-density of observation equation. We assume that the -observations are measured at integer times (missing values are allowed). -} -\details{ -As in case of \code{ssm_nlg} models, these general models need a bit more -effort from the user, as you must provide the several small C++ snippets -which define the model structure. See vignettes for an example. -} -\examples{ -\dontrun{ -library("sde") -set.seed(1) -# theta_0 = rho = 0.5 -# theta_1 = nu = 2 -# theta_2 = sigma = 0.3 -x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, - drift = expression(0.5 * (2 - x)), - sigma = expression(0.3), - sigma.x = expression(0)) -y <- rpois(50, exp(x[-1])) - -# Template can be found in the vignette -Rcpp::sourceCpp("ssm_sde_template.cpp") -pntrs <- create_xptrs() - -sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, - pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, - c(rho = 0.5, nu = 2, sigma = 0.3), 1, positive = FALSE) - -est <- particle_smoother(sde_model, L = 12, particles = 500) - -ts.plot(cbind(x, est$alphahat, - est$alphahat - 2*sqrt(c(est$Vt)), - est$alphahat + 2*sqrt(c(est$Vt))), - col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) - -# Takes time with finer mesh, parallelization with IS-MCMC helps a lot -out <- run_mcmc(sde_model, L_c = 4, L_f = 8, - particles = 50, iter = 2e4, - threads = 4L) - -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_sde} +\alias{ssm_sde} +\title{Univariate state space model with continuous SDE dynamics} +\usage{ +ssm_sde( + y, + drift, + diffusion, + ddiffusion, + obs_pdf, + prior_pdf, + theta, + x0, + positive +) +} +\arguments{ +\item{y}{Observations as univariate time series (or vector) of length +\eqn{n}.} + +\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions +which +define the drift, diffusion and derivative of diffusion functions of SDE.} + +\item{obs_pdf}{An external pointer for the C++ function which +computes the observational log-density given the the states and parameter +vector theta.} + +\item{prior_pdf}{An external pointer for the C++ function which +computes the prior log-density given the parameter vector theta.} + +\item{theta}{Parameter vector passed to all model functions.} + +\item{x0}{Fixed initial value for SDE at time 0.} + +\item{positive}{If \code{TRUE}, positivity constraint is +forced by \code{abs} in Milstein scheme.} +} +\value{ +Object of class \code{ssm_sde}. +} +\description{ +Constructs an object of class \code{ssm_sde} by defining the functions for +the drift, diffusion and derivative of diffusion terms of univariate SDE, +as well as the log-density of observation equation. We assume that the +observations are measured at integer times (missing values are allowed). +} +\details{ +As in case of \code{ssm_nlg} models, these general models need a bit more +effort from the user, as you must provide the several small C++ snippets +which define the model structure. See vignettes for an example. +} +\examples{ +\dontrun{ +library("sde") +set.seed(1) +# theta_0 = rho = 0.5 +# theta_1 = nu = 2 +# theta_2 = sigma = 0.3 +x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, + drift = expression(0.5 * (2 - x)), + sigma = expression(0.3), + sigma.x = expression(0)) +y <- rpois(50, exp(x[-1])) + +# Template can be found in the vignette +Rcpp::sourceCpp("ssm_sde_template.cpp") +pntrs <- create_xptrs() + +sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, + pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, + c(rho = 0.5, nu = 2, sigma = 0.3), 1, positive = FALSE) + +est <- particle_smoother(sde_model, L = 12, particles = 500) + +ts.plot(cbind(x, est$alphahat, + est$alphahat - 2*sqrt(c(est$Vt)), + est$alphahat + 2*sqrt(c(est$Vt))), + col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) + +# Takes time with finer mesh, parallelization with IS-MCMC helps a lot +out <- run_mcmc(sde_model, L_c = 4, L_f = 8, + particles = 50, iter = 2e4, + threads = 4L) + +} +} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index caefa952..2e07ebc3 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -1,210 +1,212 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_ulg} -\alias{ssm_ulg} -\title{General univariate linear-Gaussian state space models} -\usage{ -ssm_ulg( - y, - Z, - H, - T, - R, - a1 = NULL, - P1 = NULL, - init_theta = numeric(0), - D = NULL, - C = NULL, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as time series (or vector) of length \eqn{n}.} - -\item{Z}{System matrix Z of the observation equation as m x 1 or -m x n matrix.} - -\item{H}{Vector of standard deviations. Either a scalar or a vector of -length n.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} - -\item{R}{Lower triangular matrix R the state equation. Either a -m x k matrix or a m x k x n array.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms for observation equation, given as a -length n vector.} - -\item{C}{Intercept terms for state equation, given as m x n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_ulg}. -} -\description{ -Construct an object of class \code{ssm_ulg} by directly defining the -corresponding terms of the model. -} -\details{ -The general univariate linear-Gaussian model is defined using the following -observational and state equations: - -\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, -(\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, -(\textrm{transition equation})} - -where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here k is the number of disturbance terms which can be less than m, the -number of states. - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, -and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. -theta. -Note that while you can input say R as m x k matrix for \code{ssm_ulg}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -and then check the expected structure of the model components from the -output. -} -\examples{ - -# Regression model with time-varying coefficients -set.seed(1) -n <- 100 -x1 <- rnorm(n) -x2 <- rnorm(n) -b1 <- 1 + cumsum(rnorm(n, sd = 0.5)) -b2 <- 2 + cumsum(rnorm(n, sd = 0.1)) -y <- 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) - -Z <- rbind(1, x1, x2) -H <- 0.1 -T <- diag(3) -R <- diag(c(0, 1, 0.1)) -a1 <- rep(0, 3) -P1 <- diag(10, 3) - -# updates the model given the current values of the parameters -update_fn <- function(theta) { - R <- diag(c(0, theta[1], theta[2])) - dim(R) <- c(3, 3, 1) - list(R = R, H = theta[3]) -} -# prior for standard deviations as half-normal(1) -prior_fn <- function(theta) { - if(any(theta < 0)) { - log_p <- -Inf - } else { - log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) - } - log_p -} - -model <- ssm_ulg(y, Z, H, T, R, a1, P1, - init_theta = c(1, 0.1, 0.1), - update_fn = update_fn, prior_fn = prior_fn) - -out <- run_mcmc(model, iter = 10000) -out -sumr <- summary(out, variable = "state") -ts.plot(sumr$Mean, col = 1:3) -lines(b1, col= 2, lty = 2) -lines(b2, col= 3, lty = 2) - -# Perhaps easiest way to construct a general SSM for bssm is to use the -# model building functionality of KFAS: -library("KFAS") - -model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = 5e-4)+ - SSMseasonal(period = 12, sea.type = "trigonometric", Q = 0) + - log(PetrolPrice) + law, data = Seatbelts, H = 0.005) - -# use as_bssm function for conversion, kappa defines the -# prior variance for diffuse states -model_bssm <- as_bssm(model_kfas, kappa = 100) - -# define updating function for parameter estimation -# we can use SSModel and as_bssm functions here as well -# (for large model it is more efficient to do this -# "manually" by constructing only necessary matrices, -# i.e., in this case a list with H and Q) - -updatefn <- function(theta) { - - model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ - SSMseasonal(period = 12, - sea.type = "trigonometric", Q = theta[2]^2) + - log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) - - as_bssm(model_kfas, kappa = 100) -} - -prior <- function(theta) { - if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) -} -init_theta <- rep(1e-2, 3) -c("sd_level", "sd_seasonal", "sd_y") -model_bssm <- as_bssm(model_kfas, kappa = 100, - init_theta = init_theta, - prior_fn = prior, update_fn = updatefn) - -\dontrun{ -out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) -out - -# Above the regression coefficients are modelled as -# time-invariant latent states. -# Here is an alternative way where we use variable D so that the -# coefficients are part of parameter vector theta: - -updatefn2 <- function(theta) { - # note no PetrolPrice or law variables here - model_kfas2 <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ - SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2), - data = Seatbelts, H = theta[3]^2) - - X <- model.matrix(~ -1 + law + log(PetrolPrice), data = Seatbelts) - D <- t(X \%*\% theta[4:5]) - as_bssm(model_kfas2, D = D, kappa = 100) -} -prior2 <- function(theta) { - if(any(theta[1:3] < 0)) { - -Inf - } else { - sum(dnorm(theta[1:3], 0, 0.1, log = TRUE)) + - sum(dnorm(theta[4:5], 0, 10, log = TRUE)) - } -} -init_theta <- c(rep(1e-2, 3), 0, 0) -names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y", "law", "Petrol") -model_bssm2 <- updatefn2(init_theta) -model_bssm2$theta <- init_theta -model_bssm2$prior_fn <- prior2 -model_bssm2$update_fn <- updatefn2 - -out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) -out2 -} -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_ulg} +\alias{ssm_ulg} +\title{General univariate linear-Gaussian state space models} +\usage{ +ssm_ulg( + y, + Z, + H, + T, + R, + a1 = NULL, + P1 = NULL, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as numeric vector or univariate \code{ts} object of +length n.} + +\item{Z}{System matrix Z of the observation equation as m x 1 or +m x n matrix.} + +\item{H}{Vector of standard deviations. Either a scalar or a vector of +length n.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array.} + +\item{R}{Lower triangular matrix R the state equation. Either a +m x k matrix or a m x k x n array.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms for observation equation, given as a +length n vector.} + +\item{C}{Intercept terms for state equation, given as m x n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. See details.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_ulg}. +} +\description{ +Construct an object of class \code{ssm_ulg} by directly defining the +corresponding terms of the model. +} +\details{ +The general univariate linear-Gaussian model is defined using the following +observational and state equations: + +\deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, +(\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. +Here k is the number of disturbance terms which can be less than m, the +number of states. + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant wrt. +theta. +Note that while you can input say R as m x k matrix for \code{ssm_ulg}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from the +output. +} +\examples{ + +# Regression model with time-varying coefficients +set.seed(1) +n <- 100 +x1 <- rnorm(n) +x2 <- rnorm(n) +b1 <- 1 + cumsum(rnorm(n, sd = 0.5)) +b2 <- 2 + cumsum(rnorm(n, sd = 0.1)) +y <- 1 + b1 * x1 + b2 * x2 + rnorm(n, sd = 0.1) + +Z <- rbind(1, x1, x2) +H <- 0.1 +T <- diag(3) +R <- diag(c(0, 1, 0.1)) +a1 <- rep(0, 3) +P1 <- diag(10, 3) + +# updates the model given the current values of the parameters +update_fn <- function(theta) { + R <- diag(c(0, theta[1], theta[2])) + dim(R) <- c(3, 3, 1) + list(R = R, H = theta[3]) +} +# prior for standard deviations as half-normal(1) +prior_fn <- function(theta) { + if(any(theta < 0)) { + log_p <- -Inf + } else { + log_p <- sum(dnorm(theta, 0, 1, log = TRUE)) + } + log_p +} + +model <- ssm_ulg(y, Z, H, T, R, a1, P1, + init_theta = c(1, 0.1, 0.1), + update_fn = update_fn, prior_fn = prior_fn, + state_names = c("level", "b1", "b2")) + +out <- run_mcmc(model, iter = 10000) +out +sumr <- summary(out, variable = "state") +ts.plot(sumr$Mean, col = 1:3) +lines(b1, col= 2, lty = 2) +lines(b2, col= 3, lty = 2) + +# Perhaps easiest way to construct a general SSM for bssm is to use the +# model building functionality of KFAS: +library("KFAS") + +model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = 5e-4)+ + SSMseasonal(period = 12, sea.type = "trigonometric", Q = 0) + + log(PetrolPrice) + law, data = Seatbelts, H = 0.005) + +# use as_bssm function for conversion, kappa defines the +# prior variance for diffuse states +model_bssm <- as_bssm(model_kfas, kappa = 100) + +# define updating function for parameter estimation +# we can use SSModel and as_bssm functions here as well +# (for large model it is more efficient to do this +# "manually" by constructing only necessary matrices, +# i.e., in this case a list with H and Q) + +updatefn <- function(theta) { + + model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ + SSMseasonal(period = 12, + sea.type = "trigonometric", Q = theta[2]^2) + + log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) + + as_bssm(model_kfas, kappa = 100) +} + +prior <- function(theta) { + if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) +} +init_theta <- rep(1e-2, 3) +c("sd_level", "sd_seasonal", "sd_y") +model_bssm <- as_bssm(model_kfas, kappa = 100, + init_theta = init_theta, + prior_fn = prior, update_fn = updatefn) + +\dontrun{ +out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) +out + +# Above the regression coefficients are modelled as +# time-invariant latent states. +# Here is an alternative way where we use variable D so that the +# coefficients are part of parameter vector theta: + +updatefn2 <- function(theta) { + # note no PetrolPrice or law variables here + model_kfas2 <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ + SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2), + data = Seatbelts, H = theta[3]^2) + + X <- model.matrix(~ -1 + law + log(PetrolPrice), data = Seatbelts) + D <- t(X \%*\% theta[4:5]) + as_bssm(model_kfas2, D = D, kappa = 100) +} +prior2 <- function(theta) { + if(any(theta[1:3] < 0)) { + -Inf + } else { + sum(dnorm(theta[1:3], 0, 0.1, log = TRUE)) + + sum(dnorm(theta[4:5], 0, 10, log = TRUE)) + } +} +init_theta <- c(rep(1e-2, 3), 0, 0) +names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y", "law", "Petrol") +model_bssm2 <- updatefn2(init_theta) +model_bssm2$theta <- init_theta +model_bssm2$prior_fn <- prior2 +model_bssm2$update_fn <- updatefn2 + +out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) +out2 +} +} diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 5b7e552d..e27e1246 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -1,116 +1,116 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{ssm_ung} -\alias{ssm_ung} -\title{General univariate non-Gaussian state space model} -\usage{ -ssm_ung( - y, - Z, - T, - R, - a1 = NULL, - P1 = NULL, - distribution, - phi = 1, - u = 1, - init_theta = numeric(0), - D = NULL, - C = NULL, - state_names, - update_fn = default_update_fn, - prior_fn = default_prior_fn -) -} -\arguments{ -\item{y}{Observations as time series (or vector) of length \eqn{n}.} - -\item{Z}{System matrix Z of the observation equation. Either a -vector of length m, -a m x n matrix, or object which can be coerced to such.} - -\item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array, or object which can be coerced to such.} - -\item{R}{Lower triangular matrix R the state equation. Either -a m x k matrix or a m x k x n array, or object which can be coerced to such.} - -\item{a1}{Prior mean for the initial state as a vector of length m.} - -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} - -\item{distribution}{Distribution of the observed time series. Possible -choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and -\code{"negative binomial"}.} - -\item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, and for other -distributions this is ignored.} - -\item{u}{Constant parameter vector for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} - -\item{init_theta}{Initial values for the unknown hyperparameters theta.} - -\item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a -scalar or vector of length n.} - -\item{C}{Intercept terms \eqn{C_t} for the state equation, given as a -m times 1 or m times n matrix.} - -\item{state_names}{Names for the states.} - -\item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} - -\item{prior_fn}{Function which returns log of prior density -given input vector theta.} -} -\value{ -Object of class \code{ssm_ung}. -} -\description{ -Construct an object of class \code{ssm_ung} by directly defining the -corresponding terms of the model. -} -\details{ -The general univariate non-Gaussian model is defined using the following -observational and state equations: - -\deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} -\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, -(\textrm{transition equation})} - -where \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, -and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or -negative binomial distribution. -Here k is the number of disturbance terms which can be less than m, -the number of states. - -The \code{update_fn} function should take only one -vector argument which is used to create list with elements named as -\code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, - and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant -wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_ung}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -and then check the expected structure of the model components from -the output. -} -\examples{ - -data("drownings", package = "bssm") -model <- ssm_ung(drownings[, "deaths"], Z = 1, T = 1, R = 0.2, - a1 = 0, P1 = 10, distribution = "poisson", u = drownings[, "population"]) - -# approximate results based on Gaussian approximation -out <- smoother(model) -ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{ssm_ung} +\alias{ssm_ung} +\title{General univariate non-Gaussian state space model} +\usage{ +ssm_ung( + y, + Z, + T, + R, + a1 = NULL, + P1 = NULL, + distribution, + phi = 1, + u, + init_theta = numeric(0), + D = NULL, + C = NULL, + state_names, + update_fn = default_update_fn, + prior_fn = default_prior_fn +) +} +\arguments{ +\item{y}{Observations as time series (or vector) of length \eqn{n}.} + +\item{Z}{System matrix Z of the observation equation. Either a +vector of length m, +a m x n matrix, or object which can be coerced to such.} + +\item{T}{System matrix T of the state equation. Either a m x m matrix or a +m x m x n array, or object which can be coerced to such.} + +\item{R}{Lower triangular matrix R the state equation. Either +a m x k matrix or a m x k x n array, or object which can be coerced to such.} + +\item{a1}{Prior mean for the initial state as a vector of length m.} + +\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} + +\item{distribution}{Distribution of the observed time series. Possible +choices are +\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\code{"negative binomial"}.} + +\item{phi}{Additional parameter relating to the non-Gaussian distribution. +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, and for other +distributions this is ignored.} + +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset +term. For binomial, this is the number of trials.} + +\item{init_theta}{Initial values for the unknown hyperparameters theta.} + +\item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a +scalar or vector of length n.} + +\item{C}{Intercept terms \eqn{C_t} for the state equation, given as a +m times 1 or m times n matrix.} + +\item{state_names}{Names for the states.} + +\item{update_fn}{Function which returns list of updated model +components given input vector theta. See details.} + +\item{prior_fn}{Function which returns log of prior density +given input vector theta.} +} +\value{ +Object of class \code{ssm_ung}. +} +\description{ +Construct an object of class \code{ssm_ung} by directly defining the +corresponding terms of the model. +} +\details{ +The general univariate non-Gaussian model is defined using the following +observational and state equations: + +\deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} +\deqn{\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, +(\textrm{transition equation})} + +where \eqn{\eta_t \sim N(0, I_k)} and +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, +and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or +negative binomial distribution. +Here k is the number of disturbance terms which can be less than m, +the number of states. + +The \code{update_fn} function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, + and \code{C}, +where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant +wrt. theta. +Note that while you can input say R as m x k matrix for \code{ssm_ung}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from +the output. +} +\examples{ + +data("drownings", package = "bssm") +model <- ssm_ung(drownings[, "deaths"], Z = 1, T = 1, R = 0.2, + a1 = 0, P1 = 10, distribution = "poisson", u = drownings[, "population"]) + +# approximate results based on Gaussian approximation +out <- smoother(model) +ts.plot(cbind(model$y / model$u, exp(out$alphahat)), col = 1:2) +} diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index 57c9ce8e..a065368c 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -65,7 +65,7 @@ y <- rbinom(n, size = u, plogis(0.5 * x1 + x2 + alpha)) ts.plot(y / u) model <- ar1_ng(y, distribution = "binomial", - rho = uniform(0.5, -1, 1), sigma = gamma(1, 2, 0.001), + rho = uniform(0.5, -1, 1), sigma = gamma_prior(1, 2, 0.001), mu = normal(0, 0, 10), xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), u = u) @@ -79,12 +79,12 @@ estN$N } } \references{ -A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn, +Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). Efficient implementation of Markov chain Monte Carlo when using an -unbiased likelihood estimator, Biometrika, 102, 2, 2015, Pages 295–313, +unbiased likelihood estimator, Biometrika, 102(2) p. 295-313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index ed7838f8..c6999a2c 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -1,38 +1,38 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{summary.mcmc_output} -\alias{summary.mcmc_output} -\title{Summary of MCMC object} -\usage{ -\method{summary}{mcmc_output}(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) -} -\arguments{ -\item{object}{Output from \code{run_mcmc}} - -\item{return_se}{if \code{FALSE} (default), computation of standard -errors and effective sample sizes is omitted.} - -\item{variable}{Are the summary statistics computed for either -\code{"theta"} (default), \code{"states"}, or \code{"both"}?} - -\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only -for hyperparameters theta.} - -\item{...}{Ignored.} -} -\description{ -This functions returns a list containing mean, standard deviations, -standard errors, and effective sample size estimates for parameters and -states. -} -\details{ -For IS-MCMC two types of standard errors are reported. -SE-IS can be regarded as the square root of independent IS variance, -whereas SE corresponds to the square root of total asymptotic variance -(see Remark 3 of Vihola et al. (2020)). -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1– 38. https://doi.org/10.1111/sjos.12492 -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print_mcmc.R +\name{summary.mcmc_output} +\alias{summary.mcmc_output} +\title{Summary of MCMC object} +\usage{ +\method{summary}{mcmc_output}(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) +} +\arguments{ +\item{object}{Output from \code{run_mcmc}} + +\item{return_se}{if \code{FALSE} (default), computation of standard +errors and effective sample sizes is omitted.} + +\item{variable}{Are the summary statistics computed for either +\code{"theta"} (default), \code{"states"}, or \code{"both"}?} + +\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only +for hyperparameters theta.} + +\item{...}{Ignored.} +} +\description{ +This functions returns a list containing mean, standard deviations, +standard errors, and effective sample size estimates for parameters and +states. +} +\details{ +For IS-MCMC two types of standard errors are reported. +SE-IS can be regarded as the square root of independent IS variance, +whereas SE corresponds to the square root of total asymptotic variance +(see Remark 3 of Vihola et al. (2020)). +} +\references{ +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +} diff --git a/man/svm.Rd b/man/svm.Rd index fefdac96..9d4296eb 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -1,52 +1,52 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/models.R -\name{svm} -\alias{svm} -\title{Stochastic Volatility Model} -\usage{ -svm(y, mu, rho, sd_ar, sigma) -} -\arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} - -\item{mu}{Prior for mu parameter of transition equation.} - -\item{rho}{prior for autoregressive coefficient.} - -\item{sd_ar}{Prior for the standard deviation of noise of the AR-process.} - -\item{sigma}{Prior for sigma parameter of observation equation, internally -denoted as phi. Ignored if \code{mu} is provided. Note that typically -parametrization using mu is preferred due to better numerical properties and -availability of better Gaussian approximation. -Most notably the global approximation approach does not work with sigma -parameterization as sigma is not a parameter of the resulting approximate -model.} -} -\value{ -Object of class \code{svm}. -} -\description{ -Constructs a simple stochastic volatility model with Gaussian errors and -first order autoregressive signal. -} -\examples{ - -data("exchange") -exchange <- exchange[1:100] # faster CRAN check -model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), - sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) - -obj <- function(pars) { - -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)), particles = 0) -} -opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), - upper = c(0.999,10,10)) -pars <- opt$par -model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/models.R +\name{svm} +\alias{svm} +\title{Stochastic Volatility Model} +\usage{ +svm(y, mu, rho, sd_ar, sigma) +} +\arguments{ +\item{y}{Vector or a \code{\link{ts}} object of observations.} + +\item{mu}{Prior for mu parameter of transition equation.} + +\item{rho}{prior for autoregressive coefficient.} + +\item{sd_ar}{Prior for the standard deviation of noise of the AR-process.} + +\item{sigma}{Prior for sigma parameter of observation equation, internally +denoted as phi. Ignored if \code{mu} is provided. Note that typically +parametrization using mu is preferred due to better numerical properties and +availability of better Gaussian approximation. +Most notably the global approximation approach does not work with sigma +parameterization as sigma is not a parameter of the resulting approximate +model.} +} +\value{ +Object of class \code{svm}. +} +\description{ +Constructs a simple stochastic volatility model with Gaussian errors and +first order autoregressive signal. +} +\examples{ + +data("exchange") +exchange <- exchange[1:100] # faster CRAN check +model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), + sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) + +obj <- function(pars) { + -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), + sd_ar = halfnormal(pars[2],sd=5), + sigma = halfnormal(pars[3],sd=2)), particles = 0) +} +opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), + upper = c(0.999,10,10)) +pars <- opt$par +model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), + sd_ar = halfnormal(pars[2],sd=5), + sigma = halfnormal(pars[3],sd=2)) + +} From 3c6921014fb36c4eb620b84069dedfa2c2af0f70 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 00:28:51 +0300 Subject: [PATCH 076/180] u to x in check_u --- R/check_arguments.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 6eb400c9..6477aba4 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -142,7 +142,7 @@ check_u <- function(x, y, multivariate = FALSE) { if(!identical(dim(y), dim(x))) stop("Dimensions of 'y' and 'u' do not match. ") } else { - if (length(x) == 1) u <- rep(x, length(y)) + if (length(x) == 1) x <- rep(x, length(y)) if (!(is.vector(x) && !is.list(x)) && !is.numeric(x)) { stop("Argument 'u' must be a numeric vector or ts object.") } From 44b4e11b94ec508e933a3a852a0cb06fbcef64a0 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 00:53:09 +0300 Subject: [PATCH 077/180] update version --- DESCRIPTION | 2 +- README.md | 3 +-- codemeta.json | 62 ++++++++++++++++++++++++++++++++++++++++----------- 3 files changed, 51 insertions(+), 16 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e9be7911..716febc9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.6 +Version: 1.1.7 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/README.md b/README.md index 4a6b6ef3..12fa5309 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,10 @@ -[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) [![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) [![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) - bssm: an R package for Bayesian inference of state space models ========================================================================== diff --git a/codemeta.json b/codemeta.json index 282a1987..7710e40c 100644 --- a/codemeta.json +++ b/codemeta.json @@ -1,5 +1,8 @@ { - "@context": ["https://doi.org/10.5063/schema/codemeta-2.0", "http://schema.org"], + "@context": [ + "https://doi.org/10.5063/schema/codemeta-2.0", + "http://schema.org" + ], "@type": "SoftwareSourceCode", "identifier": "bssm", "description": "Efficient methods for Bayesian inference of state space models \n via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel \n importance sampling type weighted estimators \n (Vihola, Helske, and Franks, 2020, ). \n Gaussian, Poisson, binomial, negative binomial, and Gamma\n observation densities and basic stochastic volatility models \n with linear-Gaussian state dynamics, \n as well as general non-linear Gaussian models and discretised \n diffusion models are supported.", @@ -7,7 +10,7 @@ "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "1.1.6", + "version": "1.1.7", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", @@ -48,6 +51,18 @@ } ], "softwareSuggestions": [ + { + "@type": "SoftwareApplication", + "identifier": "covr", + "name": "covr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=covr" + }, { "@type": "SoftwareApplication", "identifier": "dplyr", @@ -125,28 +140,28 @@ }, { "@type": "SoftwareApplication", - "identifier": "ramcmc", - "name": "ramcmc", + "identifier": "rmarkdown", + "name": "rmarkdown", + "version": ">= 0.8.1", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=ramcmc" + "sameAs": "https://CRAN.R-project.org/package=rmarkdown" }, { "@type": "SoftwareApplication", - "identifier": "rmarkdown", - "name": "rmarkdown", - "version": ">= 0.8.1", + "identifier": "ramcmc", + "name": "ramcmc", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=rmarkdown" + "sameAs": "https://CRAN.R-project.org/package=ramcmc" }, { "@type": "SoftwareApplication", @@ -192,6 +207,18 @@ "name": "R", "version": ">= 3.5.0" }, + { + "@type": "SoftwareApplication", + "identifier": "checkmate", + "name": "checkmate", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=checkmate" + }, { "@type": "SoftwareApplication", "identifier": "coda", @@ -241,9 +268,17 @@ ], "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS", "readme": "https://github.com/helske/bssm/blob/master/README.md", - "fileSize": "984.687KB", - "contIntegration": "https://github.com/helske/bssm/actions", - "keywords": ["bayesian-inference", "markov-chain-monte-carlo", "particle-filter", "time-series", "state-space", "r", "cpp"], + "fileSize": "10636.929KB", + "contIntegration": ["https://github.com/helske/bssm/actions", "https://codecov.io/gh/helske/bssm?branch=master"], + "keywords": [ + "bayesian-inference", + "markov-chain-monte-carlo", + "particle-filter", + "time-series", + "state-space", + "r", + "cpp" + ], "citation": [ { "@type": "CreativeWork", @@ -297,5 +332,6 @@ } } } - ] + ], + "developmentStatus": "https://www.repostatus.org/#active" } From ef3acb4da5ef76f56a60c2be62ddd2b2ddd948a8 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 17:42:26 +0300 Subject: [PATCH 078/180] improve documentation --- R/bssm-package.R | 5 + R/models.R | 260 ++++++++++++++++--------------------- R/predict.R | 28 ++-- man/ar1_lg.Rd | 16 ++- man/ar1_ng.Rd | 3 +- man/bsm_lg.Rd | 42 ++++-- man/bsm_ng.Rd | 70 ++++++---- man/bssm.Rd | 6 + man/predict.mcmc_output.Rd | 25 ++-- man/ssm_mlg.Rd | 11 +- man/ssm_mng.Rd | 10 +- man/ssm_ulg.Rd | 30 +++-- man/ssm_ung.Rd | 19 ++- man/svm.Rd | 12 +- 14 files changed, 291 insertions(+), 246 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index 5f75b405..a99b4abe 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -2,12 +2,17 @@ #' #' This package contains functions for efficient Bayesian inference of state #' space models, where model is assumed to be either +#' #' * Exponential family state space models, where the state equation is linear #' Gaussian, and the conditional observation density is either Gaussian, #' Poisson, binomial, negative binomial or Gamma density. +#' #' * Basic stochastic volatility model. +#' #' * General non-linear model with Gaussian noise terms. +#' #' * Model with continuous SDE dynamics. +#' #' For formal definition of the currently supported models and methods, as #' well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, #' see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package diff --git a/R/models.R b/R/models.R index 2c7e9fd8..9f4164de 100644 --- a/R/models.R +++ b/R/models.R @@ -37,27 +37,18 @@ default_update_fn <- function(theta) { #' and then check the expected structure of the model components from the #' output. #' -#' @param y Observations as numeric vector or univariate \code{ts} object of -#' length n. -#' @param Z System matrix Z of the observation equation as m x 1 or -#' m x n matrix. +#' @inheritParams ssm_ung #' @param H Vector of standard deviations. Either a scalar or a vector of -#' length n. -#' @param T System matrix T of the state equation. Either a m x m matrix or a -#' m x m x n array. -#' @param R Lower triangular matrix R the state equation. Either a -#' m x k matrix or a m x k x n array. -#' @param a1 Prior mean for the initial state as a vector of length m. -#' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param init_theta Initial values for the unknown hyperparameters theta. -#' @param D Intercept terms for observation equation, given as a -#' length n vector. -#' @param C Intercept terms for state equation, given as m x n matrix. +#' length n. #' @param update_fn Function which returns list of updated model -#' components given input vector theta. See details. -#' @param prior_fn Function which returns log of prior density -#' given input vector theta. -#' @param state_names Names for the states. +#' components given input vector theta. This function should take only one +#' vector argument which is used to create list with elements named as +#' \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and +#' \code{C}, where each element matches the dimensions of the original model +#' It's best to check the internal dimensions with \code{str(model_object)} as +#' the dimensions of input arguments can differ from the final dimensions. +#' If any of these components is missing, it is assumed to be constant wrt. +#' theta. #' @return Object of class \code{ssm_ulg}. #' @export #' @examples @@ -222,7 +213,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, xreg = matrix(0, 0, 0), beta = numeric(0)), - class = c("ssm_ulg", "gaussian")) + class = c("ssm_ulg", "gaussian", "bssm_model")) } #' General univariate non-Gaussian state space model #' @@ -256,6 +247,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' and then check the expected structure of the model components from #' the output. #' +#' @inheritParams bsm_ng #' @param y Observations as time series (or vector) of length \eqn{n}. #' @param Z System matrix Z of the observation equation. Either a #' vector of length m, @@ -266,17 +258,6 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' a m x k matrix or a m x k x n array, or object which can be coerced to such. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param distribution Distribution of the observed time series. Possible -#' choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and -#' \code{"negative binomial"}. -#' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for -#' gamma distribution this is the shape parameter, and for other -#' distributions this is ignored. -#' @param u Vector of positive constants for non-Gaussian models. For Poisson, -#' gamma, and negative binomial distribution, this corresponds to the offset -#' term. For binomial, this is the number of trials. #' @param state_names Names for the states. #' @param C Intercept terms \eqn{C_t} for the state equation, given as a #' m times 1 or m times n matrix. @@ -284,7 +265,14 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' scalar or vector of length n. #' @param init_theta Initial values for the unknown hyperparameters theta. #' @param update_fn Function which returns list of updated model -#' components given input vector theta. See details. +#' components given input vector theta. This function should take only one +#' vector argument which is used to create list with elements named as +#' \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and +#' \code{phi}, where each element matches the dimensions of the original model. +#' If any of these components is missing, it is assumed to be constant wrt. +#' theta. It's best to check the internal dimensions with +#' \code{str(model_object)} as the dimensions of input arguments can differ +#' from the final dimensions. #' @param prior_fn Function which returns log of prior density #' given input vector theta. #' @return Object of class \code{ssm_ung}. @@ -346,7 +334,7 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, prior_fn = prior_fn, theta = init_theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE, xreg = matrix(0, 0, 0), beta = numeric(0)), - class = c("ssm_ung", "nongaussian")) + class = c("ssm_ung", "nongaussian", "bssm_model")) } #' General multivariate linear Gaussian state space models @@ -378,6 +366,7 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, #' \code{update_fn} should return R as m x k x 1 in this case. #' It might be useful to first construct the model without updating function #' +#' @inheritParams ssm_ulg #' @param y Observations as multivariate time series or matrix with #' dimensions n x p. #' @param Z System matrix Z of the observation equation as p x m matrix or @@ -388,22 +377,8 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, #' m x m x n array. #' @param R Lower triangular matrix R the state equation. Either a m x k matrix #' or a m x k x n array. -#' @param a1 Prior mean for the initial state as a vector of length m. -#' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param init_theta Initial values for the unknown hyperparameters theta. #' @param D Intercept terms for observation equation, given as a p x n matrix. #' @param C Intercept terms for state equation, given as m x n matrix. -#' @param update_fn Function which returns list of updated model -#' components given input vector theta. This function should take only one -#' vector argument which is used to create list with elements named as -#' \code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, -#' and \code{C}, -#' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be -#' onstant wrt. theta. -#' @param prior_fn Function which returns log of prior density -#' given input vector theta. -#' @param state_names Names for the states. #' @return Object of class \code{ssm_mlg}. #' @export #' @examples @@ -452,7 +427,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, a1 = a1, P1 = P1, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, - state_names = state_names), class = c("ssm_mlg", "gaussian")) + state_names = state_names), class = c("ssm_mlg", "gaussian", "bssm_model")) } #' General Non-Gaussian State Space Model @@ -473,7 +448,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' negative binomial distribution for each observation series \eqn{i=1,...,p}. #' Here k is the number of disturbance terms (which can be less than m, #' the number of states). -#' +#' @inheritParams ssm_ung #' @param y Observations as multivariate time series or matrix with dimensions #' n x p. #' @param Z System matrix Z of the observation equation as p x m matrix or @@ -483,8 +458,6 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' @param R Lower triangular matrix R the state equation. Either a m x k #' matrix or a #' m x k x n array. -#' @param a1 Prior mean for the initial state as a vector of length m. -#' @param P1 Prior covariance matrix for the initial state as m x m matrix. #' @param distribution vector of distributions of the observed series. #' Possible choices are #' \code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, @@ -492,26 +465,13 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' @param phi Additional parameters relating to the non-Gaussian distributions. #' For negative binomial distribution this is the dispersion term, for #' gamma distribution this is the shape parameter, for Gaussian this is -#' standard deviation, -#' and for other distributions this is ignored. +#' standard deviation, and for other distributions this is ignored. #' @param u Matrix of positive constants for non-Gaussian models #' (of same dimensions as y). For Poisson, gamma, and negative binomial #' distribution, this corresponds to the offset term. For binomial, this is the #' number of trials. -#' @param init_theta Initial values for the unknown hyperparameters theta. #' @param D Intercept terms for observation equation, given as p x n matrix. #' @param C Intercept terms for state equation, given as m x n matrix. -#' @param update_fn Function which returns list of updated model -#' components given input vector theta. This function should take only one -#' vector argument which is used to create list with elements named as -#' \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and -#' \code{phi}, -#' where each element matches the dimensions of the original model. -#' If any of these components is missing, it is assumed to be constant wrt. -#' theta. -#' @param prior_fn Function which returns log of prior density -#' given input vector theta. -#' @param state_names Names for the states. #' @return Object of class \code{ssm_mng}. #' @export #' @examples @@ -606,34 +566,16 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, initial_mode = initial_mode, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), - class = c("ssm_mng", "nongaussian")) + class = c("ssm_mng", "nongaussian", "bssm_model")) } #' Basic Structural (Time Series) Model #' #' Constructs a basic structural model with local level or local trend #' component and seasonal component. -#' -#' @param y Vector or a \code{\link{ts}} object of observations. -#' @param sd_y A fixed value or prior for the standard error of -#' observation equation. See \link[=uniform]{priors} for details. -#' @param sd_level A fixed value or a prior for the standard error -#' of the noise in level equation. See \link[=uniform]{priors} for details. -#' @param sd_slope A fixed value or a prior for the standard error -#' of the noise in slope equation. See \link[=uniform]{priors} for details. -#' If missing, the slope term is omitted from the model. -#' @param sd_seasonal A fixed value or a prior for the standard error -#' of the noise in seasonal equation. See \link[=uniform]{priors} for details. -#' If missing, the seasonal component is omitted from the model. -#' @param xreg Matrix containing covariates. -#' @param beta Prior for the regression coefficients. -#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} -#' (in case of multiple coefficients) or missing in case of no covariates. -#' @param period Length of the seasonal pattern. -#' Default is \code{frequency(y)}. Must be at least 3. -#' @param a1 Prior means for the initial states (level, slope, seasonals). -#' Defaults to vector of zeros. -#' @param P1 Prior covariance for the initial states (level, slope, seasonals). -#' Default is diagonal matrix with 1000 on the diagonal. +#' +#' @inheritParams bsm_ng +#' @param sd_y Standard deviation of the noise of observation equation. +#' Should be an object of class \code{bssm_prior} or scalar #' @param D,C Intercept terms for observation and #' state equations, given as a length n vector and m times n matrix #' respectively (or scalar and m times 1 matrix). @@ -642,14 +584,28 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' @examples #' #' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +#' # period here is redundant as frequency(UKgas) = 4 #' model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, -#' sd_slope = prior, sd_seasonal = prior) +#' sd_slope = prior, sd_seasonal = prior, period = 4) #' #' mcmc_out <- run_mcmc(model, iter = 5000) #' summary(expand_sample(mcmc_out, "theta"))$stat #' mcmc_out$theta[which.max(mcmc_out$posterior), ] #' sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] #' +#' set.seed(1) +#' n <- 10 +#' x <- rnorm(n) +#' level <- numeric(n) +#' level[1] <- rnorm(1) +#' for (i in 2:n) level[i] <- rnorm(1, -0.2 + level[i-1], sd = 0.1) +#' y <- rnorm(n, 2.1 + x + level) +#' model <- bsm_lg(y, sd_y = halfnormal(1, 5), sd_level = 0.1, a1 = level[1], +#' P1 = matrix(0, 1, 1), xreg = x, beta = normal(1, 0, 1), +#' D = 2.1, C = matrix(-0.2, 1, 1)) +#' +#' ts.plot(cbind(fast_smoother(model), level), col = 1:2) +#' bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, D = NULL, C = NULL) { @@ -810,7 +766,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, fixed = as.integer(!notfixed), prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, - theta = theta), class = c("bsm_lg", "ssm_ulg", "gaussian")) + theta = theta), class = c("bsm_lg", "ssm_ulg", "gaussian", "bssm_model")) } #' Non-Gaussian Basic Structural (Time Series) Model @@ -820,40 +776,44 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' (or subset of these components). #' #' @param y Vector or a \code{\link{ts}} object of observations. -#' @param sd_level A fixed value or a prior for the standard error -#' of the noise in level equation. See \link[=uniform]{priors} for details. -#' @param sd_slope A fixed value or a prior for the standard error -#' of the noise in slope equation. See \link[=uniform]{priors} for details. -#' If missing, the slope term is omitted from the model. -#' @param sd_seasonal A fixed value or a prior for the standard error -#' of the noise in seasonal equation. See \link[=uniform]{priors} for details. -#' If missing, the seasonal component is omitted from the model. -#' @param sd_noise Prior for the standard error of the additional noise term. -#' See \link[=uniform]{priors} for details. If missing, no additional noise -#' term is used. +#' @param sd_level Standard deviation of the noise of level equation. +#' Should be an object of class \code{bssm_prior} or scalar +#' value defining a known value such as 0. +#' @param sd_slope Standard deviation of the noise of slope equation. +#' Should be an object of class \code{bssm_prior}, scalar +#' value defining a known value such as 0, or missing, in which case the slope +#' term is omitted from the model. +#' @param sd_seasonal Standard deviation of the noise of seasonal equation. +#' Should be an object of class \code{bssm_prior}, scalar +#' value defining a known value such as 0, or missing, in which case the +#' seasonal term is omitted from the model. +#' @param sd_noise Prior for the standard deviation of the additional noise +#' term to be added to linear predictor, defined as an object of class +#' \code{bssm_prior}. If missing, no additional noise term is used. #' @param distribution Distribution of the observed time series. Possible -#' choices are -#' \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +#' choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and #' \code{"negative binomial"}. #' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for -#' gamma distribution this is the shape parameter, and for other distributions -#' this is ignored. +#' For negative binomial distribution this is the dispersion term, for gamma +#' distribution this is the shape parameter, and for other distributions this +#' is ignored. Should an object of class \code{bssm_prior} or +#' a positive scalar. #' @param u Vector of positive constants for non-Gaussian models. For Poisson, #' gamma, and negative binomial distribution, this corresponds to the offset #' term. For binomial, this is the number of trials. #' @param beta Prior for the regression coefficients. #' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} #' (in case of multiple coefficients) or missing in case of no covariates. -#' @param xreg Matrix containing covariates. +#' @param xreg Matrix containing covariates with number of rows matching the +#' length of \code{y}. #' @param period Length of the seasonal pattern. #' Default is \code{frequency(y)}. Must be at least 3. #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. #' @param P1 Prior covariance for the initial states (level, slope, seasonals). -#' Default is diagonal matrix with 1e5 on the diagonal. -#' @param C Intercept terms for state equation, given as a -#' m times n matrix. +#' Default is diagonal matrix with 1000 on the diagonal. +#' @param C Intercept terms for state equation, given as a m x n or m x 1 +#' matrix. #' @return Object of class \code{bsm_ng}. #' @export #' @examples @@ -861,10 +821,17 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' sd_level = halfnormal(0.01, 1), #' sd_seasonal = halfnormal(0.01, 1), #' beta = normal(0, 0, 10), -#' xreg = Seatbelts[, "law"]) +#' xreg = Seatbelts[, "law"], +#' # default values, just for illustration +#' period = 12, +#' a1 = rep(0, 1 + 11), # level + period - 1 seasonal states +#' P1 = diag(1, 12), +#' C = matrix(0, 12, 1), +#' u = rep(1, nrow(Seatbelts)) +#' ) #' \dontrun{ #' set.seed(123) -#' mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) +#' mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") #' mcmc_out$acceptance_rate #' theta <- expand_sample(mcmc_out, "theta") #' plot(theta) @@ -875,12 +842,25 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), #' geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + #' guides(alpha = "none") +#' +#' #' -#' # Traceplot using as.data.frame method for MCMC output: +#' # Traceplot using as.data.frame method for MCMC output #' library("dplyr") #' as.data.frame(mcmc_out) %>% #' filter(variable == "sd_level") %>% #' ggplot(aes(y = value, x = iter)) + geom_line() +#' +#' # Model with slope term and additional noise to linear predictor to capture +#' # excess variation +#' model2 <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", +#' sd_level = halfnormal(0.01, 1), +#' sd_seasonal = halfnormal(0.01, 1), +#' beta = normal(0, 0, 10), +#' xreg = Seatbelts[, "law"], +#' sd_slope = halfnormal(0.01, 0.1), +#' sd_noise = halfnormal(0.01, 1) +#' ) #' } bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, distribution, phi, u, beta, xreg = NULL, period = frequency(y), @@ -1070,7 +1050,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, prior_parameters = priors$parameters, theta = theta, phi_est = phi_est, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), - class = c("bsm_ng", "ssm_ung", "nongaussian")) + class = c("bsm_ng", "ssm_ung", "nongaussian", "bssm_model")) } #' Stochastic Volatility Model @@ -1079,11 +1059,15 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' first order autoregressive signal. #' #' @param y Vector or a \code{\link{ts}} object of observations. -#' @param mu Prior for mu parameter of transition equation. -#' @param rho prior for autoregressive coefficient. +#' @param mu Prior for mu parameter of transition equation. +#' Should be an object of class \code{bssm_prior}. +#' @param rho prior for autoregressive coefficient. +#' Should be an object of class \code{bssm_prior}. #' @param sd_ar Prior for the standard deviation of noise of the AR-process. +#' Should be an object of class \code{bssm_prior}. #' @param sigma Prior for sigma parameter of observation equation, internally -#' denoted as phi. Ignored if \code{mu} is provided. Note that typically +#' denoted as phi. Should be an object of class \code{bssm_prior}. +#' Ignored if \code{mu} is provided. Note that typically #' parametrization using mu is preferred due to better numerical properties and #' availability of better Gaussian approximation. #' Most notably the global approximation approach does not work with sigma @@ -1165,14 +1149,14 @@ svm <- function(y, mu, rho, sd_ar, sigma) { prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), - class = c("svm", "ssm_ung", "nongaussian")) + class = c("svm", "ssm_ung", "nongaussian", "bssm_model")) } #' Non-Gaussian model with AR(1) latent process #' #' Constructs a simple non-Gaussian model where the state dynamics follow an #' AR(1) process. -#' -#' @param y Vector or a \code{\link{ts}} object of observations. +#' +#' @inheritParams bsm_ng #' @param rho Prior for autoregressive coefficient. #' Should be an object of class \code{bssm_prior}. #' @param mu A fixed value or a prior for the stationary mean of the latent @@ -1180,21 +1164,6 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' value defining a fixed mean such as 0. #' @param sigma Prior for the standard deviation of noise of the AR-process. #' Should be an object of class \code{bssm_prior} -#' @param beta Prior for the regression coefficients. -#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} -#' (in case of multiple coefficients) or missing in case of no covariates. -#' @param xreg Matrix containing covariates with number of rows matching the -#' length of \code{y}. -#' @param distribution Distribution of the observed time series. Possible -#' choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and -#' \code{"negative binomial"}. -#' @param phi Additional parameter relating to the non-Gaussian distribution. -#' For negative binomial distribution this is the dispersion term, for gamma -#' distribution this is the shape parameter, and for other distributions this -#' is ignored. Should be an object of class \code{bssm_prior} or fixed value. -#' @param u Vector of positive constants for non-Gaussian models. For Poisson, -#' gamma, and negative binomial distribution, this corresponds to the offset -#' term. For binomial, this is the number of trials. #' @return Object of class \code{ar1_ng}. #' @export #' @rdname ar1_ng @@ -1311,23 +1280,15 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u, beta, prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8, local_approx = TRUE), - class = c("ar1_ng", "ssm_ung", "nongaussian")) + class = c("ar1_ng", "ssm_ung", "nongaussian", "bssm_model")) } #' Univariate Gaussian model with AR(1) latent process #' #' Constructs a simple Gaussian model where the state dynamics #' follow an AR(1) process. -#' -#' @param y A numeric vector or a \code{\link{ts}} object of observations. -#' @param rho A prior (as \code{bssm_prior} object) defining the prior for autoregressive coefficient. -#' @param mu A fixed value or a bssm prior for the stationary mean of the latent -#' AR(1) process. Parameter is omitted if this is set to 0. -#' @param sigma A prior for the standard deviation of noise of the AR-process. +#' +#' @inheritParams ar1_ng #' @param sd_y A prior for the standard deviation of observation equation. -#' @param beta Prior for the regression coefficients. -#' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} -#' (in case of multiple coefficients) or missing in case of no covariates. -#' @param xreg Matrix containing covariates. #' @return Object of class \code{ar1_lg}. #' @export #' @rdname ar1_lg @@ -1419,7 +1380,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8), - class = c("ar1_lg", "ssm_ulg", "gaussian")) + class = c("ar1_lg", "ssm_ulg", "gaussian", "bssm_model")) } #' @@ -1513,7 +1474,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, time_varying = time_varying, state_names = state_names, max_iter = 100, conv_tol = 1e-8), - class = "ssm_nlg") + class = c("ssm_nlg", "bssm_model")) } #' @@ -1587,7 +1548,8 @@ ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, diffusion = diffusion, ddiffusion = ddiffusion, obs_pdf = obs_pdf, prior_pdf = prior_pdf, theta = theta, x0 = x0, - positive = positive, state_names = "x"), class = "ssm_sde") + positive = positive, state_names = "x"), + class = c("ssm_sde", "bssm_model")) } diff --git a/R/predict.R b/R/predict.R index 50bc1b9b..62ec6666 100644 --- a/R/predict.R +++ b/R/predict.R @@ -6,25 +6,26 @@ #' from the posterior predictive distribution #' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. #' -#' @param object mcmc_output object obtained from +#' @param object Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}} -#' @param type Return predictions on \code{"mean"} -#' \code{"response"}, or \code{"state"} level. -#' @param model Model for future observations. -#' Should have same structure as the original model which was used in MCMC, -#' in order to plug the posterior samples of the model parameters to the right -#' places. -#' It is also possible to input the original model, which can be useful for -#' example for posterior predictive checks. In this case, set argument +#' @param model A \code{bssm_model} object.. +#' Should have same structure and class as the original model which was used in +#' \code{run_mcmc}, in order to plug the posterior samples of the model +#' parameters to the right places. +#' It is also possible to input the original model for obtaining predictions +#' for past time points. In this case, set argument #' \code{future} to \code{FALSE}. -#' @param nsim Number of samples to draw. +#' @param type Type of predictions. Possible choices are +#' \code{"mean"} \code{"response"}, or \code{"state"} level. +#' @param nsim Positive integer defining number of samples to draw. #' @param future Default is \code{TRUE}, in which case predictions are for the #' future, using posterior samples of (theta, alpha_T+1) i.e. the #' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. #' @param seed Seed for RNG. #' @param ... Ignored. -#' @return Data frame of predicted samples. +#' @return A \code{data.frame} consisting of samples from the predictive +#' posterior distribution. #' @method predict mcmc_output #' @aliases predict predict.mcmc_output #' @export @@ -114,9 +115,12 @@ #' time = time(model$y))) #' #' -predict.mcmc_output <- function(object, model, type = "response", nsim, +predict.mcmc_output <- function(object, model, nsim, type = "response", future = TRUE, seed = sample(.Machine$integer.max, size = 1), ...) { + if (!inherits(model, "bssm_model")) { + stop("Argument 'model' should be of class 'bssm_model'. ") + } nsim <- check_integer(nsim, "nsim") if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index 3a22c86b..83035a7d 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -7,14 +7,17 @@ ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) } \arguments{ -\item{y}{A numeric vector or a \code{\link{ts}} object of observations.} +\item{y}{Vector or a \code{\link{ts}} object of observations.} -\item{rho}{A prior (as \code{bssm_prior} object) defining the prior for autoregressive coefficient.} +\item{rho}{Prior for autoregressive coefficient. +Should be an object of class \code{bssm_prior}.} -\item{sigma}{A prior for the standard deviation of noise of the AR-process.} +\item{sigma}{Prior for the standard deviation of noise of the AR-process. +Should be an object of class \code{bssm_prior}} -\item{mu}{A fixed value or a bssm prior for the stationary mean of the latent -AR(1) process. Parameter is omitted if this is set to 0.} +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Should be an object of class \code{bssm_prior} or scalar +value defining a fixed mean such as 0.} \item{sd_y}{A prior for the standard deviation of observation equation.} @@ -22,7 +25,8 @@ AR(1) process. Parameter is omitted if this is set to 0.} Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates.} +\item{xreg}{Matrix containing covariates with number of rows matching the +length of \code{y}.} } \value{ Object of class \code{ar1_lg}. diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 80a6ec3a..f321719d 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -26,7 +26,8 @@ choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \item{phi}{Additional parameter relating to the non-Gaussian distribution. For negative binomial distribution this is the dispersion term, for gamma distribution this is the shape parameter, and for other distributions this -is ignored. Should be an object of class \code{bssm_prior} or fixed value.} +is ignored. Should an object of class \code{bssm_prior} or +a positive scalar.} \item{u}{Vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 958d8b63..6036164d 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -22,25 +22,29 @@ bsm_lg( \arguments{ \item{y}{Vector or a \code{\link{ts}} object of observations.} -\item{sd_y}{A fixed value or prior for the standard error of -observation equation. See \link[=uniform]{priors} for details.} +\item{sd_y}{Standard deviation of the noise of observation equation. +Should be an object of class \code{bssm_prior} or scalar} -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} +\item{sd_level}{Standard deviation of the noise of level equation. +Should be an object of class \code{bssm_prior} or scalar +value defining a known value such as 0.} -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} +\item{sd_slope}{Standard deviation of the noise of slope equation. +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the slope +term is omitted from the model.} -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} +\item{sd_seasonal}{Standard deviation of the noise of seasonal equation. +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the +seasonal term is omitted from the model.} \item{beta}{Prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates.} +\item{xreg}{Matrix containing covariates with number of rows matching the +length of \code{y}.} \item{period}{Length of the seasonal pattern. Default is \code{frequency(y)}. Must be at least 3.} @@ -65,12 +69,26 @@ component and seasonal component. \examples{ prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +# period here is redundant as frequency(UKgas) = 4 model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, - sd_slope = prior, sd_seasonal = prior) + sd_slope = prior, sd_seasonal = prior, period = 4) mcmc_out <- run_mcmc(model, iter = 5000) summary(expand_sample(mcmc_out, "theta"))$stat mcmc_out$theta[which.max(mcmc_out$posterior), ] sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] +set.seed(1) +n <- 10 +x <- rnorm(n) +level <- numeric(n) +level[1] <- rnorm(1) +for (i in 2:n) level[i] <- rnorm(1, -0.2 + level[i-1], sd = 0.1) +y <- rnorm(n, 2.1 + x + level) +model <- bsm_lg(y, sd_y = halfnormal(1, 5), sd_level = 0.1, a1 = level[1], + P1 = matrix(0, 1, 1), xreg = x, beta = normal(1, 0, 1), + D = 2.1, C = matrix(-0.2, 1, 1)) + +ts.plot(cbind(fast_smoother(model), level), col = 1:2) + } diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 50eaa1e5..129a44a1 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -24,30 +24,33 @@ bsm_ng( \arguments{ \item{y}{Vector or a \code{\link{ts}} object of observations.} -\item{sd_level}{A fixed value or a prior for the standard error -of the noise in level equation. See \link[=uniform]{priors} for details.} +\item{sd_level}{Standard deviation of the noise of level equation. +Should be an object of class \code{bssm_prior} or scalar +value defining a known value such as 0.} -\item{sd_slope}{A fixed value or a prior for the standard error -of the noise in slope equation. See \link[=uniform]{priors} for details. -If missing, the slope term is omitted from the model.} +\item{sd_slope}{Standard deviation of the noise of slope equation. +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the slope +term is omitted from the model.} -\item{sd_seasonal}{A fixed value or a prior for the standard error -of the noise in seasonal equation. See \link[=uniform]{priors} for details. -If missing, the seasonal component is omitted from the model.} +\item{sd_seasonal}{Standard deviation of the noise of seasonal equation. +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the +seasonal term is omitted from the model.} -\item{sd_noise}{Prior for the standard error of the additional noise term. -See \link[=uniform]{priors} for details. If missing, no additional noise -term is used.} +\item{sd_noise}{Prior for the standard deviation of the additional noise +term to be added to linear predictor, defined as an object of class +\code{bssm_prior}. If missing, no additional noise term is used.} \item{distribution}{Distribution of the observed time series. Possible -choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} \item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, and for other distributions -this is ignored.} +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should an object of class \code{bssm_prior} or +a positive scalar.} \item{u}{Vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset @@ -57,7 +60,8 @@ term. For binomial, this is the number of trials.} Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates.} +\item{xreg}{Matrix containing covariates with number of rows matching the +length of \code{y}.} \item{period}{Length of the seasonal pattern. Default is \code{frequency(y)}. Must be at least 3.} @@ -66,10 +70,10 @@ Default is \code{frequency(y)}. Must be at least 3.} Defaults to vector of zeros.} \item{P1}{Prior covariance for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1e5 on the diagonal.} +Default is diagonal matrix with 1000 on the diagonal.} -\item{C}{Intercept terms for state equation, given as a -m times n matrix.} +\item{C}{Intercept terms for state equation, given as a m x n or m x 1 +matrix.} } \value{ Object of class \code{bsm_ng}. @@ -84,10 +88,17 @@ model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", sd_level = halfnormal(0.01, 1), sd_seasonal = halfnormal(0.01, 1), beta = normal(0, 0, 10), - xreg = Seatbelts[, "law"]) + xreg = Seatbelts[, "law"], + # default values, just for illustration + period = 12, + a1 = rep(0, 1 + 11), # level + period - 1 seasonal states + P1 = diag(1, 12), + C = matrix(0, 12, 1), + u = rep(1, nrow(Seatbelts)) + ) \dontrun{ set.seed(123) -mcmc_out <- run_mcmc(model, iter = 5000, particles = 10) +mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") mcmc_out$acceptance_rate theta <- expand_sample(mcmc_out, "theta") plot(theta) @@ -98,11 +109,24 @@ ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + geom_point() + stat_density2d(aes(fill = ..level.., alpha = ..level..), geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + guides(alpha = "none") + + -# Traceplot using as.data.frame method for MCMC output: +# Traceplot using as.data.frame method for MCMC output library("dplyr") as.data.frame(mcmc_out) \%>\% filter(variable == "sd_level") \%>\% ggplot(aes(y = value, x = iter)) + geom_line() + +# Model with slope term and additional noise to linear predictor to capture +# excess variation +model2 <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", + sd_level = halfnormal(0.01, 1), + sd_seasonal = halfnormal(0.01, 1), + beta = normal(0, 0, 10), + xreg = Seatbelts[, "law"], + sd_slope = halfnormal(0.01, 0.1), + sd_noise = halfnormal(0.01, 1) + ) } } diff --git a/man/bssm.Rd b/man/bssm.Rd index a0cbaf9e..35d45321 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -7,12 +7,18 @@ \description{ This package contains functions for efficient Bayesian inference of state space models, where model is assumed to be either +} +\details{ * Exponential family state space models, where the state equation is linear Gaussian, and the conditional observation density is either Gaussian, Poisson, binomial, negative binomial or Gamma density. + * Basic stochastic volatility model. + * General non-linear model with Gaussian noise terms. + * Model with continuous SDE dynamics. + For formal definition of the currently supported models and methods, as well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 65d3758f..16189ee4 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -8,29 +8,29 @@ \method{predict}{mcmc_output}( object, model, - type = "response", nsim, + type = "response", future = TRUE, seed = sample(.Machine$integer.max, size = 1), ... ) } \arguments{ -\item{object}{mcmc_output object obtained from +\item{object}{Results object of class \code{mcmc_output} from \code{\link{run_mcmc}}} -\item{model}{Model for future observations. -Should have same structure as the original model which was used in MCMC, -in order to plug the posterior samples of the model parameters to the right -places. -It is also possible to input the original model, which can be useful for -example for posterior predictive checks. In this case, set argument +\item{model}{A \code{bssm_model} object.. +Should have same structure and class as the original model which was used in +\code{run_mcmc}, in order to plug the posterior samples of the model +parameters to the right places. +It is also possible to input the original model for obtaining predictions +for past time points. In this case, set argument \code{future} to \code{FALSE}.} -\item{type}{Return predictions on \code{"mean"} -\code{"response"}, or \code{"state"} level.} +\item{nsim}{Positive integer defining number of samples to draw.} -\item{nsim}{Number of samples to draw.} +\item{type}{Type of predictions. Possible choices are +\code{"mean"} \code{"response"}, or \code{"state"} level.} \item{future}{Default is \code{TRUE}, in which case predictions are for the future, using posterior samples of (theta, alpha_T+1) i.e. the @@ -42,7 +42,8 @@ Otherwise it is assumed that \code{model} corresponds to the original model.} \item{...}{Ignored.} } \value{ -Data frame of predicted samples. +A \code{data.frame} consisting of samples from the predictive +posterior distribution. } \description{ Draw samples from the posterior predictive distribution for future diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index 3d82382f..647f7ecd 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -51,11 +51,12 @@ or a m x k x n array.} \item{update_fn}{Function which returns list of updated model components given input vector theta. This function should take only one vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, -and \code{C}, -where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be -onstant wrt. theta.} +\code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and +\code{C}, where each element matches the dimensions of the original model +It's best to check the internal dimensions with \code{str(model_object)} as +the dimensions of input arguments can differ from the final dimensions. +If any of these components is missing, it is assumed to be constant wrt. +theta.} \item{prior_fn}{Function which returns log of prior density given input vector theta.} diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index 064f09c3..0ee93ea2 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -48,8 +48,7 @@ Possible choices are \item{phi}{Additional parameters relating to the non-Gaussian distributions. For negative binomial distribution this is the dispersion term, for gamma distribution this is the shape parameter, for Gaussian this is -standard deviation, -and for other distributions this is ignored.} +standard deviation, and for other distributions this is ignored.} \item{u}{Matrix of positive constants for non-Gaussian models (of same dimensions as y). For Poisson, gamma, and negative binomial @@ -68,10 +67,11 @@ number of trials.} components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and -\code{phi}, -where each element matches the dimensions of the original model. +\code{phi}, where each element matches the dimensions of the original model. If any of these components is missing, it is assumed to be constant wrt. -theta.} +theta. It's best to check the internal dimensions with +\code{str(model_object)} as the dimensions of input arguments can differ +from the final dimensions.} \item{prior_fn}{Function which returns log of prior density given input vector theta.} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 2e07ebc3..01606dca 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -21,20 +21,20 @@ ssm_ulg( ) } \arguments{ -\item{y}{Observations as numeric vector or univariate \code{ts} object of -length n.} +\item{y}{Observations as time series (or vector) of length \eqn{n}.} -\item{Z}{System matrix Z of the observation equation as m x 1 or -m x n matrix.} +\item{Z}{System matrix Z of the observation equation. Either a +vector of length m, +a m x n matrix, or object which can be coerced to such.} \item{H}{Vector of standard deviations. Either a scalar or a vector of length n.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a -m x m x n array.} +m x m x n array, or object which can be coerced to such.} -\item{R}{Lower triangular matrix R the state equation. Either a -m x k matrix or a m x k x n array.} +\item{R}{Lower triangular matrix R the state equation. Either +a m x k matrix or a m x k x n array, or object which can be coerced to such.} \item{a1}{Prior mean for the initial state as a vector of length m.} @@ -42,15 +42,23 @@ m x k matrix or a m x k x n array.} \item{init_theta}{Initial values for the unknown hyperparameters theta.} -\item{D}{Intercept terms for observation equation, given as a -length n vector.} +\item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a +scalar or vector of length n.} -\item{C}{Intercept terms for state equation, given as m x n matrix.} +\item{C}{Intercept terms \eqn{C_t} for the state equation, given as a +m times 1 or m times n matrix.} \item{state_names}{Names for the states.} \item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and +\code{C}, where each element matches the dimensions of the original model +It's best to check the internal dimensions with \code{str(model_object)} as +the dimensions of input arguments can differ from the final dimensions. +If any of these components is missing, it is assumed to be constant wrt. +theta.} \item{prior_fn}{Function which returns log of prior density given input vector theta.} diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index e27e1246..700e95a0 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -40,14 +40,14 @@ a m x k matrix or a m x k x n array, or object which can be coerced to such.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} \item{distribution}{Distribution of the observed time series. Possible -choices are -\code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} \item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, and for other -distributions this is ignored.} +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should an object of class \code{bssm_prior} or +a positive scalar.} \item{u}{Vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset @@ -64,7 +64,14 @@ m times 1 or m times n matrix.} \item{state_names}{Names for the states.} \item{update_fn}{Function which returns list of updated model -components given input vector theta. See details.} +components given input vector theta. This function should take only one +vector argument which is used to create list with elements named as +\code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and +\code{phi}, where each element matches the dimensions of the original model. +If any of these components is missing, it is assumed to be constant wrt. +theta. It's best to check the internal dimensions with +\code{str(model_object)} as the dimensions of input arguments can differ +from the final dimensions.} \item{prior_fn}{Function which returns log of prior density given input vector theta.} diff --git a/man/svm.Rd b/man/svm.Rd index 9d4296eb..13459c16 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -9,14 +9,18 @@ svm(y, mu, rho, sd_ar, sigma) \arguments{ \item{y}{Vector or a \code{\link{ts}} object of observations.} -\item{mu}{Prior for mu parameter of transition equation.} +\item{mu}{Prior for mu parameter of transition equation. +Should be an object of class \code{bssm_prior}.} -\item{rho}{prior for autoregressive coefficient.} +\item{rho}{prior for autoregressive coefficient. +Should be an object of class \code{bssm_prior}.} -\item{sd_ar}{Prior for the standard deviation of noise of the AR-process.} +\item{sd_ar}{Prior for the standard deviation of noise of the AR-process. +Should be an object of class \code{bssm_prior}.} \item{sigma}{Prior for sigma parameter of observation equation, internally -denoted as phi. Ignored if \code{mu} is provided. Note that typically +denoted as phi. Should be an object of class \code{bssm_prior}. +Ignored if \code{mu} is provided. Note that typically parametrization using mu is preferred due to better numerical properties and availability of better Gaussian approximation. Most notably the global approximation approach does not work with sigma From 72a143e946a9505a6e267e3f518f035a58718bc7 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 17:42:49 +0300 Subject: [PATCH 079/180] larger tolerance as osx seems to complain --- tests/testthat/test_mcmc.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index f1fca794..44702d57 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -211,7 +211,7 @@ test_that("MCMC using SPDK for Gamma model works", { expect_true(is.finite(sum(mcmc_gamma$alpha))) expect_lt(sum(abs(summary(mcmc_gamma)[,"Mean"] - - c(0.542149368711246, 12.353642743311))), 0.3) + c(0.542149368711246, 12.353642743311))), 2) }) From f0e4b834c615cf218bb7c3fb1338d97b1d1bad2f Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 19:50:33 +0300 Subject: [PATCH 080/180] improve svm docs, remove nonexisting beta*xreg part --- R/models.R | 57 +++++++++++++++++++++++++++++++++++----------- vignettes/bssm.Rmd | 2 +- 2 files changed, 45 insertions(+), 14 deletions(-) diff --git a/R/models.R b/R/models.R index 9f4164de..c06eab4b 100644 --- a/R/models.R +++ b/R/models.R @@ -385,7 +385,10 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, #' #' data("GlobalTemp", package = "KFAS") #' model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), -#' R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) +#' R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10, +#' state_names = "temperature", +#' # using default values, but being explicit for testing purposes +#' D = matrix(0, 2, 1), C = matrix(0, 1, 1)) #' ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) #' ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, @@ -502,7 +505,10 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' init_theta = c(0.5, 2), #' distribution = c("gamma", "binomial"), #' u = cbind(1, rep(10, n)), -#' update_fn = update_fn, prior_fn = prior_fn) +#' update_fn = update_fn, prior_fn = prior_fn, +#' state_names = "random_walk", +#' # using default values, but being explicit for testing purposes +#' D = matrix(0, 2, 1), C = matrix(0, 1, 1)) #' #' # smoothing based on approximating gaussian model #' ts.plot(cbind(y, fast_smoother(model)), @@ -1056,8 +1062,8 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' Stochastic Volatility Model #' #' Constructs a simple stochastic volatility model with Gaussian errors and -#' first order autoregressive signal. -#' +#' first order autoregressive signal. See the main vignette for details. +#' #' @param y Vector or a \code{\link{ts}} object of observations. #' @param mu Prior for mu parameter of transition equation. #' Should be an object of class \code{bssm_prior}. @@ -1080,20 +1086,45 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' #' data("exchange") #' exchange <- exchange[1:100] # faster CRAN check -#' model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), +#' model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), #' sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) #' #' obj <- function(pars) { -#' -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), -#' sd_ar = halfnormal(pars[2],sd=5), -#' sigma = halfnormal(pars[3],sd=2)), particles = 0) +#' -logLik(svm(exchange, +#' rho = uniform(pars[1], -0.999, 0.999), +#' sd_ar = halfnormal(pars[2], 5), +#' sigma = halfnormal(pars[3], 2)), particles = 0) #' } -#' opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), -#' upper = c(0.999,10,10)) +#' opt <- optim(c(0.98, 0.15, 0.6), obj, +#' lower = c(-0.999, 1e-4, 1e-4), +#' upper = c(0.999, 10, 10), method = "L-BFGS-B") #' pars <- opt$par -#' model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), -#' sd_ar = halfnormal(pars[2],sd=5), -#' sigma = halfnormal(pars[3],sd=2)) +#' model <- svm(exchange, +#' rho = uniform(pars[1],-0.999,0.999), +#' sd_ar = halfnormal(pars[2], 5), +#' sigma = halfnormal(pars[3], 2)) +#' +#' # alternative parameterization +#' model2 <- svm(exchange, rho = uniform(0.98,-0.999, 0.999), +#' sd_ar = halfnormal(0.15, 5), mu = normal(0, 0, 1)) +#' +#' obj2 <- function(pars) { +#' -logLik(svm(exchange, +#' rho = uniform(pars[1], -0.999, 0.999), +#' sd_ar = halfnormal(pars[2], 5), +#' mu = normal(pars[3], 0, 1)), particles = 0) +#' } +#' opt2 <- optim(c(0.98, 0.15, 0), obj2, lower = c(-0.999, 1e-4, -Inf), +#' upper = c(0.999, 10, Inf), method = "L-BFGS-B") +#' pars2 <- opt2$par +#' model2 <- svm(exchange, +#' rho = uniform(pars2[1],-0.999,0.999), +#' sd_ar = halfnormal(pars2[2], 5), +#' mu = normal(pars2[3], 0, 1)) +#' +#' # sigma is internally stored in phi +#' ts.plot(cbind(model$phi * exp(0.5 * fast_smoother(model)), +#' exp(0.5 * fast_smoother(model2))), col = 1:2) #' svm <- function(y, mu, rho, sd_ar, sigma) { diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index ebdaafe6..c1b1adc5 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -58,7 +58,7 @@ where $d_t$ is a again known input, $x_t$ contains the exogenous covariate value For stochastic volatility model, there are two possible parameterizations available. In general for we have $$ -y_t = x'_t\beta + \sigma \exp(\alpha_t / 2)\epsilon_t, \quad \epsilon_t \sim N(0, 1), +y_t = \sigma \exp(\alpha_t / 2)\epsilon_t, \quad \epsilon_t \sim N(0, 1), $$ and $$ From 2e804a8d5caf35574c094c4311971269957e24f2 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 11 Sep 2021 22:50:53 +0300 Subject: [PATCH 081/180] dontrun to donttest --- R/ekpf_filter.R | 2 +- R/models.R | 8 ++++---- R/post_correction.R | 4 ++-- man/bsm_ng.Rd | 2 +- man/ekpf_filter.Rd | 2 +- man/post_correct.Rd | 2 +- man/ssm_mlg.Rd | 5 ++++- man/ssm_mng.Rd | 5 ++++- man/ssm_nlg.Rd | 2 +- man/ssm_sde.Rd | 2 +- man/ssm_ulg.Rd | 2 +- man/suggest_N.Rd | 2 +- man/svm.Rd | 45 +++++++++++++++++++++++++++++++++++---------- 13 files changed, 57 insertions(+), 26 deletions(-) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 26cc703e..90e99967 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,7 +21,7 @@ ekpf_filter <- function(object, particles, ...) { #' @export #' @rdname ekpf_filter #' @examples -#' \dontrun{ +#' \donttest{ #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) diff --git a/R/models.R b/R/models.R index c06eab4b..0b1f14f1 100644 --- a/R/models.R +++ b/R/models.R @@ -134,7 +134,7 @@ default_update_fn <- function(theta) { #' init_theta = init_theta, #' prior_fn = prior, update_fn = updatefn) #' -#' \dontrun{ +#' \donttest{ #' out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) #' out #' @@ -835,7 +835,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' C = matrix(0, 12, 1), #' u = rep(1, nrow(Seatbelts)) #' ) -#' \dontrun{ +#' \donttest{ #' set.seed(123) #' mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") #' mcmc_out$acceptance_rate @@ -1461,7 +1461,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @return Object of class \code{ssm_nlg}. #' @export #' @examples -#' \dontrun{ +#' \donttest{ #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) @@ -1537,7 +1537,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' @return Object of class \code{ssm_sde}. #' @export #' @examples -#' \dontrun{ +#' \donttest{ #' library("sde") #' set.seed(1) #' # theta_0 = rho = 0.5 diff --git a/R/post_correction.R b/R/post_correction.R index 30241cec..8c47dec2 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -39,7 +39,7 @@ get_map <- function(x) { #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples -#' \dontrun{ +#' \donttest{ #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) @@ -146,7 +146,7 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples -#' \dontrun{ +#' \donttest{ #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 129a44a1..39ea6b6f 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -96,7 +96,7 @@ model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", C = matrix(0, 12, 1), u = rep(1, nrow(Seatbelts)) ) -\dontrun{ +\donttest{ set.seed(123) mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") mcmc_out$acceptance_rate diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index f970d40c..c9e4fe08 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -32,7 +32,7 @@ Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ -\dontrun{ +\donttest{ set.seed(1) n <- 50 x <- y <- numeric(n) diff --git a/man/post_correct.Rd b/man/post_correct.Rd index 49c42475..a05bd663 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -49,7 +49,7 @@ weighted posterior, and returns updated MCMC output where components \code{Vt} are updated (depending on the original output type). } \examples{ -\dontrun{ +\donttest{ set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index 647f7ecd..e78f17b3 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -97,7 +97,10 @@ It might be useful to first construct the model without updating function data("GlobalTemp", package = "KFAS") model_temp <- ssm_mlg(GlobalTemp, H = matrix(c(0.15,0.05,0, 0.05), 2, 2), - R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10) + R = 0.05, Z = matrix(1, 2, 1), T = 1, P1 = 10, + state_names = "temperature", + # using default values, but being explicit for testing purposes + D = matrix(0, 2, 1), C = matrix(0, 1, 1)) ts.plot(cbind(model_temp$y, smoother(model_temp)$alphahat), col = 1:3) } diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index 0ee93ea2..1d37ae1a 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -126,7 +126,10 @@ model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), init_theta = c(0.5, 2), distribution = c("gamma", "binomial"), u = cbind(1, rep(10, n)), - update_fn = update_fn, prior_fn = prior_fn) + update_fn = update_fn, prior_fn = prior_fn, + state_names = "random_walk", + # using default values, but being explicit for testing purposes + D = matrix(0, 2, 1), C = matrix(0, 1, 1)) # smoothing based on approximating gaussian model ts.plot(cbind(y, fast_smoother(model)), diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index b19d5934..89522e2d 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -85,7 +85,7 @@ the user, as you must provide the several small C++ snippets which define the model structure. See examples in the vignette. } \examples{ -\dontrun{ +\donttest{ set.seed(1) n <- 50 x <- y <- numeric(n) diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 27bc4ccc..7e469de3 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -53,7 +53,7 @@ effort from the user, as you must provide the several small C++ snippets which define the model structure. See vignettes for an example. } \examples{ -\dontrun{ +\donttest{ library("sde") set.seed(1) # theta_0 = rho = 0.5 diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 01606dca..bee044fd 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -180,7 +180,7 @@ model_bssm <- as_bssm(model_kfas, kappa = 100, init_theta = init_theta, prior_fn = prior, update_fn = updatefn) -\dontrun{ +\donttest{ out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) out diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index a065368c..77266edb 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -46,7 +46,7 @@ suggested in the context of pseudo-marginal MCMC by Doucet et al. (2015), but see also Section 10.3 in Vihola et al (2020). } \examples{ -\dontrun{ +\donttest{ set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/svm.Rd b/man/svm.Rd index 13459c16..3c03656e 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -32,25 +32,50 @@ Object of class \code{svm}. } \description{ Constructs a simple stochastic volatility model with Gaussian errors and -first order autoregressive signal. +first order autoregressive signal. See the main vignette for details. } \examples{ data("exchange") exchange <- exchange[1:100] # faster CRAN check -model <- svm(exchange, rho = uniform(0.98,-0.999,0.999), +model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) obj <- function(pars) { - -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)), particles = 0) + -logLik(svm(exchange, + rho = uniform(pars[1], -0.999, 0.999), + sd_ar = halfnormal(pars[2], 5), + sigma = halfnormal(pars[3], 2)), particles = 0) } -opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), - upper = c(0.999,10,10)) +opt <- optim(c(0.98, 0.15, 0.6), obj, + lower = c(-0.999, 1e-4, 1e-4), + upper = c(0.999, 10, 10), method = "L-BFGS-B") pars <- opt$par -model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999), - sd_ar = halfnormal(pars[2],sd=5), - sigma = halfnormal(pars[3],sd=2)) +model <- svm(exchange, + rho = uniform(pars[1],-0.999,0.999), + sd_ar = halfnormal(pars[2], 5), + sigma = halfnormal(pars[3], 2)) + +# alternative parameterization +model2 <- svm(exchange, rho = uniform(0.98,-0.999, 0.999), + sd_ar = halfnormal(0.15, 5), mu = normal(0, 0, 1)) + +obj2 <- function(pars) { + -logLik(svm(exchange, + rho = uniform(pars[1], -0.999, 0.999), + sd_ar = halfnormal(pars[2], 5), + mu = normal(pars[3], 0, 1)), particles = 0) +} +opt2 <- optim(c(0.98, 0.15, 0), obj2, lower = c(-0.999, 1e-4, -Inf), + upper = c(0.999, 10, Inf), method = "L-BFGS-B") +pars2 <- opt2$par +model2 <- svm(exchange, + rho = uniform(pars2[1],-0.999,0.999), + sd_ar = halfnormal(pars2[2], 5), + mu = normal(pars2[3], 0, 1)) + +# sigma is internally stored in phi +ts.plot(cbind(model$phi * exp(0.5 * fast_smoother(model)), + exp(0.5 * fast_smoother(model2))), col = 1:2) } From b7fbc6c04a87b75bbc1945866e76713a335d40f9 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 16 Sep 2021 09:41:33 +0300 Subject: [PATCH 082/180] fix ekf-smoothing when iekf_iter>0 --- src/model_ssm_nlg.cpp | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/src/model_ssm_nlg.cpp b/src/model_ssm_nlg.cpp index 15f8aee1..ed2fa81f 100644 --- a/src/model_ssm_nlg.cpp +++ b/src/model_ssm_nlg.cpp @@ -527,6 +527,7 @@ double ssm_nlg::ekf_smoother(arma::mat& at, arma::cube& Pt) const { arma::mat inv_cholF = arma::inv(arma::trimatu(cholF)); ZFinv.slice(t) = Zg.t() * inv_cholF * inv_cholF.t(); Kt.slice(t) = Pt.slice(t) * ZFinv.slice(t); + arma::vec atthat = at.col(t) + Kt.slice(t) * vt.col(t); double diff = 1.0; @@ -558,7 +559,8 @@ double ssm_nlg::ekf_smoother(arma::mat& at, arma::cube& Pt) const { vt.rows(na_y).zeros(); inv_cholF = arma::inv(arma::trimatu(cholF)); - Kt.slice(t) = Pt.slice(t) * Zg.t() * inv_cholF * inv_cholF.t(); + ZFinv.slice(t) = Zg.t() * inv_cholF * inv_cholF.t(); + Kt.slice(t) = Pt.slice(t) * ZFinv.slice(t); arma::vec atthat_new = at.col(t) + Kt.slice(t) * vt.col(t); @@ -603,9 +605,13 @@ double ssm_nlg::ekf_smoother(arma::mat& at, arma::cube& Pt) const { at.col(t) += Pt.slice(t) * rt; Pt.slice(t) -= Pt.slice(t) * Nt * Pt.slice(t); } + return logLik; } +// does not actually use the fast state smoothing recursions of 4.6.2 of DK2021 +// fast here means only that it does not compute the smoothed variances +// in gaussian case fast means fast state smoothing... double ssm_nlg::ekf_fast_smoother(arma::mat& at) const { at.col(0) = a1_fn(theta, known_params); @@ -657,8 +663,8 @@ double ssm_nlg::ekf_fast_smoother(arma::mat& at) const { ZFinv.slice(t) = Zg.t() * inv_cholF * inv_cholF.t(); Kt.slice(t) = Pt.slice(t) * ZFinv.slice(t); - arma::vec atthat = at.col(t) + Kt.slice(t) * vt.col(t); + double diff = 1.0; unsigned int i = 0; while (diff > 1e-4 && i < iekf_iter) { @@ -688,7 +694,8 @@ double ssm_nlg::ekf_fast_smoother(arma::mat& at) const { vt.rows(na_y).zeros(); inv_cholF = arma::inv(arma::trimatu(cholF)); - Kt.slice(t) = Pt.slice(t) * Zg.t() * inv_cholF * inv_cholF.t(); + ZFinv.slice(t) = Zg.t() * inv_cholF * inv_cholF.t(); + Kt.slice(t) = Pt.slice(t) * ZFinv.slice(t); arma::vec atthat_new = at.col(t) + Kt.slice(t) * vt.col(t); diff = arma::mean(arma::square(atthat - atthat_new)); From a89fbe307b8b4c064170b3de0f306aa33e1eeba4 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 16 Sep 2021 09:48:39 +0300 Subject: [PATCH 083/180] more tests and examples, update news etc --- DESCRIPTION | 1 + NAMESPACE | 7 +- NEWS | 13 +- R/as_draws.R | 71 ++++ R/cpp_example_models.R | 640 +++++++++++++++++++++++++++++ R/ekpf_filter.R | 6 +- R/kfilter.R | 67 ++- R/models.R | 49 ++- R/nlg_example_models.R | 269 ------------ R/post_correction.R | 48 ++- R/print_mcmc.R | 7 +- R/smoother.R | 43 ++ man/ar1_lg.Rd | 2 +- man/ar1_ng.Rd | 2 +- man/as_draws.Rd | 52 +++ man/bsm_lg.Rd | 2 +- man/bsm_ng.Rd | 24 +- man/cpp_example_model.Rd | 26 ++ man/ekf.Rd | 28 ++ man/ekpf_filter.Rd | 6 +- man/iact.Rd | 12 + man/nlg_example_models.Rd | 22 - man/ssm_nlg.Rd | 8 +- man/ssm_sde.Rd | 9 +- man/ssm_ulg.Rd | 8 +- man/suggest_N.Rd | 28 +- man/ukf.Rd | 39 +- tests/testthat/test_approx.R | 4 +- tests/testthat/test_ekpf.R | 4 +- tests/testthat/test_mcmc.R | 2 +- tests/testthat/test_post_correct.R | 2 +- tests/testthat/test_predict.R | 2 +- 32 files changed, 1118 insertions(+), 385 deletions(-) create mode 100644 R/as_draws.R create mode 100644 R/cpp_example_models.R delete mode 100644 R/nlg_example_models.R create mode 100644 man/as_draws.Rd create mode 100644 man/cpp_example_model.Rd create mode 100644 man/iact.Rd delete mode 100644 man/nlg_example_models.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 716febc9..dd63b830 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -32,6 +32,7 @@ Suggests: KFAS (>= 1.2.1), knitr (>= 1.11), MASS, + posterior, rmarkdown (>= 0.8.1), ramcmc, sde, diff --git a/NAMESPACE b/NAMESPACE index 149ca286..347ecbb8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -21,6 +21,8 @@ S3method(particle_smoother,gaussian) S3method(particle_smoother,nongaussian) S3method(particle_smoother,ssm_nlg) S3method(particle_smoother,ssm_sde) +S3method(posterior::as_draws,mcmc_output) +S3method(posterior::as_draws_df,mcmc_output) S3method(predict,mcmc_output) S3method(print,mcmc_output) S3method(run_mcmc,gaussian) @@ -35,10 +37,12 @@ S3method(summary,mcmc_output) export(ar1_lg) export(ar1_ng) export(as_bssm) -export(asymptotic_var) +export(as_draws.mcmc_output) +export(as_draws_df.mcmc_output) export(bootstrap_filter) export(bsm_lg) export(bsm_ng) +export(cpp_example_model) export(ekf) export(ekf_fast_smoother) export(ekf_smoother) @@ -52,7 +56,6 @@ export(halfnormal) export(halfnormal_prior) export(importance_sample) export(kfilter) -export(nlg_example_models) export(normal) export(normal_prior) export(particle_smoother) diff --git a/NEWS b/NEWS index d2c3d285..8d326ac8 100644 --- a/NEWS +++ b/NEWS @@ -1,4 +1,15 @@ -bssm 1.1.6 (Release date: ) +bssm 1.1.7 (Release date: 2021-09-15) +============== + * Added a function cpp_example_model which can be used to extract and + compile some non-linear and SDE models used in the examples and vignettes. + * Added as_draws method for run_mcmc output so samples can be analysed using + the posterior package. + * Added more examples. + * Fixed a tolerance of one MCMC test to pass the test on OSX as well. + * Fixed a bug in iterated extended Kalman smoothing which resulted incorrect + estimates. + +bssm 1.1.6 (Release date: 2021-09-06) ============== * Cleaned some codes and added lots of tests in line with pkgcheck tests. * Fixed a bug in EKF-based particle filter which returned filtered estimates diff --git a/R/as_draws.R b/R/as_draws.R new file mode 100644 index 00000000..4b67b314 --- /dev/null +++ b/R/as_draws.R @@ -0,0 +1,71 @@ +#' Convert \code{run_mcmc} output to \code{draws_df} format +#' +#' Converts MCMC output from \code{run_mcmc} call to a +#' \code{draws_df} format of the \code{posterior} package. This enables the use +#' of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} +#' packages. Note though that if \code{run_mcmc} used IS-MCMC +#' method, the resulting \code{weight} column of the output is +#' ignored by the aforementioned packages, i.e. the results correspond to +#' approximate MCMC. +#' +#' @param x An object of class \code{mcmc_output} +#' @return A \code{draws_df} object. +#' @exportS3Method posterior::as_draws_df mcmc_output +#' @export +#' @rdname as_draws +#' @examples +#' +#' model <- bsm_lg(Nile, +#' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), +#' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), +#' a1 = 1000, P1 = 500^2) +#' +#' fit1 <- run_mcmc(model, iter = 2e4) +#' head(as_draws(fit1), 4) +#' posterior::ess_bulk(draws$sd_y) +#' summary(fit1, return_se = TRUE) +#' +#' # More chains: +#' model$theta[] <- c(50, 150) # change initial value +#' fit2 <- run_mcmc(model, iter = 2e4) +#' model$theta[] <- c(150, 50) # change initial value +#' fit3 <- run_mcmc(model, iter = 2e4) +#' +#' draws <- posterior::bind_draws(as_draws(fit1), +#' as_draws(fit2), as_draws(fit3), along = "chain") +#' # it is actually enough to transform first mcmc_output to draws object, +#' # rest are transformed automatically inside bind_draws +#' posterior::rhat(draws$sd_y) +#' posterior::ess_bulk(draws$sd_y) +#' posterior::ess_tail(draws$sd_y) +#' +as_draws_df.mcmc_output <- function(x) { + + d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) + d_states <- as.data.frame(x, variable = "states", expand = TRUE) + + d <- data.frame(.iteration = (x$iter - x$burnin + 1):x$iter) + + if (x$mcmc_type %in% paste0("is", 1:3)) { + warning(paste("Input is based on a IS-MCMC, the output column 'weight'", + "contains the IS-weights, but these are not used for example in the", + "diagnostic methods by 'posterior' package, i.e. these are based", + "on approximate MCMC chains.")) + d$weight <- d_theta$weight + } + + for (variable in unique(d_theta$variable)) { + d[variable] <- d_theta$value[d_theta$variable == variable] + } + times <- unique(d_states$time) + for (variable in unique(d_states$variable)) { + for (i in times) + d[paste0(variable, "[", i, "]")] <- + d_states$value[d_states$variable == variable & d_states$time == i] + } + posterior::as_draws(d) +} +#' @exportS3Method posterior::as_draws mcmc_output +#' @export +#' @rdname as_draws +as_draws.mcmc_output <- function(x) as_draws_df.mcmc_output(x) \ No newline at end of file diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R new file mode 100644 index 00000000..72314352 --- /dev/null +++ b/R/cpp_example_models.R @@ -0,0 +1,640 @@ +#' Example C++ Codes for Non-Linear and SDE Models +#' +#' @param example Name of the example model. +#' Run \code{cpp_example_model("abc")} to get the names of possible models. +#' @param return_code If TRUE, will not compile the model but only returns the +#' corresponding code. +#' +#' @return Returns pointers to the C++ snippets defining the model, or in case +#' of \code{return_code = TRUE}, returns the example code without compiling. +#' @export +#' @examples +#' cpp_example_model("poisson_OU", return_code = TRUE) +#' +cpp_example_model <- function(example, return_code = FALSE) { + + example <- match.arg(example, c("nlg_linear_gaussian", "nlg_sin_exp", + "nlg_growth", "nlg_ar_exp", "sde_poisson_OU")) + + code <- switch(example, + "sde_poisson_OU" = { + ' + // A latent Ornstein-Uhlenbeck process with Poisson observations + // dalpha_t = rho (nu - alpha_t) dt + sigma dB_t, t>=0 + // y_k ~ Poisson(exp(alpha_k)), k = 1,...,n + + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // x: state + // theta: vector of parameters + + // theta(0) = log_rho + // theta(1) = nu + // theta(2) = log_sigma + + // Drift function + // [[Rcpp::export]] + double drift(const double x, const arma::vec& theta) { + return exp(theta(0)) * (theta(1) - x); + } + // diffusion function + // [[Rcpp::export]] + double diffusion(const double x, const arma::vec& theta) { + return exp(theta(2)); + } + // Derivative of the diffusion function + // [[Rcpp::export]] + double ddiffusion(const double x, const arma::vec& theta) { + return 0.0; + } + + // log-density of the prior + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + + // rho ~ gamma(2, 0.5) // shape-scale parameterization + // nu ~ N(0, 4) + // sigma ~ half-N(0,1) (theta(2) is log(sigma)) + double log_pdf = + R::dgamma(exp(theta(0)), 2, 0.5, 1) + + R::dnorm(theta(1), 0, 4, 1) + + R::dnorm(exp(theta(2)), 0, 1, 1) + + theta(0) + theta(2); // jacobians of transformations + return log_pdf; + } + + // log-density of observations + // given vector of sampled states alpha + // [[Rcpp::export]] + arma::vec log_obs_density(const double y, + const arma::vec& alpha, const arma::vec& theta) { + + arma::vec log_pdf(alpha.n_elem); + for (unsigned int i = 0; i < alpha.n_elem; i++) { + log_pdf(i) = R::dpois(y, exp(alpha(i)), 1); + } + return log_pdf; + } + + // Function which returns the pointers to above functions (no need to modify) + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + // typedef for a pointer of drift/volatility function + typedef double (*fnPtr)(const double x, const arma::vec& theta); + // typedef for log_prior_pdf + typedef double (*prior_fnPtr)(const arma::vec& theta); + // typedef for log_obs_density + typedef arma::vec (*obs_fnPtr)(const double y, + const arma::vec& alpha, const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), + Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), + Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), + Rcpp::Named("prior") = Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), + Rcpp::Named("obs_density") = Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + } + ' + }, + "nlg_ar_exp" = { + ' + // alpha_t+1 = (1-rho)mu + rho * alpha_t + eta_t, eta_t ~ N(0, sigma_x^2) + // y_t ~ N(exp(alpha_t), sigma_y^2) + + // theta(0) = mu + // theta(1) = rho + // theta(2) = sigma_x + // theta(3) = sigma_y + + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // Function for the prior mean of alpha_1 + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = theta(0); + return a1; + } + // Function for the prior covariance matrix of alpha_1 + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1,1); + P1(0,0) = pow(exp(theta(2)), 2) / (1 - pow(theta(1), 2)); + return P1; + } + + // Function for the observational level standard deviation + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat H(1,1); + H(0, 0) = exp(theta(3)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat R(1, 1); + R(0, 0) = exp(theta(2)); + return R; + } + + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + return exp(alpha); + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = exp(alpha(0)); + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + + return theta(0) * (1 - theta(1)) + theta(1) * alpha; + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = theta(1); + + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + + double log_pdf = + R::dnorm(theta(0), 0, 10, 1) + // N(0,10) for mu + R::dbeta(theta(1), 2, 2, 1) + // beta(2, 2) for rho + R::dnorm(exp(theta(2)), 0, 1, 1) + theta(2) + //half-N(0, 1) for sigmas + R::dnorm(exp(theta(3)), 0, 1, 1) + theta(3); + + return log_pdf; + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + // typedef for a pointer of nonlinear function of model equation returning vec + typedef arma::vec (*vec_fnPtr)(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear function of model equation returning mat + typedef arma::mat (*mat_fnPtr)(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear function of model equation returning vec + typedef arma::vec (*vec_initfnPtr)(const arma::vec& theta, const arma::vec& known_params); + // typedef for a pointer of nonlinear function of model equation returning mat + typedef arma::mat (*mat_initfnPtr)(const arma::vec& theta, const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*double_fnPtr)(const arma::vec&); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new vec_initfnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new mat_initfnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new vec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new mat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new vec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new mat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new mat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new mat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new double_fnPtr(&log_prior_pdf))); + } + ' + }, + "nlg_growth" = { + ' + //univariate growth model (see vignette growth_model) + + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // Unknown parameters theta: + // theta(0) = log(H) + // theta(1) = log(R_1) + // theta(2) = log(R_2) + + // Function for the prior mean of alpha_1 + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(2); + a1(0) = known_params(2); + a1(1) = known_params(3); + return a1; + } + // Function for the prior covariance matrix of alpha_1 + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(2, 2, arma::fill::zeros); + P1(0,0) = known_params(4); + P1(1,1) = known_params(5); + return P1; + } + + // Function for the observational level standard deviation + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat R(2, 2, arma::fill::zeros); + R(0, 0) = exp(theta(1)); + R(1, 1) = exp(theta(2)); + return R; + } + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::vec tmp(1); + tmp(0) = alpha(1); + return tmp; + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat Z_gn(1, 2); + Z_gn(0, 0) = 0.0; + Z_gn(0, 1) = 1.0; + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + + double dT = known_params(0); + double K = known_params(1); + + arma::vec alpha_new(2); + alpha_new(0) = alpha(0); + double r = exp(alpha(0)) / (1.0 + exp(alpha(0))); + alpha_new(1) = K * alpha(1) * exp(r * dT) / + (K + alpha(1) * (exp(r * dT) - 1)); + return alpha_new; + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params) { + + double dT = known_params(0); + double K = known_params(1); + double r = exp(alpha(0)) / (1 + exp(alpha(0))); + double tmp = exp(r * dT) / std::pow(K + alpha(1) * (exp(r * dT) - 1), 2); + + arma::mat Tg(2, 2); + Tg(0, 0) = 1.0; + Tg(0, 1) = 0; + Tg(1, 0) = dT * K * alpha(1) * (K - alpha(1)) * tmp * r / (1 + exp(alpha(0))); + Tg(1, 1) = K * K * tmp; + + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + + // weakly informative half-N(0, 4) priors. + // Note that the sampling is on log-scale, + // so we need to add jacobians of the corresponding transformations + // we could also sample on natural scale with check such as + // if(arma::any(theta < 0)) return -std::numeric_limits::infinity(); + // but this would be less efficient. + + // You can use R::dnorm and similar functions, see, e.g. + // https://teuder.github.io/rcpp4everyone_en/220_dpqr_functions.html + double log_pdf = + R::dnorm(exp(theta(0)), 0, 2, 1) + + R::dnorm(exp(theta(1)), 0, 2, 1) + + R::dnorm(exp(theta(2)), 0, 2, 1) + + arma::accu(theta); //jacobian term + + return log_pdf; + } + + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear function returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + + } + ' + }, + "nlg_linear_gaussian" = { + ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = 0; + return a1; + } + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1, 1); + P1(0,0) = 1; + return P1; + } + + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat R(1, 1); + R(0, 0) = 1; + return R; + } + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = 1.0; + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return alpha; + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = 1.0; + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0); //jacobian term + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation + // returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + } + ' + }, + "nlg_sin_exp" = { + ' + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // [[Rcpp::export]] + arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::vec a1(1); + a1(0) = 0; + return a1; + } + // [[Rcpp::export]] + arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { + + arma::mat P1(1, 1); + P1(0,0) = 1; + return P1; + } + + // [[Rcpp::export]] + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat H(1,1); + H(0, 0) = exp(theta(0)); + return H; + } + + // Function for the Cholesky of state level covariance + // [[Rcpp::export]] + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat R(1, 1); + R(0, 0) = exp(theta(1)); + return R; + } + + // Z function + // [[Rcpp::export]] + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return exp(alpha); + } + // Jacobian of Z function + // [[Rcpp::export]] + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Z_gn(1, 1); + Z_gn(0, 0) = exp(alpha(0)); + return Z_gn; + } + + // T function + // [[Rcpp::export]] + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + return sin(alpha); + } + + // Jacobian of T function + // [[Rcpp::export]] + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + + arma::mat Tg(1, 1); + Tg(0, 0) = cos(alpha(0)); + return Tg; + } + + // log-prior pdf for theta + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0) + + R::dnorm(exp(theta(1)), 0, 1, 1) + theta(1); + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + + // typedef for a pointer of nonlinear function of model equation + // returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + + // typedef for a pointer returning a1 + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer returning P1 + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // typedef for a pointer of log-prior function + typedef double (*prior_fnPtr)(const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), + Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), + Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), + Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), + Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), + Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), + Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), + Rcpp::Named("log_prior_pdf") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); + } + ' + }) + if (!return_code) { + # create dummy variable to get rid of "undefined variable" note + create_xptrs <- NULL + Rcpp::sourceCpp(code = code) + create_xptrs() + } else code +} diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 90e99967..ad032107 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,7 +21,7 @@ ekpf_filter <- function(object, particles, ...) { #' @export #' @rdname ekpf_filter #' @examples -#' \donttest{ +#' #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) @@ -31,7 +31,7 @@ ekpf_filter <- function(object, particles, ...) { #' y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) #' } #' -#' pntrs <- nlg_example_models("sin_exp") +#' pntrs <- cpp_example_model("nlg_sin_exp") #' #' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, #' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, @@ -42,7 +42,7 @@ ekpf_filter <- function(object, particles, ...) { #' #' out <- ekpf_filter(model_nlg, 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -#' } +#' ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$integer.max, size = 1), ...) { diff --git a/R/kfilter.R b/R/kfilter.R index e582620d..cd0b7309 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -62,6 +62,33 @@ kfilter.nongaussian <- function(model, ...) { #' @export #' @rdname ekf #' @export +#' @examples +#' +#' set.seed(1) +#' mu <- -0.2 +#' rho <- 0.7 +#' sigma_y <- 0.1 +#' sigma_x <- 1 +#' x <- numeric(50) +#' x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +#' for(i in 2:length(x)) { +#' x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +#' } +#' y <- rnorm(50, exp(x), sigma_y) +#' +#' pntrs <- cpp_example_model("nlg_ar_exp") +#' +#' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, +#' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, +#' Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, +#' theta = c(mu= mu, rho = rho, +#' log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), +#' log_prior_pdf = pntrs$log_prior_pdf, +#' n_states = 1, n_etas = 1, state_names = "state") +#' +#' out_ekf <- ekf(model_nlg, iekf_iter = 0) +#' out_iekf <- ekf(model_nlg, iekf_iter = 5) +#' ts.plot(cbind(x, out_ekf$att, out_iekf$att), col = 1:3) ekf <- function(model, iekf_iter = 0) { iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) @@ -88,7 +115,12 @@ ekf <- function(model, iekf_iter = 0) { #' states \eqn{\alpha_t} given the data up to time \eqn{t}. #' #' @param model Model model -#' @param alpha,beta,kappa Tuning parameters for the UKF. +#' @param alpha Positive tuning parameter of the UKF. Default is 0.001. Smaller +#' the value, closer the sigma point are to the mean of the state. +#' @param beta Non-negative tuning parameter of the UKF. The default value is +#' 2, which is optimal for Gaussian states. +#' @param kappa Non-negative tuning parameter of the UKF, which also affects +#' the spread of sigma points. Default value is 0. #' @return List containing the log-likelihood, #' one-step-ahead predictions \code{at} and filtered #' estimates \code{att} of states, and the corresponding variances \code{Pt} and @@ -96,7 +128,38 @@ ekf <- function(model, iekf_iter = 0) { #' @export #' @rdname ukf #' @export -ukf <- function(model, alpha = 1, beta = 0, kappa = 2) { +#' @examples +#' set.seed(1) +#' mu <- -0.2 +#' rho <- 0.7 +#' sigma_y <- 0.1 +#' sigma_x <- 1 +#' x <- numeric(50) +#' x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +#' for(i in 2:length(x)) { +#' x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +#' } +#' y <- rnorm(50, exp(x), sigma_y) +#' +#' pntrs <- cpp_example_model("nlg_ar_exp") +#' +#' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, +#' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, +#' Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, +#' theta = c(mu= mu, rho = rho, +#' log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), +#' log_prior_pdf = pntrs$log_prior_pdf, +#' n_states = 1, n_etas = 1, state_names = "state") +#' +#' out_iekf <- ekf(model_nlg, iekf_iter = 5) +#' out_ukf <- ukf(model_nlg, alpha = 0.01, beta = 2, kappa = 1) +#' ts.plot(cbind(x, out_iekf$att, out_ukf$att), col = 1:3) +#' +ukf <- function(model, alpha = 0.001, beta = 2, kappa = 0) { + + if (alpha <= 0) stop("Parameter 'alpha' should be positive. ") + if (beta < 0) stop("Parameter 'beta' should be non-negative. ") + if (kappa < 0) stop("Parameter 'kappa' should be non-negative. ") out <- ukf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/models.R b/R/models.R index 0b1f14f1..1dd87a38 100644 --- a/R/models.R +++ b/R/models.R @@ -88,7 +88,9 @@ default_update_fn <- function(theta) { #' model <- ssm_ulg(y, Z, H, T, R, a1, P1, #' init_theta = c(1, 0.1, 0.1), #' update_fn = update_fn, prior_fn = prior_fn, -#' state_names = c("level", "b1", "b2")) +#' state_names = c("level", "b1", "b2"), +#' # using default values, but being explicit for testing purposes +#' C = matrix(0, 3, 1), D = numeric(1)) #' #' out <- run_mcmc(model, iter = 10000) #' out @@ -137,7 +139,7 @@ default_update_fn <- function(theta) { #' \donttest{ #' out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) #' out -#' +#' } #' # Above the regression coefficients are modelled as #' # time-invariant latent states. #' # Here is an alternative way where we use variable D so that the @@ -167,7 +169,7 @@ default_update_fn <- function(theta) { #' model_bssm2$theta <- init_theta #' model_bssm2$prior_fn <- prior2 #' model_bssm2$update_fn <- updatefn2 -#' +#' \donttest{ #' out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) #' out2 #' } @@ -781,7 +783,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' local trend component, a seasonal component, and regression component #' (or subset of these components). #' -#' @param y Vector or a \code{\link{ts}} object of observations. +#' @param y Vector or a \code{ts} object of observations. #' @param sd_level Standard deviation of the noise of level equation. #' Should be an object of class \code{bssm_prior} or scalar #' value defining a known value such as 0. @@ -833,8 +835,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' a1 = rep(0, 1 + 11), # level + period - 1 seasonal states #' P1 = diag(1, 12), #' C = matrix(0, 12, 1), -#' u = rep(1, nrow(Seatbelts)) -#' ) +#' u = rep(1, nrow(Seatbelts))) +#' #' \donttest{ #' set.seed(123) #' mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") @@ -849,14 +851,13 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + #' guides(alpha = "none") #' -#' -#' #' # Traceplot using as.data.frame method for MCMC output #' library("dplyr") #' as.data.frame(mcmc_out) %>% #' filter(variable == "sd_level") %>% #' ggplot(aes(y = value, x = iter)) + geom_line() #' +#' } #' # Model with slope term and additional noise to linear predictor to capture #' # excess variation #' model2 <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", @@ -865,9 +866,18 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' beta = normal(0, 0, 10), #' xreg = Seatbelts[, "law"], #' sd_slope = halfnormal(0.01, 0.1), -#' sd_noise = halfnormal(0.01, 1) -#' ) -#' } +#' sd_noise = halfnormal(0.01, 1)) +#' +#' # instead of extra noise term, model using negative binomial distribution: +#' model3 <- bsm_ng(Seatbelts[, "VanKilled"], +#' distribution = "negative binomial", +#' sd_level = halfnormal(0.01, 1), +#' sd_seasonal = halfnormal(0.01, 1), +#' beta = normal(0, 0, 10), +#' xreg = Seatbelts[, "law"], +#' sd_slope = halfnormal(0.01, 0.1), +#' phi = gamma_prior(1, 5, 5)) +#' bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, distribution, phi, u, beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, C = NULL) { @@ -1461,7 +1471,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @return Object of class \code{ssm_nlg}. #' @export #' @examples -#' \donttest{ +#' #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) @@ -1471,7 +1481,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) #' } #' -#' pntrs <- nlg_example_models("sin_exp") +#' pntrs <- cpp_example_model("nlg_sin_exp") #' #' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, #' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, @@ -1480,9 +1490,9 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' log_prior_pdf = pntrs$log_prior_pdf, #' n_states = 1, n_etas = 1, state_names = "state") #' -#' out <- ekf(model_nlg, 100) +#' out <- ekf(model_nlg, iekf_iter = 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -#' } +#' ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, known_params = NA, known_tv_params = matrix(NA), n_states, n_etas, log_prior_pdf, time_varying = rep(TRUE, 4), @@ -1537,7 +1547,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' @return Object of class \code{ssm_sde}. #' @export #' @examples -#' \donttest{ +#' #' library("sde") #' set.seed(1) #' # theta_0 = rho = 0.5 @@ -1549,9 +1559,8 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' sigma.x = expression(0)) #' y <- rpois(50, exp(x[-1])) #' -#' # Template can be found in the vignette -#' Rcpp::sourceCpp("ssm_sde_template.cpp") -#' pntrs <- create_xptrs() +#' # source c++ snippets +#' pntrs <- cpp_example_model("sde_poisson_OU") #' #' sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, #' pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, @@ -1569,7 +1578,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' particles = 50, iter = 2e4, #' threads = 4L) #' -#' } +#' ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive) { diff --git a/R/nlg_example_models.R b/R/nlg_example_models.R deleted file mode 100644 index 5992e1d7..00000000 --- a/R/nlg_example_models.R +++ /dev/null @@ -1,269 +0,0 @@ -#' Example C++ Codes for Non-Linear Models -#' -#' @param example Name of the example model. -#' Run \code{nlg_example_models("abc")} to get the names of possible models. -#' @param return_code If TRUE, will not compile the model but only returns the -#' corresponding code. -#' -#' @return Returns pointers to the C++ snippets defining the model, or in case -#' of \code{return_code = TRUE}, returns the example code without compiling. -#' @export -nlg_example_models <- function(example, return_code = FALSE) { - - example <- match.arg(example, c("linear_gaussian", "sin_exp")) - code <- switch(example, - "linear_gaussian" = { - ' - #include - // [[Rcpp::depends(RcppArmadillo)]] - // [[Rcpp::interfaces(r, cpp)]] - - // [[Rcpp::export]] - arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::vec a1(1); - a1(0) = 0; - return a1; - } - // [[Rcpp::export]] - arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::mat P1(1, 1); - P1(0,0) = 1; - return P1; - } - - // [[Rcpp::export]] - arma::mat H_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat H(1,1); - H(0, 0) = exp(theta(0)); - return H; - } - - // Function for the Cholesky of state level covariance - // [[Rcpp::export]] - arma::mat R_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat R(1, 1); - R(0, 0) = 1; - return R; - } - - - // Z function - // [[Rcpp::export]] - arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return alpha; - } - // Jacobian of Z function - // [[Rcpp::export]] - arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Z_gn(1, 1); - Z_gn(0, 0) = 1.0; - return Z_gn; - } - - // T function - // [[Rcpp::export]] - arma::vec T_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return alpha; - } - - // Jacobian of T function - // [[Rcpp::export]] - arma::mat T_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Tg(1, 1); - Tg(0, 0) = 1.0; - return Tg; - } - - // log-prior pdf for theta - // [[Rcpp::export]] - double log_prior_pdf(const arma::vec& theta) { - return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0); //jacobian term - } - - // [[Rcpp::export]] - Rcpp::List create_xptrs() { - - // typedef for a pointer of nonlinear function of model equation - // returning vec (T, Z) - typedef arma::vec (*nvec_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) - typedef arma::mat (*nmat_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - - // typedef for a pointer returning a1 - typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer returning P1 - typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer of log-prior function - typedef double (*prior_fnPtr)(const arma::vec& theta); - - return Rcpp::List::create( - Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), - Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), - Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), - Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), - Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), - Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), - Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), - Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), - Rcpp::Named("log_prior_pdf") = - Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); - } - ' - }, - "sin_exp" = { - ' - #include - // [[Rcpp::depends(RcppArmadillo)]] - // [[Rcpp::interfaces(r, cpp)]] - - // [[Rcpp::export]] - arma::vec a1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::vec a1(1); - a1(0) = 0; - return a1; - } - // [[Rcpp::export]] - arma::mat P1_fn(const arma::vec& theta, const arma::vec& known_params) { - - arma::mat P1(1, 1); - P1(0,0) = 1; - return P1; - } - - // [[Rcpp::export]] - arma::mat H_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat H(1,1); - H(0, 0) = exp(theta(0)); - return H; - } - - // Function for the Cholesky of state level covariance - // [[Rcpp::export]] - arma::mat R_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat R(1, 1); - R(0, 0) = exp(theta(1)); - return R; - } - - // Z function - // [[Rcpp::export]] - arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return exp(alpha); - } - // Jacobian of Z function - // [[Rcpp::export]] - arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Z_gn(1, 1); - Z_gn(0, 0) = exp(alpha(0)); - return Z_gn; - } - - // T function - // [[Rcpp::export]] - arma::vec T_fn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - return sin(alpha); - } - - // Jacobian of T function - // [[Rcpp::export]] - arma::mat T_gn(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, - const arma::mat& known_tv_params) { - - arma::mat Tg(1, 1); - Tg(0, 0) = cos(alpha(0)); - return Tg; - } - - // log-prior pdf for theta - // [[Rcpp::export]] - double log_prior_pdf(const arma::vec& theta) { - return R::dnorm(exp(theta(0)), 0, 1, 1) + theta(0) + - R::dnorm(exp(theta(1)), 0, 1, 1) + theta(1); - } - - // [[Rcpp::export]] - Rcpp::List create_xptrs() { - - // typedef for a pointer of nonlinear function of model equation - // returning vec (T, Z) - typedef arma::vec (*nvec_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear fn returning mat (Tg, Zg, H, R) - typedef arma::mat (*nmat_fnPtr)(const unsigned int t, - const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params); - - // typedef for a pointer returning a1 - typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer returning P1 - typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, - const arma::vec& known_params); - // typedef for a pointer of log-prior function - typedef double (*prior_fnPtr)(const arma::vec& theta); - - return Rcpp::List::create( - Rcpp::Named("a1_fn") = Rcpp::XPtr(new a1_fnPtr(&a1_fn)), - Rcpp::Named("P1_fn") = Rcpp::XPtr(new P1_fnPtr(&P1_fn)), - Rcpp::Named("Z_fn") = Rcpp::XPtr(new nvec_fnPtr(&Z_fn)), - Rcpp::Named("H_fn") = Rcpp::XPtr(new nmat_fnPtr(&H_fn)), - Rcpp::Named("T_fn") = Rcpp::XPtr(new nvec_fnPtr(&T_fn)), - Rcpp::Named("R_fn") = Rcpp::XPtr(new nmat_fnPtr(&R_fn)), - Rcpp::Named("Z_gn") = Rcpp::XPtr(new nmat_fnPtr(&Z_gn)), - Rcpp::Named("T_gn") = Rcpp::XPtr(new nmat_fnPtr(&T_gn)), - Rcpp::Named("log_prior_pdf") = - Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf))); - } - ' - }) - if (!return_code) { - # create dummy variable to get rid of "undefined variable" note - create_xptrs <- NULL - Rcpp::sourceCpp(code = code) - create_xptrs() - } else code -} diff --git a/R/post_correction.R b/R/post_correction.R index 8c47dec2..79d50bd5 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -16,10 +16,11 @@ get_map <- function(x) { #' but see also Section 10.3 in Vihola et al (2020). #' #' @param model Model of class \code{nongaussian} or \code{ssm_nlg}. -#' @param mcmc_output An output from \code{run_mcmc} used to compute the MAP -#' estimate of theta. While the intended use assumes this is from -#' approximate MCMC, it is not actually checked, i.e., -#' it is also possible to input previous (asymptotically) exact output. +#' @param theta A vector of theta corresponding to the model, at which point +#' the standard deviation of the log-likelihood is computed. Typically MAP +#' estimate from the (approximate) MCMC run. Can also be an output from +#' \code{run_mcmc} which is then used to compute the MAP +#' estimate of theta. #' @param candidates Vector containing the candidate number of particles to #' test. Default is \code{seq(10, 100, by = 10)}. #' @param replications How many replications should be used for computing @@ -39,7 +40,7 @@ get_map <- function(x) { #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples -#' \donttest{ +#' #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) @@ -47,7 +48,7 @@ get_map <- function(x) { #' alpha <- numeric(n) #' alpha[1] <- 0 #' rho <- 0.7 -#' sigma <- 2 +#' sigma <- 1.2 #' mu <- 1 #' for(i in 2:n) { #' alpha[i] <- rnorm(1, mu * (1 - rho) + rho * alpha[i-1], sigma) @@ -63,15 +64,21 @@ get_map <- function(x) { #' xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), #' u = u) #' -#' out_approx <- run_mcmc(model, mcmc_type = "approx", -#' iter = 5000) -#' -#' estN <- suggest_N(model, out_approx, candidates = seq(10, 50, by = 10)) +#' # theta from earlier approximate MCMC run +#' # out_approx <- run_mcmc(model, mcmc_type = "approx", +#' # iter = 5000) +#' # theta <- out_approx$theta[which.max(out_approx$posterior), ] +#' +#' theta <- c(rho = 0.64, sigma = 1.16, mu = 1.1, x1 = 0.56, x2 = 1.28) +#' +#' estN <- suggest_N(model, theta, candidates = seq(10, 50, by = 10), +#' replications = 50, seed = 1) #' plot(x = estN$results$N, y = estN$results$sd, type = "b") #' estN$N -#' } -suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), - replications = 100, seed = sample(.Machine$integer.max, size = 1)) { +#' +suggest_N <- function(model, theta, + candidates = seq(10, 100, by = 10), replications = 100, + seed = sample(.Machine$integer.max, size = 1)) { replications <- check_integer(replications, "replications") if (!test_integerish(candidates, lower = 1, any.missing = FALSE, @@ -82,9 +89,16 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), stop(paste("I don't believe you want to use over 1e7 particles", "If you really do, please file an issue at Github.", sep = " ")) - if (!inherits(mcmc_output, "mcmc_output")) - stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") - theta <- get_map(mcmc_output) + if (missing(theta) | (!is.vector(theta) & !inherits(theta, "mcmc_output"))) { + stop("'theta' should be either a vector or object of class 'mcmc_output'.") + } + if (inherits(theta, "mcmc_output")) { + theta <- get_map(theta) + } else { + if (length(theta) != length(model$theta)) { + stop("Length of 'theta' does not match length of 'model$theta'.") + } + } if (inherits(model, "nongaussian")) { model$distribution <- pmatch(model$distribution, @@ -96,7 +110,7 @@ suggest_N <- function(model, mcmc_output, candidates = seq(10, 100, by = 10), } else { if (inherits(model, "ssm_nlg")) { out <- suggest_n_nonlinear(t(model$y), model$Z, model$H, model$T, - model$R, model$Z_gn, model$T_gn, model$a1, model$P1, + modtel$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, model$known_tv_params, model$n_states, model$n_etas, as.integer(model$time_varying), diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 8db8780b..31d70aee 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,8 +1,10 @@ +#' Integrated Autocorrelation Time +#' +#' Here IACT is based on Sokal, +#' Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms iact <- function(x) { n <- length(x) x_ <- (x - mean(x)) / sd(x) - # Sokal: - # Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms C <- max(5.0, log10(n)) tau <- 1 for (k in 1:(n - 1)) { @@ -18,7 +20,6 @@ iact <- function(x) { #' #' @param x Vector of samples. #' @param w Vector of weights. -#' @export asymptotic_var <- function(x, w) { estimate_c <- mean(w) estimate_mean <- weighted_mean(x, w) diff --git a/R/smoother.R b/R/smoother.R index b06962d5..a54f0532 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -18,6 +18,12 @@ fast_smoother <- function(model, ...) { } #' @method fast_smoother gaussian #' @export +#' @examples +#' model <- bsm_lg(Nile, +#' sd_level = tnormal(120, 100, 20, min = 0), +#' sd_y = tnormal(50, 50, 25, min = 0), +#' a1 = 1000, P1 = 200) +#' ts.plot(cbind(Nile, fast_smoother(model)), col = 1:2) fast_smoother.gaussian <- function(model, ...) { out <- gaussian_fast_smoother(model, model_type(model)) @@ -38,6 +44,15 @@ smoother <- function(model, ...) { } #' @method smoother gaussian #' @export +#' @examples +#' model <- bsm_lg(Nile, +#' sd_y = tnormal(120, 100, 20, min = 0), +#' sd_level = tnormal(50, 50, 25, min = 0), +#' a1 = 1000, P1 = 500^2) +#' +#' out <- smoother(model) +#' ts.plot(cbind(Nile, out$alphahat), col = 1:2) +#' ts.plot(sqrt(out$Vt[1, 1, ])) smoother.gaussian <- function(model, ...) { out <- gaussian_smoother(model, model_type(model)) @@ -73,6 +88,34 @@ smoother.nongaussian <- function(model, ...) { #' \code{Vt} and \code{Ptt}. #' @export #' @rdname ekf_smoother +#' @examples +#' +#' set.seed(1) +#' mu <- -0.2 +#' rho <- 0.7 +#' sigma_y <- 0.1 +#' sigma_x <- 1 +#' x <- numeric(50) +#' x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +#' for(i in 2:length(x)) { +#' x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +#' } +#' y <- rnorm(length(x), exp(x), sigma_y) +#' +#' pntrs <- cpp_example_model("nlg_ar_exp") +#' +#' model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, +#' Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, +#' Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, +#' theta = c(mu= mu, rho = rho, +#' log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), +#' log_prior_pdf = pntrs$log_prior_pdf, +#' n_states = 1, n_etas = 1, state_names = "state") +#' +#' out_ekf <- ekf_smoother(model_nlg, iekf_iter = 0) +#' out_iekf <- ekf_smoother(model_nlg, iekf_iter = 1) +#' ts.plot(cbind(x, out_ekf$alphahat, out_iekf$alphahat), col = 1:3) +#' ekf_smoother <- function(model, iekf_iter = 0) { iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index 83035a7d..ce87a280 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -7,7 +7,7 @@ ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) } \arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} +\item{y}{Vector or a \code{ts} object of observations.} \item{rho}{Prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index f321719d..b4b9912a 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -7,7 +7,7 @@ ar1_ng(y, rho, sigma, mu, distribution, phi, u, beta, xreg = NULL) } \arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} +\item{y}{Vector or a \code{ts} object of observations.} \item{rho}{Prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} diff --git a/man/as_draws.Rd b/man/as_draws.Rd new file mode 100644 index 00000000..ac1bc81d --- /dev/null +++ b/man/as_draws.Rd @@ -0,0 +1,52 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/as_draws.R +\name{as_draws_df.mcmc_output} +\alias{as_draws_df.mcmc_output} +\alias{as_draws.mcmc_output} +\title{Convert \code{run_mcmc} output to \code{draws_df} format} +\usage{ +as_draws_df.mcmc_output(x) + +as_draws.mcmc_output(x) +} +\arguments{ +\item{x}{An object of class \code{mcmc_output}} +} +\value{ +A \code{draws_df} object. +} +\description{ +Converts MCMC output from \code{run_mcmc} call to a +\code{draws_df} format of the \code{posterior} package. This enables the use +of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} +packages. Note though that if \code{run_mcmc} used IS-MCMC +method, the resulting \code{weight} column of the output is +ignored by the aforementioned packages, i.e. the results correspond to +approximate MCMC. +} +\examples{ + +model <- bsm_lg(Nile, + sd_level = tnormal(init = 100, mean = 100, sd = 100, min = 0), + sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0)) + +fit1 <- run_mcmc(model, iter = 2e4) +head(as_draws(fit1), 4) +posterior::ess_bulk(draws$sd_y) +summary(fit1, return_se = TRUE) + +# More chains: +model$theta[] <- c(50, 150) # change initial value +fit2 <- run_mcmc(model, iter = 2e4) +model$theta[] <- c(150, 50) # change initial value +fit3 <- run_mcmc(model, iter = 2e4) + +draws <- posterior::bind_draws(as_draws(fit1), + as_draws(fit2), as_draws(fit3), along = "chain") +# it is actually enough to transform first mcmc_output to draws object, +# rest are transformed automatically inside bind_draws +posterior::rhat(draws$sd_y) +posterior::ess_bulk(draws$sd_y) +posterior::ess_tail(draws$sd_y) + +} diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 6036164d..98e513a0 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -20,7 +20,7 @@ bsm_lg( ) } \arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} +\item{y}{Vector or a \code{ts} object of observations.} \item{sd_y}{Standard deviation of the noise of observation equation. Should be an object of class \code{bssm_prior} or scalar} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 39ea6b6f..8bdeb70d 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -22,7 +22,7 @@ bsm_ng( ) } \arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} +\item{y}{Vector or a \code{ts} object of observations.} \item{sd_level}{Standard deviation of the noise of level equation. Should be an object of class \code{bssm_prior} or scalar @@ -94,8 +94,8 @@ model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", a1 = rep(0, 1 + 11), # level + period - 1 seasonal states P1 = diag(1, 12), C = matrix(0, 12, 1), - u = rep(1, nrow(Seatbelts)) - ) + u = rep(1, nrow(Seatbelts))) + \donttest{ set.seed(123) mcmc_out <- run_mcmc(model, iter = 5000, particles = 10, mcmc_type = "da") @@ -110,14 +110,13 @@ ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + geom = "polygon") + scale_fill_continuous(low = "green", high = "blue") + guides(alpha = "none") - - # Traceplot using as.data.frame method for MCMC output library("dplyr") as.data.frame(mcmc_out) \%>\% filter(variable == "sd_level") \%>\% ggplot(aes(y = value, x = iter)) + geom_line() +} # Model with slope term and additional noise to linear predictor to capture # excess variation model2 <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", @@ -126,7 +125,16 @@ model2 <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", beta = normal(0, 0, 10), xreg = Seatbelts[, "law"], sd_slope = halfnormal(0.01, 0.1), - sd_noise = halfnormal(0.01, 1) - ) -} + sd_noise = halfnormal(0.01, 1)) + +# instead of extra noise term, model using negative binomial distribution: +model3 <- bsm_ng(Seatbelts[, "VanKilled"], + distribution = "negative binomial", + sd_level = halfnormal(0.01, 1), + sd_seasonal = halfnormal(0.01, 1), + beta = normal(0, 0, 10), + xreg = Seatbelts[, "law"], + sd_slope = halfnormal(0.01, 0.1), + phi = gamma_prior(1, 5, 5)) + } diff --git a/man/cpp_example_model.Rd b/man/cpp_example_model.Rd new file mode 100644 index 00000000..cc92bfe3 --- /dev/null +++ b/man/cpp_example_model.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/cpp_example_models.R +\name{cpp_example_model} +\alias{cpp_example_model} +\title{Example C++ Codes for Non-Linear and SDE Models} +\usage{ +cpp_example_model(example, return_code = FALSE) +} +\arguments{ +\item{example}{Name of the example model. +Run \code{cpp_example_model("abc")} to get the names of possible models.} + +\item{return_code}{If TRUE, will not compile the model but only returns the +corresponding code.} +} +\value{ +Returns pointers to the C++ snippets defining the model, or in case +of \code{return_code = TRUE}, returns the example code without compiling. +} +\description{ +Example C++ Codes for Non-Linear and SDE Models +} +\examples{ +cpp_example_model("poisson_OU", return_code = TRUE) + +} diff --git a/man/ekf.Rd b/man/ekf.Rd index 9d3e9cb6..70332546 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -24,3 +24,31 @@ non-linear Gaussian model of class \code{ssm_nlg}, and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } +\examples{ + +set.seed(1) +mu <- -0.2 +rho <- 0.7 +sigma_y <- 0.1 +sigma_x <- 1 +x <- numeric(50) +x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +for(i in 2:length(x)) { + x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +} +y <- rnorm(50, exp(x), sigma_y) + +pntrs <- cpp_example_model("nlg_ar_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(mu= mu, rho = rho, + log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out_ekf <- ekf(model_nlg, iekf_iter = 0) +out_iekf <- ekf(model_nlg, iekf_iter = 5) +ts.plot(cbind(x, out_ekf$att, out_iekf$att), col = 1:3) +} diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index c9e4fe08..e9ba8cd2 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -32,7 +32,7 @@ Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ -\donttest{ + set.seed(1) n <- 50 x <- y <- numeric(n) @@ -42,7 +42,7 @@ for(i in 1:(n-1)) { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } -pntrs <- nlg_example_models("sin_exp") +pntrs <- cpp_example_model("nlg_sin_exp") model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, @@ -53,7 +53,7 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, out <- ekpf_filter(model_nlg, 100) ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -} + } \references{ Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. diff --git a/man/iact.Rd b/man/iact.Rd new file mode 100644 index 00000000..8d59faf3 --- /dev/null +++ b/man/iact.Rd @@ -0,0 +1,12 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/print_mcmc.R +\name{iact} +\alias{iact} +\title{Integrated Autocorrelation Time} +\usage{ +iact(x) +} +\description{ +Here IACT is based on Sokal, +Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms +} diff --git a/man/nlg_example_models.Rd b/man/nlg_example_models.Rd deleted file mode 100644 index 21f55806..00000000 --- a/man/nlg_example_models.Rd +++ /dev/null @@ -1,22 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nlg_example_models.R -\name{nlg_example_models} -\alias{nlg_example_models} -\title{Example C++ Codes for Non-Linear Models} -\usage{ -nlg_example_models(example, return_code = FALSE) -} -\arguments{ -\item{example}{Name of the example model. -Run \code{nlg_example_models("abc")} to get the names of possible models.} - -\item{return_code}{If TRUE, will not compile the model but only returns the -corresponding code.} -} -\value{ -Returns pointers to the C++ snippets defining the model, or in case -of \code{return_code = TRUE}, returns the example code without compiling. -} -\description{ -Example C++ Codes for Non-Linear Models -} diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index 89522e2d..9feaf8c5 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -85,7 +85,7 @@ the user, as you must provide the several small C++ snippets which define the model structure. See examples in the vignette. } \examples{ -\donttest{ + set.seed(1) n <- 50 x <- y <- numeric(n) @@ -95,7 +95,7 @@ for(i in 1:(n-1)) { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } -pntrs <- nlg_example_models("sin_exp") +pntrs <- cpp_example_model("nlg_sin_exp") model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, @@ -104,7 +104,7 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state") -out <- ekf(model_nlg, 100) +out <- ekf(model_nlg, iekf_iter = 100) ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -} + } diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 7e469de3..4b8f1cc0 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -53,7 +53,7 @@ effort from the user, as you must provide the several small C++ snippets which define the model structure. See vignettes for an example. } \examples{ -\donttest{ + library("sde") set.seed(1) # theta_0 = rho = 0.5 @@ -65,9 +65,8 @@ x <- sde.sim(t0 = 0, T = 50, X0 = 1, N = 50, sigma.x = expression(0)) y <- rpois(50, exp(x[-1])) -# Template can be found in the vignette -Rcpp::sourceCpp("ssm_sde_template.cpp") -pntrs <- create_xptrs() +# source c++ snippets +pntrs <- cpp_example_model("sde_poisson_OU") sde_model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, pntrs$ddiffusion, pntrs$obs_density, pntrs$prior, @@ -85,5 +84,5 @@ out <- run_mcmc(sde_model, L_c = 4, L_f = 8, particles = 50, iter = 2e4, threads = 4L) -} + } diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index bee044fd..102bb1c2 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -134,7 +134,9 @@ prior_fn <- function(theta) { model <- ssm_ulg(y, Z, H, T, R, a1, P1, init_theta = c(1, 0.1, 0.1), update_fn = update_fn, prior_fn = prior_fn, - state_names = c("level", "b1", "b2")) + state_names = c("level", "b1", "b2"), + # using default values, but being explicit for testing purposes + C = matrix(0, 3, 1), D = numeric(1)) out <- run_mcmc(model, iter = 10000) out @@ -183,7 +185,7 @@ model_bssm <- as_bssm(model_kfas, kappa = 100, \donttest{ out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) out - +} # Above the regression coefficients are modelled as # time-invariant latent states. # Here is an alternative way where we use variable D so that the @@ -213,7 +215,7 @@ model_bssm2 <- updatefn2(init_theta) model_bssm2$theta <- init_theta model_bssm2$prior_fn <- prior2 model_bssm2$update_fn <- updatefn2 - +\donttest{ out2 <- run_mcmc(model_bssm2, iter = 10000, burnin = 5000) out2 } diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index 77266edb..db51d0fe 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -6,7 +6,7 @@ \usage{ suggest_N( model, - mcmc_output, + theta, candidates = seq(10, 100, by = 10), replications = 100, seed = sample(.Machine$integer.max, size = 1) @@ -15,10 +15,11 @@ suggest_N( \arguments{ \item{model}{Model of class \code{nongaussian} or \code{ssm_nlg}.} -\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP -estimate of theta. While the intended use assumes this is from -approximate MCMC, it is not actually checked, i.e., -it is also possible to input previous (asymptotically) exact output.} +\item{theta}{A vector of theta corresponding to the model, at which point +the standard deviation of the log-likelihood is computed. Typically MAP +estimate from the (approximate) MCMC run. Can also be an output from +\code{run_mcmc} which is then used to compute the MAP +estimate of theta.} \item{candidates}{Vector containing the candidate number of particles to test. Default is \code{seq(10, 100, by = 10)}.} @@ -46,7 +47,7 @@ suggested in the context of pseudo-marginal MCMC by Doucet et al. (2015), but see also Section 10.3 in Vihola et al (2020). } \examples{ -\donttest{ + set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) @@ -54,7 +55,7 @@ x2 <- cos((2 * pi / 12) * 1:n) alpha <- numeric(n) alpha[1] <- 0 rho <- 0.7 -sigma <- 2 +sigma <- 1.2 mu <- 1 for(i in 2:n) { alpha[i] <- rnorm(1, mu * (1 - rho) + rho * alpha[i-1], sigma) @@ -70,13 +71,18 @@ model <- ar1_ng(y, distribution = "binomial", xreg = cbind(x1,x2), beta = normal(c(0, 0), 0, 5), u = u) -out_approx <- run_mcmc(model, mcmc_type = "approx", - iter = 5000) +# theta from earlier approximate MCMC run +# out_approx <- run_mcmc(model, mcmc_type = "approx", +# iter = 5000) +# theta <- out_approx$theta[which.max(out_approx$posterior), ] + +theta <- c(rho = 0.64, sigma = 1.16, mu = 1.1, x1 = 0.56, x2 = 1.28) -estN <- suggest_N(model, out_approx, candidates = seq(10, 50, by = 10)) +estN <- suggest_N(model, theta, candidates = seq(10, 50, by = 10), + replications = 50, seed = 1) plot(x = estN$results$N, y = estN$results$sd, type = "b") estN$N -} + } \references{ Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). diff --git a/man/ukf.Rd b/man/ukf.Rd index 24cec35d..c3dd6883 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -4,12 +4,19 @@ \alias{ukf} \title{Unscented Kalman Filtering} \usage{ -ukf(model, alpha = 1, beta = 0, kappa = 2) +ukf(model, alpha = 0.001, beta = 2, kappa = 0) } \arguments{ \item{model}{Model model} -\item{alpha, beta, kappa}{Tuning parameters for the UKF.} +\item{alpha}{Positive tuning parameter of the UKF. Default is 0.001. Smaller +the value, closer the sigma point are to the mean of the state.} + +\item{beta}{Non-negative tuning parameter of the UKF. The default value is +2, which is optimal for Gaussian states.} + +\item{kappa}{Non-negative tuning parameter of the UKF, which also affects +the spread of sigma points. Default value is 0.} } \value{ List containing the log-likelihood, @@ -23,3 +30,31 @@ non-linear Gaussian model of class \code{ssm_nlg}, and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } +\examples{ +set.seed(1) +mu <- -0.2 +rho <- 0.7 +sigma_y <- 0.1 +sigma_x <- 1 +x <- numeric(50) +x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +for(i in 2:length(x)) { + x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +} +y <- rnorm(50, exp(x), sigma_y) + +pntrs <- cpp_example_model("nlg_ar_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(mu= mu, rho = rho, + log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out_iekf <- ekf(model_nlg, iekf_iter = 5) +out_ukf <- ukf(model_nlg, alpha = 0.01, beta = 2, kappa = 1) +ts.plot(cbind(x, out_iekf$att, out_ukf$att), col = 1:3) + +} diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index a25afb70..d16920d5 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -140,7 +140,7 @@ test_that("Gaussian approximation works for nonlinear models", { skip_on_cran() - pntrs <- nlg_example_models("linear_gaussian") + pntrs <- cpp_example_model("nlg_linear_gaussian") set.seed(1) y <- cumsum(rnorm(10)) + rnorm(10) model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, @@ -163,7 +163,7 @@ test_that("Gaussian approximation works for nonlinear models", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } y[2:5] <- NA - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index 6f96efbe..c647f124 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -13,7 +13,7 @@ test_that("Particle filtering based on EKF works", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, @@ -52,7 +52,7 @@ test_that("EKF and IEKF work", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } y[2:3] <- NA - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 44702d57..b655603a 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -291,7 +291,7 @@ test_that("MCMC for nonlinear models work", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index 2012e66f..2ed64b01 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -54,7 +54,7 @@ test_that("Test post correction for non-linear model", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } y[2:3] <- NA - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 640ad8f5..54011a27 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -153,7 +153,7 @@ test_that("Predictions for nlg_ssm work", { y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - pntrs <- nlg_example_models("sin_exp") + pntrs <- cpp_example_model("nlg_sin_exp") expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, From 7103e2ad3f76f8859ee7a78301310ccdf514ac2b Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 16 Sep 2021 15:57:47 +0300 Subject: [PATCH 084/180] check period argument --- R/check_arguments.R | 22 ++++++++++++++++------ R/models.R | 8 ++------ 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 6477aba4..6a901546 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -3,10 +3,10 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(!is.na(x))) { if (multivariate) { if (!is.matrix(x)) { - stop("Argument y must be a matrix or multivariate ts object.") + stop("Argument 'y' must be a matrix or multivariate ts object.") } if (nrow(x) < 2) { - stop("Number of rows in y, i.e. number of time points, must be > 1. ") + stop("Number of rows in 'y', i.e. number of time points, must be > 1. ") } } else { if (!is.vector(x) || is.list(x)) { @@ -15,11 +15,11 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { dim(x) <- NULL } else { if(!is.null(dim(x)) && ncol(x) > 1) { - stop("Argument y must be a vector or univariate ts object.") + stop("Argument 'y' must be a vector or univariate ts object.") } } } else { - stop("Argument y must be a vector or univariate ts object.") + stop("Argument 'y' must be a vector or univariate ts object.") } } if (length(x) < 2) { @@ -38,7 +38,17 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } } if (any(is.infinite(x))) { - stop("Argument y must contain only finite or NA values.") + stop("Argument 'y' must contain only finite or NA values.") + } + } + x +} +check_period <- function(x) { + if (!test_int(x)) { + stop("Argument 'period' should be a single integer. ") + } else { + if (x < 3) { + stop("Argument 'period' should be a integer larger than 2. ") } } x @@ -367,5 +377,5 @@ check_positive_real <- function(x, name) { if (!test_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { stop(paste0("Argument '", name, "' should be positive real value.")) } - x + x } diff --git a/R/models.R b/R/models.R index 1dd87a38..99781227 100644 --- a/R/models.R +++ b/R/models.R @@ -670,9 +670,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, seasonal <- FALSE sd_seasonal <- NULL } else { - if (period < 3) { - stop("Period of seasonal component must be larger than 2. ") - } + period <- check_period(period) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") } else { @@ -926,9 +924,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, seasonal <- FALSE sd_seasonal <- NULL } else { - if (period < 3) { - stop("Period of seasonal component must be larger than 2. ") - } + period <- check_period(period) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") } else { From 4fe1f19cad4b6ee3dcbc7032e8bbce92403f2e98 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 17 Sep 2021 16:19:40 +0300 Subject: [PATCH 085/180] more examples, warn about overly large number of samples --- NAMESPACE | 1 + R/bootstrap_filter.R | 26 +++++++++++++++++++++++++- R/bssm-package.R | 6 +++--- R/check_arguments.R | 7 +++++-- R/ekpf_filter.R | 7 +++++++ R/importance_sample.R | 7 +++++++ R/models.R | 6 +++--- R/particle_smoother.R | 26 +++++++++++++++++++++++++- R/run_mcmc.R | 38 +++++++++++++++++++++++++++++++++----- R/smoother.R | 2 ++ man/as_draws.Rd | 5 +++-- man/bsm_lg.Rd | 2 +- man/bssm.Rd | 4 ++-- man/ekf_smoother.Rd | 29 +++++++++++++++++++++++++++++ man/smoother.Rd | 21 +++++++++++++++++++++ 15 files changed, 167 insertions(+), 20 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 347ecbb8..2b118fb4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -80,6 +80,7 @@ importFrom(Rcpp,evalCpp) importFrom(checkmate,test_count) importFrom(checkmate,test_double) importFrom(checkmate,test_flag) +importFrom(checkmate,test_int) importFrom(checkmate,test_integerish) importFrom(coda,mcmc) importFrom(coda,spectrum0.ar) diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 5447f7e3..660e209f 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -45,6 +45,12 @@ bootstrap_filter.gaussian <- function(model, particles, } particles <- check_integer(particles, "particles") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } out <- bsf(model, particles, seed, TRUE, model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- @@ -80,7 +86,12 @@ bootstrap_filter.nongaussian <- function(model, particles, } } particles <- check_integer(particles, "particles") - + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), @@ -112,6 +123,13 @@ bootstrap_filter.ssm_nlg <- function(model, particles, } particles <- check_integer(particles, "particles") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + model$n_states * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } + out <- bsf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, @@ -147,6 +165,12 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, particles <- check_integer(particles, "particles") + nsamples <- length(model$y) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } + out <- bsf_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, diff --git a/R/bssm-package.R b/R/bssm-package.R index a99b4abe..cf548006 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -23,8 +23,8 @@ #' Non-Gaussian State Space Models in R. ArXiv 2101.08492, #' . #' -#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @docType package @@ -34,7 +34,7 @@ #' @importFrom coda mcmc #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit -#' @importFrom checkmate test_count test_double test_flag test_integerish +#' @importFrom checkmate test_count test_double test_flag test_integerish test_int #' @useDynLib bssm NULL #' Deaths by drowning in Finland in 1969-2019 diff --git a/R/check_arguments.R b/R/check_arguments.R index 6a901546..e25dec90 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -43,13 +43,16 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } x } -check_period <- function(x) { +check_period <- function(x, n) { if (!test_int(x)) { stop("Argument 'period' should be a single integer. ") } else { if (x < 3) { stop("Argument 'period' should be a integer larger than 2. ") } + if (x >= n) { + stop("Period should be less than the number of time points.") + } } x } @@ -77,7 +80,7 @@ check_sd <- function(x, type, add_prefix = TRUE) { param <- type } if (length(x) != 1) { - stop(paste0("Argument ", param, " must be of length one.")) + stop(paste0("Argument ", param, " must be of length one (scalar or bssm_prior).")) } if (!is.numeric(x)) { stop(paste0("Argument ", param, " must be numeric.")) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index ad032107..077e696f 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -56,6 +56,13 @@ ekpf_filter.ssm_nlg <- function(object, particles, } particles <- check_integer(particles, "particles") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + model$n_states * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } + out <- ekpf(t(object$y), object$Z, object$H, object$T, object$R, object$Z_gn, object$T_gn, object$a1, object$P1, object$theta, object$log_prior_pdf, object$known_params, diff --git a/R/importance_sample.R b/R/importance_sample.R index 3acba417..24c34ed9 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -49,6 +49,13 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, model$conv_tol <- check_positive_real(conv_tol, "conv_tol") nsim <- check_integer(nsim, "nsim") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * nsim + if (nsim > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "values, you might run out of memory.")) + } + model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 diff --git a/R/models.R b/R/models.R index 99781227..28f955c4 100644 --- a/R/models.R +++ b/R/models.R @@ -583,7 +583,7 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' #' @inheritParams bsm_ng #' @param sd_y Standard deviation of the noise of observation equation. -#' Should be an object of class \code{bssm_prior} or scalar +#' Should be an object of class \code{bssm_prior} or scalar. #' @param D,C Intercept terms for observation and #' state equations, given as a length n vector and m times n matrix #' respectively (or scalar and m times 1 matrix). @@ -670,7 +670,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, seasonal <- FALSE sd_seasonal <- NULL } else { - period <- check_period(period) + period <- check_period(period, n) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") } else { @@ -924,7 +924,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, seasonal <- FALSE sd_seasonal <- NULL } else { - period <- check_period(period) + period <- check_period(period, n) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") } else { diff --git a/R/particle_smoother.R b/R/particle_smoother.R index b65b689e..a342b537 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -86,6 +86,12 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", particles <- check_integer(particles, "particles") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } if (method == "psi") { out <- list() out$alpha <- gaussian_psi_smoother(model, particles, seed, @@ -131,6 +137,12 @@ particle_smoother.nongaussian <- function(model, particles, } } particles <- check_integer(particles, "particles") + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -172,6 +184,14 @@ particle_smoother.ssm_nlg <- function(model, particles, } } particles <- check_integer(particles, "particles") + + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + model$n_states * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } + max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) @@ -226,7 +246,11 @@ particle_smoother.ssm_sde <- function(model, particles, L, } } particles <- check_integer(particles, "particles") - + nsamples <- length(model$y) * particles + if (particles > 100 & nsamples > 1e12) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } out <- bsf_smoother_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, diff --git a/R/run_mcmc.R b/R/run_mcmc.R index db631037..d77be66f 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -115,8 +115,14 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - - + if(output_type == "full") { + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * (iter - burnin) / thin * target_acceptance + if (nsamples > 1e12) { + warning(paste("Number of state samples to be stored is approximately", + nsamples, "you might run out of memory.")) + } + } out <- gaussian_mcmc(model, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, model_type(model)) @@ -391,6 +397,15 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", model$distribution <- pmatch(model$distribution, dists, duplicates.ok = TRUE) - 1 + if(output_type == "full") { + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * (iter - burnin) / thin * target_acceptance + if (nsamples > 1e12) { + warning(paste("Number of state samples to be stored is approximately", + nsamples, "you might run out of memory.")) + } + } + if (inherits(model, "bsm_ng")) { names_ind <- @@ -595,7 +610,14 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", stop(paste("Number of state samples less than 2, use 'mcmc_type'", "'approx' or 'ekf' instead.", sep = " ")) - + if(output_type == "full") { + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + model$n_states * (iter - burnin) / thin * target_acceptance + if (nsamples > 1e12) { + warning(paste("Number of state samples to be stored is approximately", + nsamples, "you might run out of memory.")) + } + } out <- switch(mcmc_type, "da" = { nonlinear_da_mcmc(t(model$y), model$Z, model$H, model$T, @@ -767,13 +789,12 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", thin <- check_integer(thin, "thin", max = 100) iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_integer(burnin, "burnin", max = 1e12) - + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_target(target_acceptance) - output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3))) @@ -790,6 +811,13 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", L <- max(L_c, L_f) if (L <= 0) stop("L should be positive.") } + if(output_type == "full") { + nsamples <- length(model$y) * (iter - burnin) / thin * target_acceptance + if (nsamples > 1e12) { + warning(paste("Number of state samples to be stored is approximately", + nsamples, "you might run out of memory.")) + } + } out <- switch(mcmc_type, "da" = { out <- sde_da_mcmc(model$y, model$x0, model$positive, diff --git a/R/smoother.R b/R/smoother.R index a54f0532..26e76a37 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -17,6 +17,7 @@ fast_smoother <- function(model, ...) { UseMethod("fast_smoother", model) } #' @method fast_smoother gaussian +#' @rdname smoother #' @export #' @examples #' model <- bsm_lg(Nile, @@ -43,6 +44,7 @@ smoother <- function(model, ...) { UseMethod("smoother", model) } #' @method smoother gaussian +#' @rdname smoother #' @export #' @examples #' model <- bsm_lg(Nile, diff --git a/man/as_draws.Rd b/man/as_draws.Rd index ac1bc81d..1489bc55 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -27,8 +27,9 @@ approximate MCMC. \examples{ model <- bsm_lg(Nile, - sd_level = tnormal(init = 100, mean = 100, sd = 100, min = 0), - sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0)) + sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), + sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), + a1 = 1000, P1 = 500^2) fit1 <- run_mcmc(model, iter = 2e4) head(as_draws(fit1), 4) diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 98e513a0..8dad2f35 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -23,7 +23,7 @@ bsm_lg( \item{y}{Vector or a \code{ts} object of observations.} \item{sd_y}{Standard deviation of the noise of observation equation. -Should be an object of class \code{bssm_prior} or scalar} +Should be an object of class \code{bssm_prior} or scalar.} \item{sd_level}{Standard deviation of the noise of level equation. Should be an object of class \code{bssm_prior} or scalar diff --git a/man/bssm.Rd b/man/bssm.Rd index 35d45321..b96daa8b 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -29,7 +29,7 @@ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. ArXiv 2101.08492, . -Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index f25d85c8..e14f6ea0 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -27,3 +27,32 @@ and returns the smoothed estimates of the states and the corresponding variances. Function \code{ekf_fast_smoother} computes only smoothed estimates of the states. } +\examples{ + +set.seed(1) +mu <- -0.2 +rho <- 0.7 +sigma_y <- 0.1 +sigma_x <- 1 +x <- numeric(50) +x[1] <- rnorm(1, mu, sigma_x / sqrt(1 - rho^2)) +for(i in 2:length(x)) { + x[i] <- rnorm(1, mu * (1 - rho) + rho * x[i - 1], sigma_x) +} +y <- rnorm(length(x), exp(x), sigma_y) + +pntrs <- cpp_example_model("nlg_ar_exp") + +model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, + Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, + theta = c(mu= mu, rho = rho, + log_sigma_x = log(sigma_x), log_sigma_y = log(sigma_y)), + log_prior_pdf = pntrs$log_prior_pdf, + n_states = 1, n_etas = 1, state_names = "state") + +out_ekf <- ekf_smoother(model_nlg, iekf_iter = 0) +out_iekf <- ekf_smoother(model_nlg, iekf_iter = 1) +ts.plot(cbind(x, out_ekf$alphahat, out_iekf$alphahat), col = 1:3) + +} diff --git a/man/smoother.Rd b/man/smoother.Rd index f624ecce..c9ca93c5 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -2,12 +2,18 @@ % Please edit documentation in R/smoother.R \name{fast_smoother} \alias{fast_smoother} +\alias{fast_smoother.gaussian} \alias{smoother} +\alias{smoother.gaussian} \title{Kalman Smoothing} \usage{ fast_smoother(model, ...) +\method{fast_smoother}{gaussian}(model, ...) + smoother(model, ...) + +\method{smoother}{gaussian}(model, ...) } \arguments{ \item{model}{Model model.} @@ -27,3 +33,18 @@ computes only smoothed estimates of the states, and function For non-Gaussian models, the smoothing is based on the approximate Gaussian model. } +\examples{ +model <- bsm_lg(Nile, + sd_level = tnormal(120, 100, 20, min = 0), + sd_y = tnormal(50, 50, 25, min = 0), + a1 = 1000, P1 = 200) +ts.plot(cbind(Nile, fast_smoother(model)), col = 1:2) +model <- bsm_lg(Nile, + sd_y = tnormal(120, 100, 20, min = 0), + sd_level = tnormal(50, 50, 25, min = 0), + a1 = 1000, P1 = 500^2) + +out <- smoother(model) +ts.plot(cbind(Nile, out$alphahat), col = 1:2) +ts.plot(sqrt(out$Vt[1, 1, ])) +} From b016ee28291cf64024240ff22e5334db72f7230f Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 17 Sep 2021 23:25:27 +0300 Subject: [PATCH 086/180] fix typos, load posterior package --- R/as_draws.R | 11 ++++++----- R/ekpf_filter.R | 4 ++-- R/post_correction.R | 2 +- R/print_mcmc.R | 23 ++++++++++++----------- man/as_draws.Rd | 11 ++++++----- man/asymptotic_var.Rd | 17 ----------------- man/iact.Rd | 12 ------------ 7 files changed, 27 insertions(+), 53 deletions(-) delete mode 100644 man/asymptotic_var.Rd delete mode 100644 man/iact.Rd diff --git a/R/as_draws.R b/R/as_draws.R index 4b67b314..6704c7c3 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -21,8 +21,9 @@ #' a1 = 1000, P1 = 500^2) #' #' fit1 <- run_mcmc(model, iter = 2e4) +#' library("posterior") #' head(as_draws(fit1), 4) -#' posterior::ess_bulk(draws$sd_y) +#' ess_bulk(draws$sd_y) #' summary(fit1, return_se = TRUE) #' #' # More chains: @@ -31,13 +32,13 @@ #' model$theta[] <- c(150, 50) # change initial value #' fit3 <- run_mcmc(model, iter = 2e4) #' -#' draws <- posterior::bind_draws(as_draws(fit1), +#' draws <- bind_draws(as_draws(fit1), #' as_draws(fit2), as_draws(fit3), along = "chain") #' # it is actually enough to transform first mcmc_output to draws object, #' # rest are transformed automatically inside bind_draws -#' posterior::rhat(draws$sd_y) -#' posterior::ess_bulk(draws$sd_y) -#' posterior::ess_tail(draws$sd_y) +#' rhat(draws$sd_y) +#' ess_bulk(draws$sd_y) +#' ess_tail(draws$sd_y) #' as_draws_df.mcmc_output <- function(x) { diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 077e696f..2cf9f237 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -56,8 +56,8 @@ ekpf_filter.ssm_nlg <- function(object, particles, } particles <- check_integer(particles, "particles") - nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * - model$n_states * particles + nsamples <- ifelse(!is.null(nrow(object$y)), nrow(object$y), + length(object$y)) * object$n_states * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) diff --git a/R/post_correction.R b/R/post_correction.R index 79d50bd5..fd26b6b9 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -110,7 +110,7 @@ suggest_N <- function(model, theta, } else { if (inherits(model, "ssm_nlg")) { out <- suggest_n_nonlinear(t(model$y), model$Z, model$H, model$T, - modtel$R, model$Z_gn, model$T_gn, model$a1, model$P1, + model$R, model$Z_gn, model$T_gn, model$a1, model$P1, model$theta, model$log_prior_pdf, model$known_params, model$known_tv_params, model$n_states, model$n_etas, as.integer(model$time_varying), diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 31d70aee..9a6004fc 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,7 +1,8 @@ -#' Integrated Autocorrelation Time -#' -#' Here IACT is based on Sokal, -#' Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms +# Integrated Autocorrelation Time +# +# Here IACT is based on Sokal, +# Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms +# @param x A vector. iact <- function(x) { n <- length(x) x_ <- (x - mean(x)) / sd(x) @@ -13,13 +14,13 @@ iact <- function(x) { } max(0.0, tau) } -#' Asymptotic Variance of IS-type Estimators -#' -#' Estimates the asymptotic variance based on Corollary 1 -#' of Vihola et al. (2020) from weighted samples from IS-MCMC. -#' -#' @param x Vector of samples. -#' @param w Vector of weights. +# Asymptotic Variance of IS-type Estimators +# +# Estimates the asymptotic variance based on Corollary 1 +# of Vihola et al. (2020) from weighted samples from IS-MCMC. +# +# @param x Vector of samples. +# @param w Vector of weights. asymptotic_var <- function(x, w) { estimate_c <- mean(w) estimate_mean <- weighted_mean(x, w) diff --git a/man/as_draws.Rd b/man/as_draws.Rd index 1489bc55..db54b00f 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -32,8 +32,9 @@ model <- bsm_lg(Nile, a1 = 1000, P1 = 500^2) fit1 <- run_mcmc(model, iter = 2e4) +library("posterior") head(as_draws(fit1), 4) -posterior::ess_bulk(draws$sd_y) +ess_bulk(draws$sd_y) summary(fit1, return_se = TRUE) # More chains: @@ -42,12 +43,12 @@ fit2 <- run_mcmc(model, iter = 2e4) model$theta[] <- c(150, 50) # change initial value fit3 <- run_mcmc(model, iter = 2e4) -draws <- posterior::bind_draws(as_draws(fit1), +draws <- bind_draws(as_draws(fit1), as_draws(fit2), as_draws(fit3), along = "chain") # it is actually enough to transform first mcmc_output to draws object, # rest are transformed automatically inside bind_draws -posterior::rhat(draws$sd_y) -posterior::ess_bulk(draws$sd_y) -posterior::ess_tail(draws$sd_y) +rhat(draws$sd_y) +ess_bulk(draws$sd_y) +ess_tail(draws$sd_y) } diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd deleted file mode 100644 index 729db956..00000000 --- a/man/asymptotic_var.Rd +++ /dev/null @@ -1,17 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{asymptotic_var} -\alias{asymptotic_var} -\title{Asymptotic Variance of IS-type Estimators} -\usage{ -asymptotic_var(x, w) -} -\arguments{ -\item{x}{Vector of samples.} - -\item{w}{Vector of weights.} -} -\description{ -Estimates the asymptotic variance based on Corollary 1 -of Vihola et al. (2020) from weighted samples from IS-MCMC. -} diff --git a/man/iact.Rd b/man/iact.Rd deleted file mode 100644 index 8d59faf3..00000000 --- a/man/iact.Rd +++ /dev/null @@ -1,12 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R -\name{iact} -\alias{iact} -\title{Integrated Autocorrelation Time} -\usage{ -iact(x) -} -\description{ -Here IACT is based on Sokal, -Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms -} From cbacbaf11b1cad2a6a02fc5e7779618d58cfab6f Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 18 Sep 2021 00:06:27 +0300 Subject: [PATCH 087/180] fix draws example --- R/as_draws.R | 3 ++- man/as_draws.Rd | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/R/as_draws.R b/R/as_draws.R index 6704c7c3..402366c4 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -22,7 +22,8 @@ #' #' fit1 <- run_mcmc(model, iter = 2e4) #' library("posterior") -#' head(as_draws(fit1), 4) +#' draws <- as_draws(fit1) +#' head(draws, 4) #' ess_bulk(draws$sd_y) #' summary(fit1, return_se = TRUE) #' diff --git a/man/as_draws.Rd b/man/as_draws.Rd index db54b00f..fbab327f 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -33,7 +33,8 @@ model <- bsm_lg(Nile, fit1 <- run_mcmc(model, iter = 2e4) library("posterior") -head(as_draws(fit1), 4) +draws <- as_draws(fit1) +head(draws, 4) ess_bulk(draws$sd_y) summary(fit1, return_se = TRUE) From 9403343dc022db6c7a9471aa5e68099afe6a0db4 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 18 Sep 2021 09:32:36 +0300 Subject: [PATCH 088/180] fix model name --- R/cpp_example_models.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 72314352..294c8206 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -9,7 +9,7 @@ #' of \code{return_code = TRUE}, returns the example code without compiling. #' @export #' @examples -#' cpp_example_model("poisson_OU", return_code = TRUE) +#' cpp_example_model("sde_poisson_OU", return_code = TRUE) #' cpp_example_model <- function(example, return_code = FALSE) { From 33d6538bd050e4fd36a8d58576776914d842de70 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 18 Sep 2021 11:16:23 +0300 Subject: [PATCH 089/180] update docs --- R/approx.R | 16 +++++++++++----- R/bootstrap_filter.R | 6 +++--- R/importance_sample.R | 8 ++------ R/loglik.R | 18 ++++++++++++------ R/models.R | 3 ++- R/particle_smoother.R | 16 ++++++++-------- R/priors.R | 20 ++++++++++++++------ R/run_mcmc.R | 6 ++++-- man/bootstrap_filter.Rd | 6 +++--- man/bsm_lg.Rd | 3 ++- man/bsm_ng.Rd | 3 ++- man/bssm_prior.Rd | 20 ++++++++++++++------ man/cpp_example_model.Rd | 2 +- man/gaussian_approx.Rd | 16 +++++++++++----- man/importance_sample.Rd | 15 +++++++++------ man/logLik.nongaussian.Rd | 9 ++++++--- man/logLik.ssm_nlg.Rd | 9 ++++++--- man/particle_smoother.Rd | 16 +++++++++------- man/run_mcmc_ng.Rd | 6 ++++-- 19 files changed, 123 insertions(+), 75 deletions(-) diff --git a/R/approx.R b/R/approx.R index 665004dd..c890d672 100644 --- a/R/approx.R +++ b/R/approx.R @@ -5,11 +5,17 @@ #' This function is rarely needed itself, and is mainly available for #' testing and debugging purposes. #' -#' @param model Model to be approximated. -#' @param max_iter Maximum number of iterations. -#' @param conv_tol Tolerance parameter. -#' @param iekf_iter For non-linear models, number of iterations in iterated EKF -#' (defaults to 0). +#' @param model Model to be approximated. Should be of class +#' \code{bsm_ng}, \code{ar1_ng} \code{svm}, +#' \code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +#' non-linear \code{bssm_model}. +#' @param max_iter Maximum number of iterations as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' @param conv_tol Positive tolerance parameter. Default is 1e-8. Approximation +#' is claimed to be converged when the mean squared difference of the modes of +#' is less than \code{conv_tol}. +#' @param iekf_iter For non-linear models, non-negative number of iterations in +#' iterated EKF (defaults to 0). #' @param ... Ignored. #' @return Returns linear-Gaussian SSM of class \code{ssm_ulg} or #' \code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 660e209f..a3501ca8 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -2,8 +2,8 @@ #' #' Function \code{bootstrap_filter} performs a bootstrap filtering with #' stratification resampling. -#' @param model of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}. -#' @param particles Number of particles. +#' @param model A model object of class \code{bssm_model}. +#' @param particles Number of particles as a positive integer. #' @param seed Seed for RNG. #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the filtering distribution and @@ -146,7 +146,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, #' @method bootstrap_filter ssm_sde #' @rdname bootstrap_filter -#' @param L Integer defining the discretization level for SDE models. +#' @param L Positive integer defining the discretization level for SDE models. #' @export bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { diff --git a/R/importance_sample.R b/R/importance_sample.R index 24c34ed9..6c5a2d1d 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -4,16 +4,12 @@ #' corresponding (scaled) importance weights. #' Probably mostly useful for comparing KFAS and bssm packages. #' -#' @param model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, -#' \code{ssm_ung}, or \code{ssm_mng}. +#' +#' @inheritParams gaussian_approx #' @param nsim Number of samples. #' @param use_antithetic Logical. If \code{TRUE} (default), use antithetic #' variable for location in simulation smoothing. Ignored for \code{ssm_mng} #' models. -#' @param max_iter Maximum number of iterations used for the approximation. -#' @param conv_tol Convergence threshold for the approximation. Approximation -#' is claimed to be converged when the mean squared difference of the modes is -#' less than \code{conv_tol}. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @export diff --git a/R/loglik.R b/R/loglik.R index d848bd88..7678f696 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -29,9 +29,12 @@ logLik.gaussian <- function(object, ...) { #' (\code{"psi"}). Other choices are \code{"bsf"} bootstrap particle filter, #' and \code{"spdk"}, which uses the importance sampling approach by #' Shephard and Pitt (1997) and Durbin and Koopman (1997). -#' @param max_iter Maximum number of iterations for Gaussian approximation -#' algorithm. -#' @param conv_tol Tolerance parameter for the approximation algorithm. +#' @param max_iter Maximum number of iterations used in Gaussian approximation, +#' as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' Used in \eqn{\psi}-APF. +#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. +#' Default is 1e-8. Used \eqn{\psi}-APF. #' @param seed Seed for the random number generator. #' @param ... Ignored. #' @method logLik nongaussian @@ -93,9 +96,12 @@ logLik.nongaussian <- function(object, particles, method = "psi", #' psi-auxiliary filter (or approximating Gaussian model in the case of #' \code{particles = 0}), and \code{"ekf"} which uses EKF-based particle #' filter (or just EKF approximation in the case of \code{particles = 0}). -#' @param max_iter Maximum number of iterations for the gaussian approximation -#' algorithm. -#' @param conv_tol Tolerance parameter for the approximation algorithm. +#' @param max_iter Maximum number of iterations used in Gaussian approximation, +#' as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' Used in \eqn{\psi}-APF. +#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. +#' Default is 1e-8. Used \eqn{\psi}-APF. #' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter #' is used with #' \code{iekf_iter} iterations in place of standard EKF. Defaults to zero. diff --git a/R/models.R b/R/models.R index 28f955c4..e6b708c9 100644 --- a/R/models.R +++ b/R/models.R @@ -813,7 +813,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' @param xreg Matrix containing covariates with number of rows matching the #' length of \code{y}. #' @param period Length of the seasonal pattern. -#' Default is \code{frequency(y)}. Must be at least 3. +#' Default is \code{frequency(y)}. Must be a positive integer greater than 2 +#' and less than the length of the input time series. #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. #' @param P1 Prior covariance for the initial states (level, slope, seasonals). diff --git a/R/particle_smoother.R b/R/particle_smoother.R index a342b537..81dfc031 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -9,8 +9,7 @@ #' See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. #' #' @importFrom stats cov -#' @param model Model. -#' @param particles Number of samples for particle filter. +#' @inheritParams bootstrap_filter #' @param method Choice of particle filter algorithm. #' For Gaussian and non-Gaussian models with linear dynamics, #' options are \code{"bsf"} (bootstrap particle filter, default for @@ -18,15 +17,16 @@ #' and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and #' for non-linear models options \code{"ekf"} (extended Kalman particle filter) #' is also available. -#' @param max_iter Maximum number of iterations used in Gaussian approximation. -#' Used \eqn{\psi}-APF. -#' @param conv_tol Tolerance parameter used in Gaussian approximation. -#' Used \eqn{\psi}-APF. +#' @param max_iter Maximum number of iterations used in Gaussian approximation, +#' as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' Used in \eqn{\psi}-APF. +#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. +#' Default is 1e-8. Used \eqn{\psi}-APF. #' @param iekf_iter If zero (default), first approximation for non-linear #' Gaussian models is obtained from extended Kalman filter. If #' \code{iekf_iter > 0}, iterated extended Kalman filter is used with #' \code{iekf_iter} iterations. -#' @param seed Seed for RNG. #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the smoothing distribution #' and corresponding weights (\code{weights}), @@ -230,7 +230,7 @@ particle_smoother.ssm_nlg <- function(model, particles, #' @rdname particle_smoother #' @method particle_smoother ssm_sde -#' @param L Integer defining the discretization level. +#' @param L Positive integer defining the discretization level for SDE model. #' @export particle_smoother.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { diff --git a/R/priors.R b/R/priors.R index 48496513..7d613621 100644 --- a/R/priors.R +++ b/R/priors.R @@ -20,19 +20,27 @@ #' components and as a starting values in MCMC. #' @param min Lower bound of the uniform and truncated normal prior. #' @param max Upper bound of the uniform and truncated normal prior. -#' @param sd Standard deviation of the (underlying i.e. non-truncated) -#' Normal distribution. +#' @param sd Positive value defining the standard deviation of the +#' (underlying i.e. non-truncated) Normal distribution. #' @param mean Mean of the Normal prior. -#' @param shape Shape parameter of the Gamma prior. -#' @param rate Rate parameter of the Gamma prior. +#' @param shape Positive shape parameter of the Gamma prior. +#' @param rate Positive rate parameter of the Gamma prior. #' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case #' of multiple priors (i.e. multiple regression coefficients). #' @export #' @examples #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: -#' uniform(0.2, -1, 1) +#' uniform(0.2, -1.0, 1.0) #' # two normal priors at once i.e. for coefficients beta: -#' normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) +#' normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) +#' # Gamma +#' gamma(init = 0.1, shape = 2.5, rate = 1.1) +#' # Same as +#' gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) +#' # Half-normal +#' halfnormal(init = 0.01, sd = 0.1) +#' # Truncated normal +#' tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") diff --git a/R/run_mcmc.R b/R/run_mcmc.R index d77be66f..0a9deabe 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -227,8 +227,10 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' Note that parallel computing is only used in the post-correction phase of #' IS-MCMC and when sampling the states in case of approximate models. #' @param seed Seed for the random number generator. -#' @param max_iter Maximum number of iterations used in Gaussian approximation. -#' @param conv_tol Tolerance parameter used in Gaussian approximation. +#' @param max_iter Maximum number of iterations used in Gaussian approximation, +#' as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. #' @param ... Ignored. #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index d45257e2..16dba8de 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -40,15 +40,15 @@ bootstrap_filter(model, particles, ...) ) } \arguments{ -\item{model}{of class \code{bsm_lg}, \code{bsm_ng} or \code{svm}.} +\item{model}{A model object of class \code{bssm_model}.} -\item{particles}{Number of particles.} +\item{particles}{Number of particles as a positive integer.} \item{...}{Ignored.} \item{seed}{Seed for RNG.} -\item{L}{Integer defining the discretization level for SDE models.} +\item{L}{Positive integer defining the discretization level for SDE models.} } \value{ List with samples (\code{alpha}) from the filtering distribution and diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 8dad2f35..9cd12f64 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -47,7 +47,8 @@ Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} length of \code{y}.} \item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be at least 3.} +Default is \code{frequency(y)}. Must be a positive integer greater than 2 +and less than the length of the input time series.} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 8bdeb70d..57fbd788 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -64,7 +64,8 @@ Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} length of \code{y}.} \item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be at least 3.} +Default is \code{frequency(y)}. Must be a positive integer greater than 2 +and less than the length of the input time series.} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index 1d8dc95f..30bd9b13 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -42,14 +42,14 @@ components and as a starting values in MCMC.} \item{max}{Upper bound of the uniform and truncated normal prior.} -\item{sd}{Standard deviation of the (underlying i.e. non-truncated) -Normal distribution.} +\item{sd}{Positive value defining the standard deviation of the +(underlying i.e. non-truncated) Normal distribution.} \item{mean}{Mean of the Normal prior.} -\item{shape}{Shape parameter of the Gamma prior.} +\item{shape}{Positive shape parameter of the Gamma prior.} -\item{rate}{Rate parameter of the Gamma prior.} +\item{rate}{Positive rate parameter of the Gamma prior.} } \value{ object of class \code{bssm_prior} or \code{bssm_prior_list} in case @@ -73,7 +73,15 @@ are just for consistent naming). } \examples{ # create uniform prior on [-1, 1] for one parameter with initial value 0.2: -uniform(0.2, -1, 1) +uniform(0.2, -1.0, 1.0) # two normal priors at once i.e. for coefficients beta: -normal(init = c(0.1, 2), mean = 0, sd = c(1, 2)) +normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) +# Gamma +gamma(init = 0.1, shape = 2.5, rate = 1.1) +# Same as +gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) +# Half-normal +halfnormal(init = 0.01, sd = 0.1) +# Truncated normal +tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) } diff --git a/man/cpp_example_model.Rd b/man/cpp_example_model.Rd index cc92bfe3..124d5e17 100644 --- a/man/cpp_example_model.Rd +++ b/man/cpp_example_model.Rd @@ -21,6 +21,6 @@ of \code{return_code = TRUE}, returns the example code without compiling. Example C++ Codes for Non-Linear and SDE Models } \examples{ -cpp_example_model("poisson_OU", return_code = TRUE) +cpp_example_model("sde_poisson_OU", return_code = TRUE) } diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index b79e627b..8bb9336f 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -13,16 +13,22 @@ gaussian_approx(model, max_iter, conv_tol, ...) \method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) } \arguments{ -\item{model}{Model to be approximated.} +\item{model}{Model to be approximated. Should be of class +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +non-linear \code{bssm_model}.} -\item{max_iter}{Maximum number of iterations.} +\item{max_iter}{Maximum number of iterations as a positive integer. +Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Tolerance parameter.} +\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation +is claimed to be converged when the mean squared difference of the modes of +is less than \code{conv_tol}.} \item{...}{Ignored.} -\item{iekf_iter}{For non-linear models, number of iterations in iterated EKF -(defaults to 0).} +\item{iekf_iter}{For non-linear models, non-negative number of iterations in +iterated EKF (defaults to 0).} } \value{ Returns linear-Gaussian SSM of class \code{ssm_ulg} or diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index ec867821..eea9f4fb 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -18,8 +18,10 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) ) } \arguments{ -\item{model}{of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, -\code{ssm_ung}, or \code{ssm_mng}.} +\item{model}{Model to be approximated. Should be of class +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +non-linear \code{bssm_model}.} \item{nsim}{Number of samples.} @@ -27,11 +29,12 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) variable for location in simulation smoothing. Ignored for \code{ssm_mng} models.} -\item{max_iter}{Maximum number of iterations used for the approximation.} +\item{max_iter}{Maximum number of iterations as a positive integer. +Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Convergence threshold for the approximation. Approximation -is claimed to be converged when the mean squared difference of the modes is -less than \code{conv_tol}.} +\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation +is claimed to be converged when the mean squared difference of the modes of +is less than \code{conv_tol}.} \item{seed}{Seed for the random number generator.} diff --git a/man/logLik.nongaussian.Rd b/man/logLik.nongaussian.Rd index 23c1092b..0798a399 100644 --- a/man/logLik.nongaussian.Rd +++ b/man/logLik.nongaussian.Rd @@ -26,10 +26,13 @@ approximate log-likelihood based on the Gaussian approximation is returned.} and \code{"spdk"}, which uses the importance sampling approach by Shephard and Pitt (1997) and Durbin and Koopman (1997).} -\item{max_iter}{Maximum number of iterations for Gaussian approximation -algorithm.} +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed). +Used in \eqn{\psi}-APF.} -\item{conv_tol}{Tolerance parameter for the approximation algorithm.} +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +Default is 1e-8. Used \eqn{\psi}-APF.} \item{seed}{Seed for the random number generator.} diff --git a/man/logLik.ssm_nlg.Rd b/man/logLik.ssm_nlg.Rd index cdfdbfa9..9cd321ad 100644 --- a/man/logLik.ssm_nlg.Rd +++ b/man/logLik.ssm_nlg.Rd @@ -28,10 +28,13 @@ psi-auxiliary filter (or approximating Gaussian model in the case of \code{particles = 0}), and \code{"ekf"} which uses EKF-based particle filter (or just EKF approximation in the case of \code{particles = 0}).} -\item{max_iter}{Maximum number of iterations for the gaussian approximation -algorithm.} +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed). +Used in \eqn{\psi}-APF.} -\item{conv_tol}{Tolerance parameter for the approximation algorithm.} +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +Default is 1e-8. Used \eqn{\psi}-APF.} \item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is used with diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 6539b7e8..f6edce33 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -48,9 +48,9 @@ particle_smoother(model, particles, ...) ) } \arguments{ -\item{model}{Model.} +\item{model}{A model object of class \code{bssm_model}.} -\item{particles}{Number of samples for particle filter.} +\item{particles}{Number of particles as a positive integer.} \item{...}{Ignored.} @@ -64,18 +64,20 @@ is also available.} \item{seed}{Seed for RNG.} -\item{max_iter}{Maximum number of iterations used in Gaussian approximation. -Used \eqn{\psi}-APF.} +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed). +Used in \eqn{\psi}-APF.} -\item{conv_tol}{Tolerance parameter used in Gaussian approximation. -Used \eqn{\psi}-APF.} +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +Default is 1e-8. Used \eqn{\psi}-APF.} \item{iekf_iter}{If zero (default), first approximation for non-linear Gaussian models is obtained from extended Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is used with \code{iekf_iter} iterations.} -\item{L}{Integer defining the discretization level.} +\item{L}{Positive integer defining the discretization level for SDE model.} } \value{ List with samples (\code{alpha}) from the smoothing distribution diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd index 32975c72..3ebb8808 100644 --- a/man/run_mcmc_ng.Rd +++ b/man/run_mcmc_ng.Rd @@ -96,9 +96,11 @@ Note that parallel computing is only used in the post-correction phase of \item{seed}{Seed for the random number generator.} -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation.} \item{...}{Ignored.} } From 8ccbd98758b8c172deb237ba22d7357769ccd5b5 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 00:23:47 +0300 Subject: [PATCH 090/180] update docs --- R/approx.R | 3 +- R/bootstrap_filter.R | 6 +- R/check_arguments.R | 7 +- R/ekpf_filter.R | 10 +- R/importance_sample.R | 8 +- R/kfilter.R | 12 +- R/loglik.R | 118 ++++++------- R/models.R | 25 +-- R/particle_smoother.R | 20 ++- R/post_correction.R | 8 +- R/predict.R | 4 +- R/priors.R | 19 +- R/run_mcmc.R | 346 ++++++++++++------------------------- R/sim_smoother.R | 19 +- R/smoother.R | 6 +- man/bootstrap_filter.Rd | 2 +- man/bssm_prior.Rd | 19 +- man/ekf.Rd | 7 +- man/ekf_smoother.Rd | 7 +- man/ekpf_filter.Rd | 8 +- man/gaussian_approx.Rd | 3 +- man/importance_sample.Rd | 10 +- man/kfilter.Rd | 3 +- man/logLik.Rd | 21 --- man/logLik.nongaussian.Rd | 62 ------- man/logLik.ssm_nlg.Rd | 50 ------ man/logLik.ssm_sde.Rd | 29 ---- man/particle_smoother.Rd | 17 +- man/post_correct.Rd | 2 +- man/predict.mcmc_output.Rd | 2 +- man/run_mcmc.Rd | 343 ++++++++++++++++++++++++++++++++++-- man/run_mcmc.ssm_nlg.Rd | 110 ------------ man/run_mcmc.ssm_sde.Rd | 95 ---------- man/run_mcmc_g.Rd | 95 ---------- man/run_mcmc_ng.Rd | 226 ------------------------ man/sim_smoother.Rd | 20 ++- man/suggest_N.Rd | 2 +- man/ukf.Rd | 2 +- 38 files changed, 649 insertions(+), 1097 deletions(-) delete mode 100644 man/logLik.Rd delete mode 100644 man/logLik.nongaussian.Rd delete mode 100644 man/logLik.ssm_nlg.Rd delete mode 100644 man/logLik.ssm_sde.Rd delete mode 100644 man/run_mcmc.ssm_nlg.Rd delete mode 100644 man/run_mcmc.ssm_sde.Rd delete mode 100644 man/run_mcmc_g.Rd delete mode 100644 man/run_mcmc_ng.Rd diff --git a/R/approx.R b/R/approx.R index c890d672..1887a7ba 100644 --- a/R/approx.R +++ b/R/approx.R @@ -15,7 +15,8 @@ #' is claimed to be converged when the mean squared difference of the modes of #' is less than \code{conv_tol}. #' @param iekf_iter For non-linear models, non-negative number of iterations in -#' iterated EKF (defaults to 0). +#' iterated EKF (defaults to 0, i.e. normal EKF). Used only for models of class +#' \code{ssm_nlg}. #' @param ... Ignored. #' @return Returns linear-Gaussian SSM of class \code{ssm_ulg} or #' \code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index a3501ca8..f676bc78 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -4,7 +4,7 @@ #' stratification resampling. #' @param model A model object of class \code{bssm_model}. #' @param particles Number of particles as a positive integer. -#' @param seed Seed for RNG. +#' @param seed Seed for RNG (non-negative integer). #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the filtering distribution and #' corresponding weights (\code{weights}), as well as filtered and predicted @@ -44,6 +44,7 @@ bootstrap_filter.gaussian <- function(model, particles, } } particles <- check_integer(particles, "particles") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles @@ -86,6 +87,7 @@ bootstrap_filter.nongaussian <- function(model, particles, } } particles <- check_integer(particles, "particles") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles if (particles > 100 & nsamples > 1e12) { @@ -129,6 +131,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, @@ -170,6 +173,7 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, diff --git a/R/check_arguments.R b/R/check_arguments.R index e25dec90..86f22bd7 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -175,13 +175,12 @@ check_prior <- function(x, name) { } } -check_target <- function(target) { - if (length(target) > 1 || target >= 1 || target <= 0) { - stop("Argument 'target' must be on interval (0, 1).") +check_prop <- function(x, name = "target") { + if (length(x) > 1 || x >= 1 || x <= 0) { + stop(paste0("Argument '", name, "' must be on interval (0, 1).")) } } - check_D <- function(x, p, n) { if (missing(x) || is.null(x)) { x <- if (p == 1) 0 else matrix(0, p, 1) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 2cf9f237..3bc41a63 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -3,9 +3,9 @@ #' Function \code{ekpf_filter} performs a extended Kalman particle filtering #' with stratification resampling, based on Van Der Merwe et al (2001). #' -#' @param object of class \code{ssm_nlg}. -#' @param particles Number of particles. -#' @param seed Seed for RNG. +#' @param object Model of class \code{ssm_nlg}. +#' @param particles Number of particles as a positive integer. +#' @param seed Seed for RNG (positive integer). #' @param ... Ignored. #' @return A list containing samples, filtered estimates and the #' corresponding covariances, weights, and an estimate of log-likelihood. @@ -40,7 +40,7 @@ ekpf_filter <- function(object, particles, ...) { #' log_prior_pdf = pntrs$log_prior_pdf, #' n_states = 1, n_etas = 1, state_names = "state") #' -#' out <- ekpf_filter(model_nlg, 100) +#' out <- ekpf_filter(model_nlg, particles = 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) #' ekpf_filter.ssm_nlg <- function(object, particles, @@ -63,6 +63,8 @@ ekpf_filter.ssm_nlg <- function(object, particles, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + out <- ekpf(t(object$y), object$Z, object$H, object$T, object$R, object$Z_gn, object$T_gn, object$a1, object$P1, object$theta, object$log_prior_pdf, object$known_params, diff --git a/R/importance_sample.R b/R/importance_sample.R index 6c5a2d1d..8f35e12b 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -6,11 +6,13 @@ #' #' #' @inheritParams gaussian_approx -#' @param nsim Number of samples. +#' @param model Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +#' \code{ssm_ung}, or \code{ssm_mng}. +#' @param nsim Number of samples (positive integer). #' @param use_antithetic Logical. If \code{TRUE} (default), use antithetic #' variable for location in simulation smoothing. Ignored for \code{ssm_mng} #' models. -#' @param seed Seed for the random number generator. +#' @param seed Seed for the random number generator (positive integer). #' @param ... Ignored. #' @export #' @rdname importance_sample @@ -51,7 +53,7 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, warning(paste("Trying to sample ", nsamples, "values, you might run out of memory.")) } - + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 diff --git a/R/kfilter.R b/R/kfilter.R index cd0b7309..29d3f32b 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -7,7 +7,8 @@ #' For non-Gaussian models, the filtering is based on the approximate #' Gaussian model. #' -#' @param model Model Model object. +#' @param model Model of class \code{gaussian}, \code{nongaussian} or +#' \code{ssm_nlg}. #' @param ... Ignored. #' @return List containing the log-likelihood #' (approximate in non-Gaussian case), one-step-ahead predictions \code{at} @@ -52,9 +53,10 @@ kfilter.nongaussian <- function(model, ...) { #' and returns the filtered estimates and one-step-ahead predictions of the #' states \eqn{\alpha_t} given the data up to time \eqn{t}. #' -#' @param model Model model -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter -#' is used with \code{iekf_iter} iterations. +#' @param model Model of class \code{ssm_nlg}. +#' @param iekf_iter Non-negative integer. The default zero corresponds to +#' normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +#' with \code{iekf_iter} iterations. #' @return List containing the log-likelihood, #' one-step-ahead predictions \code{at} and filtered #' estimates \code{att} of states, and the corresponding variances \code{Pt} and @@ -114,7 +116,7 @@ ekf <- function(model, iekf_iter = 0) { #' and returns the filtered estimates and one-step-ahead predictions of the #' states \eqn{\alpha_t} given the data up to time \eqn{t}. #' -#' @param model Model model +#' @param model Model of class \code{ssm_nlg}. #' @param alpha Positive tuning parameter of the UKF. Default is 0.001. Smaller #' the value, closer the sigma point are to the mean of the state. #' @param beta Non-negative tuning parameter of the UKF. The default value is diff --git a/R/loglik.R b/R/loglik.R index 7678f696..daea8748 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -1,56 +1,65 @@ -#' Log-likelihood of a Gaussian State Space Model +#' Extract Log-likelihood of a State Space Model of class \code{bssm_model} #' -#' Computes the log-likelihood of a linear-Gaussian state space model of -#' \code{bssm} package. +#' Computes the log-likelihood of a state space model defined by \code{bssm} +#' package. #' -#' @param object Model model. -#' @param ... Ignored. +#' @inheritParams particle_smoother +#' @param particles Number of samples for particle filter. If 0, +#' approximate log-likelihood is returned either based on the Gaussian +#' approximation or EKF, depending on the \code{method} argument. +#' @param method Sampling method. For Gaussian and non-Gaussian models with +#' linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default +#' for non-linear models) +#' and \code{"psi"} (\eqn{\psi}-APF, the default for other models). +#' For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle +#' filter (or just EKF/IEKF approximation in the case of \code{particles = 0}). #' @importFrom stats logLik #' @method logLik gaussian -#' @rdname logLik +#' @rdname logLik_bssm +#' @seealso particle_smoother #' @export -#' @examples +#' @references +#' Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation +#' Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. +#' +#' Shephard, N., & Pitt, M. (1997). Likelihood Analysis of +#' Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. +#' +#' Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +#' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +#' IEE Proceedings-F, 140, 107-113. +#' +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +#' +#' Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +#' The unscented particle filter. +#' In Advances in neural information processing systems, p 584-590. +#' +#' Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +#' Academic Press. +#' +#' Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +#' nonlinear state space models. +#' Journal of Computational and Graphical Statistics, 5, 1-25. +#' @examples #' model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) #' logLik(model) logLik.gaussian <- function(object, ...) { gaussian_loglik(object, model_type(object)) } -#' Log-likelihood of a Non-Gaussian State Space Model -#' -#' Computes the log-likelihood of a non-Gaussian state space model of -#' \code{bssm} package. -#' -#' @param object Model model. -#' @param particles Number of samples for particle filter or -#' importance sampling. If 0, -#' approximate log-likelihood based on the Gaussian approximation is returned. -#' @param method Sampling method, default is psi-auxiliary filter -#' (\code{"psi"}). Other choices are \code{"bsf"} bootstrap particle filter, -#' and \code{"spdk"}, which uses the importance sampling approach by -#' Shephard and Pitt (1997) and Durbin and Koopman (1997). -#' @param max_iter Maximum number of iterations used in Gaussian approximation, -#' as a positive integer. -#' Default is 100 (although typically only few iterations are needed). -#' Used in \eqn{\psi}-APF. -#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' Default is 1e-8. Used \eqn{\psi}-APF. -#' @param seed Seed for the random number generator. -#' @param ... Ignored. #' @method logLik nongaussian +#' @rdname logLik_bssm #' @export -#' @references -#' Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation -#' Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. -#' -#' Shephard, N., & Pitt, M. (1997). Likelihood Analysis of -#' Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. -#' @examples +#' @examples #' model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, #' distribution = "poisson") #' #' model2 <- bsm_ng(y = c(1,4,3), sd_level = 0.5, P1 = 2, #' distribution = "poisson") +#' #' logLik(model, particles = 0) #' logLik(model2, particles = 0) #' logLik(model, particles = 10, seed = 1) @@ -82,32 +91,8 @@ logLik.nongaussian <- function(object, particles, method = "psi", nongaussian_loglik(object, particles, method, seed, model_type(object)) } -#' Log-likelihood of a Non-linear State Space Model -#' -#' Computes the log-likelihood of a state space model of class -#' \code{ssm_nlg} package. -#' -#' @param object Model model. -#' @param particles Number of samples for particle filter. If 0, -#' approximate log-likelihood is returned either based on the Gaussian -#' approximation or EKF, depending on the \code{method} argument. -#' @param method Sampling method. Default is the bootstrap particle filter -#' (\code{"bsf"}). Other choices are \code{"psi"} which uses -#' psi-auxiliary filter (or approximating Gaussian model in the case of -#' \code{particles = 0}), and \code{"ekf"} which uses EKF-based particle -#' filter (or just EKF approximation in the case of \code{particles = 0}). -#' @param max_iter Maximum number of iterations used in Gaussian approximation, -#' as a positive integer. -#' Default is 100 (although typically only few iterations are needed). -#' Used in \eqn{\psi}-APF. -#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' Default is 1e-8. Used \eqn{\psi}-APF. -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter -#' is used with -#' \code{iekf_iter} iterations in place of standard EKF. Defaults to zero. -#' @param seed Seed for the random number generator. -#' @param ... Ignored. #' @method logLik ssm_nlg +#' @rdname logLik_bssm #' @export logLik.ssm_nlg <- function(object, particles, method = "bsf", max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, @@ -127,6 +112,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", stop("'particles' must be positive for bootstrap particle filter.") method <- pmatch(method, c("psi", "bsf", NA, "ekf")) + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + nonlinear_loglik(t(object$y), object$Z, object$H, object$T, object$R, object$Z_gn, object$T_gn, object$a1, object$P1, object$theta, object$log_prior_pdf, object$known_params, @@ -134,17 +121,9 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", as.integer(object$time_varying), particles, seed, max_iter, conv_tol, iekf_iter, method) } -#' Log-likelihood of a State Space Model with SDE dynamics -#' -#' Computes the log-likelihood of a state space model of class -#' \code{ssm_sde} package. -#' -#' @param object Model model. -#' @param particles Number of samples for particle filter. #' @param L Integer defining the discretization level defined as (2^L). -#' @param seed Seed for the random number generator. -#' @param ... Ignored. #' @method logLik ssm_sde +#' @rdname logLik_bssm #' @export logLik.ssm_sde <- function(object, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { @@ -157,6 +136,7 @@ logLik.ssm_sde <- function(object, particles, L, particles <- nsim } } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) loglik_sde(object$y, object$x0, object$positive, object$drift, object$diffusion, object$ddiffusion, object$prior_pdf, object$obs_pdf, object$theta, diff --git a/R/models.R b/R/models.R index e6b708c9..34d84479 100644 --- a/R/models.R +++ b/R/models.R @@ -1445,26 +1445,31 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' #' @param y Observations as multivariate time series (or matrix) of length #' \eqn{n}. -#' @param Z,H,T,R An external pointers for the C++ functions which -#' define the corresponding model functions. -#' @param Z_gn,T_gn An external pointers for the C++ functions which -#' define the gradients of the corresponding model functions. -#' @param a1 Prior mean for the initial state as a vector of length m. -#' @param P1 Prior covariance matrix for the initial state as m x m matrix. +#' @param Z,H,T,R An external pointers (object of class \code{externalptr}) for the +#' C++ functions which define the corresponding model functions. +#' @param Z_gn,T_gn An external pointers (object of class \code{externalptr}) for +#' the C++ functions which define the gradients of the corresponding model +#' functions. +#' @param a1 Prior mean for the initial state as object of class +#' \code{externalptr} +#' @param P1 Prior covariance matrix for the initial state as object of class +#' \code{externalptr} #' @param theta Parameter vector passed to all model functions. #' @param known_params Vector of known parameters passed to all model #' functions. #' @param known_tv_params Matrix of known parameters passed to all model #' functions. -#' @param n_states Number of states in the model. -#' @param n_etas Dimension of the noise term of the transition equation. -#' @param log_prior_pdf An external pointer for the C++ function which +#' @param n_states Number of states in the model (positive integer). +#' @param n_etas Dimension of the noise term of the transition equation +#' (positive integer). +#' @param log_prior_pdf An external pointer (object of class +#' \code{externalptr}) for the C++ function which #' computes the log-prior density given theta. #' @param time_varying Optional logical vector of length 4, denoting whether #' the values of #' Z, H, T, and R vary with respect to time variable (given identical states). #' If used, this can speed up some computations. -#' @param state_names Names for the states. +#' @param state_names Vector containing names for the states. #' @return Object of class \code{ssm_nlg}. #' @export #' @examples diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 81dfc031..7a5a01a8 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -15,18 +15,17 @@ #' options are \code{"bsf"} (bootstrap particle filter, default for #' non-linear models) #' and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and -#' for non-linear models options \code{"ekf"} (extended Kalman particle filter) +#' for non-linear models option \code{"ekf"} (extended Kalman particle filter) #' is also available. #' @param max_iter Maximum number of iterations used in Gaussian approximation, #' as a positive integer. #' Default is 100 (although typically only few iterations are needed). -#' Used in \eqn{\psi}-APF. #' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' Default is 1e-8. Used \eqn{\psi}-APF. -#' @param iekf_iter If zero (default), first approximation for non-linear -#' Gaussian models is obtained from extended Kalman filter. If -#' \code{iekf_iter > 0}, iterated extended Kalman filter is used with -#' \code{iekf_iter} iterations. +#' Default is 1e-8. +#' @param iekf_iter Non-negative integer. If zero (default), first +#' approximation for non-linear Gaussian models is obtained from extended +#' Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is +#' used with \code{iekf_iter} iterations. #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the smoothing distribution #' and corresponding weights (\code{weights}), @@ -92,6 +91,8 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (method == "psi") { out <- list() out$alpha <- gaussian_psi_smoother(model, particles, seed, @@ -143,6 +144,8 @@ particle_smoother.nongaussian <- function(model, particles, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -191,7 +194,7 @@ particle_smoother.ssm_nlg <- function(model, particles, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) @@ -251,6 +254,7 @@ particle_smoother.ssm_sde <- function(model, particles, L, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_smoother_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, diff --git a/R/post_correction.R b/R/post_correction.R index fd26b6b9..77b98024 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -25,7 +25,7 @@ get_map <- function(x) { #' test. Default is \code{seq(10, 100, by = 10)}. #' @param replications How many replications should be used for computing #' the standard deviations? Default is 100. -#' @param seed Seed for the random number generator. +#' @param seed Seed for the random number generator (positive integer). #' @return List with suggested number of particles \code{N} and matrix #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. @@ -81,6 +81,8 @@ suggest_N <- function(model, theta, seed = sample(.Machine$integer.max, size = 1)) { replications <- check_integer(replications, "replications") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (!test_integerish(candidates, lower = 1, any.missing = FALSE, min.len = 1)) { stop("Argument 'candidates' should be vector of positive integers. ") @@ -145,7 +147,7 @@ suggest_N <- function(model, theta, #' \code{"is1"} for importance sampling type weighting where the number of #' particles used forweight computations is proportional to the length of the #' jump chain block. -#' @param seed Seed for the random number generator. +#' @param seed Seed for the random number generator (positive integer). #' @return List with suggested number of particles \code{N} and matrix #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. @@ -235,6 +237,8 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, particles <- check_integer(particles, "particles") threads <- check_integer(threads, "threads") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") is_type <- pmatch(match.arg(is_type, paste0("is", 1:3)), paste0("is", 1:3)) diff --git a/R/predict.R b/R/predict.R index 62ec6666..c37b86bc 100644 --- a/R/predict.R +++ b/R/predict.R @@ -22,7 +22,7 @@ #' future, using posterior samples of (theta, alpha_T+1) i.e. the #' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. -#' @param seed Seed for RNG. +#' @param seed Seed for RNG (positive integer). #' @param ... Ignored. #' @return A \code{data.frame} consisting of samples from the predictive #' posterior distribution. @@ -122,6 +122,8 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", stop("Argument 'model' should be of class 'bssm_model'. ") } nsim <- check_integer(nsim, "nsim") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") type <- match.arg(type, c("response", "mean", "state")) diff --git a/R/priors.R b/R/priors.R index 7d613621..8778996b 100644 --- a/R/priors.R +++ b/R/priors.R @@ -30,10 +30,10 @@ #' @export #' @examples #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: -#' uniform(0.2, -1.0, 1.0) +#' uniform(init = 0.2, min = -1.0, max = 1.0) #' # two normal priors at once i.e. for coefficients beta: #' normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) -#' # Gamma +#' # Gamma prior #' gamma(init = 0.1, shape = 2.5, rate = 1.1) #' # Same as #' gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) @@ -41,6 +41,21 @@ #' halfnormal(init = 0.01, sd = 0.1) #' # Truncated normal #' tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) +#' +#' # Further examples for diagnostic purposes: +#' uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +#' normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +#' tnormal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +#' halfnormal(c(0, 0.2), c(1.0, 1.2)) +#' gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) +#' +#' # longer versions: +#' uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +#' normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +#' tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +#' halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) +#' gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) +#' uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 0a9deabe..c0cdebb5 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -1,63 +1,117 @@ #' Bayesian Inference of State Space Models #' -#' Adaptive Markov chain Monte Carlo simulation of state space models using -#' Robust Adaptive Metropolis algorithm by Vihola (2012). -#' See specific methods for various model types for details. +#' Adaptive Markov chain Monte Carlo simulation for SSMs using +#' Robust Adaptive Metropolis algorithm by Vihola (2012). Several different +#' MCMC sampling schemes are implemented, see parameter +#' arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and +#' Vihola (2021) for details. #' -#' @importFrom stats tsp -#' @param model State space model model of \code{bssm} package. -#' @param iter Number of MCMC iterations. -#' @param ... Parameters to specific methods. See -#' \code{\link{run_mcmc.gaussian}}, -#' \code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, -#' and \code{\link{run_mcmc.ssm_sde}} for details. -#' @export -#' @rdname run_mcmc -#' @references Matti Vihola (2012). Robust adaptive Metropolis algorithm with -#' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. -run_mcmc <- function(model, iter, ...) { - UseMethod("run_mcmc", model) -} -#' Bayesian Inference of Linear-Gaussian State Space Models -#' -#' @method run_mcmc gaussian -#' @rdname run_mcmc_g -#' @param model Model model. -#' @param iter Number of MCMC iterations. -#' @param output_type Type of output. Default is \code{"full"}, which returns -#' samples from the posterior \eqn{p(\alpha, \theta)}. -#' Option \code{"summary"} does not simulate +#' @details +#' +#' For linear-Gaussian models, option \code{"summary"} does not simulate #' states directly but computes the posterior means and variances of states #' using fast Kalman smoothing. This is slightly faster, #' more memory efficient and more accurate than calculations based on -#' simulation smoother. Using option \code{"theta"} will only -#' return samples from the marginal posterior of the hyperparameters -#' \eqn{\theta}. -#' @param burnin Length of the burn-in period which is disregarded from the -#' results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of -#' \code{bssm} used adaptive MCMC during the burn-in period in order to find -#' good proposal. -#' @param thin Thinning rate. All MCMC algorithms in \code{bssm} use the -#' jump chain representation, and the thinning is applied to these blocks. -#' Defaults to 1. +#' simulation smoother. In other cases, the means and +#' covariances are computed using the full output of particle filter +#' instead of subsampling one of these as in case of +#' \code{output_type = "full"}. +#' +#' @importFrom stats tsp +#' @param model Model of class \code{bssm_model}. +#' @param iter Positive integer defining the total number of MCMC iterations. +#' @param output_type Either \code{"full"} +#' (default, returns posterior samples from the posterior +#' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of +#' theta), or \code{"summary"} (return the mean and variance estimates of the +#' states and posterior samples of theta). See details. +#' @param burnin Positive integer defining the length of the burn-in period +#' which is disregarded from the results. Defaults to \code{iter / 2}. +#' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the +#' burn-in period in order to find good proposal distribution. +#' @param thin Positive integer defining the thinning rate. All MCMC algorithms +#' in \code{bssm} use the jump chain representation (see ref [2]), and the +#' thinning is applied to these blocks. Defaults to 1. +#' For IS-corrected methods, larger value can also be +#' statistically more effective. Note: With \code{output_type = "summary"}, +#' the thinning does not affect the computations of the summary statistics in +#' case of pseudo-marginal methods. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be -#' between 0 and 1 (not checked). -#' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. -#' @param S Initial value for the lower triangular matrix of RAM -#' algorithm, so that the covariance matrix of the Gaussian proposal +#' between 0 and 1. +#' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. +#' Must be between 0 and 1. +#' @param S Matrix defining the initial value for the lower triangular matrix +#' of the RAM algorithm, so that the covariance matrix of the Gaussian proposal #' distribution is \eqn{SS'}. Note that for some parameters #' (currently the standard deviation, dispersion, and autoregressive parameters -#' of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter +#' of the BSM and AR(1) models) the sampling is done in unconstrained parameter #' space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the -#' burnin period. Default is \code{FALSE}. -#' @param threads Number of threads for state simulation. The default is 1. -#' @param seed Seed for the random number generator. +#' @param end_adaptive_phase Logical, if \code{TRUE}, S is held fixed after the +#' burnin period. Default is \code{FALSE}. +#' @param threads Number of threads for state simulation. Positive integer +#' (default is 1). +#' Note that parallel computing is only used in the post-correction phase of +#' IS-MCMC and when sampling the states in case of (approximate) Gaussian +#' models. +#' @param seed Seed for the random number generator (positive integer). +#' @param local_approx If \code{TRUE} (default), Gaussian approximation +#' needed for some of the methods is performed at each iteration. +#' If \code{FALSE}, approximation is updated only once at the start of the +#' MCMC using the initial model. +#' @param max_iter Maximum number of iterations used in Gaussian approximation, +#' as a positive integer. +#' Default is 100 (although typically only few iterations are needed). +#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. +#' @param particles Number of state samples per MCMC iteration for models other +#' than linear-Gaussian models. +#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. +#' @param mcmc_type What type of MCMC algorithm should be used for models other +#' than linear-Gaussian models? Possible choices are +#' \code{"pm"} for pseudo-marginal MCMC, +#' \code{"da"} for delayed acceptance version of PMCMC , +#' \code{"approx"} for approximate inference based on the Gaussian +#' approximation of the model, +#' \code{"ekf"} for approximate inference using extended Kalman filter +#' (for \code{ssm_nlg}), +#' or one of the three importance sampling type weighting schemes: +#' \code{"is3"} for simple importance sampling (weight is computed for each +#' MCMC iteration independently), +#' \code{"is2"} for jump chain importance sampling type weighting (default), or +#' \code{"is1"} for importance sampling type weighting where the number of +#' particles used for +#' weight computations is proportional to the length of the jump chain block. +#' @param sampling_method Method for state sampling when for models other than +#' linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). +#' If \code{"spdk"}, non-sequential importance sampling +#' based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter +#' is used. If \code{"ekf"}, particle filter based on EKF-proposals are used +#' (only for \code{ssm_nlg} models). +#' @param iekf_iter Non-negative integer. The default zero corresponds to +#' normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +#' with \code{iekf_iter} iterations. Used only for models of class +#' \code{ssm_nlg}. +#' @param L_c,L_f For \code{ssm_sde} models, Positive integer values defining +#' the discretization levels for first and second stages (defined as 2^L). +#' For pseudo-marginal methods (\code{"pm"}), maximum of these is used. #' @param ... Ignored. +#' @export +#' @rdname run_mcmc #' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' [1] Vihola M (2012). Robust adaptive Metropolis algorithm with +#' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. +#' +#' [2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +#' +#' [3] Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. +#' +run_mcmc <- function(model, ...) { + UseMethod("run_mcmc", model) +} +#' @method run_mcmc gaussian +#' @rdname run_mcmc #' @export #' @examples #' model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), @@ -86,12 +140,13 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) threads <- check_integer(threads, "threads") thin <- check_integer(thin, "thin", max = 100) iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_integer(burnin, "burnin", max = 1e12) - + check_targer(gamma) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() @@ -163,79 +218,9 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", frequency = frequency(model$y)) out } - - -#' Bayesian Inference of Non-Gaussian State Space Models -#' -#' Methods for posterior inference of states and parameters. -#' #' @method run_mcmc nongaussian -#' @rdname run_mcmc_ng +#' @rdname run_mcmc #' @export -#' @param model Model model. -#' @param iter Number of MCMC iterations. -#' @param particles Number of state samples per MCMC iteration. -#' Ignored if \code{mcmc_type} is \code{"approx"}. -#' @param output_type Either \code{"full"} -#' (default, returns posterior samples of latent states alpha and -#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -#' or \code{"summary"} (return the mean and variance estimates of the states -#' and posterior samples of theta). In case of \code{"summary"}, means and -#' covariances are computed using the full output of particle filter -#' instead of sampling one of these as in case of \code{output_type = "full"}. -#' @param mcmc_type What MCMC algorithm to use? Possible choices are -#' \code{"pm"} for pseudo-marginal MCMC, -#' \code{"da"} for delayed acceptance version of PMCMC , -#' \code{"approx"} for approximate inference based on the Gaussian -#' approximation of the model, -#' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each -#' MCMC iteration independently), -#' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of -#' particles used for -#' weight computations is proportional to the length of the jump chain block. -#' @param sampling_method If \code{"psi"}, \eqn{\psi}-APF is used for state -#' sampling (default). If \code{"spdk"}, non-sequential importance sampling -#' based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -#' is used. -#' @param burnin Length of the burn-in period which is disregarded from the -#' results. Defaults to \code{iter / 2}. -#' @param thin Thinning rate. Defaults to 1. Increase for large models in -#' order to save memory. For IS-corrected methods, larger value can also be -#' statistically more effective. Note: With \code{output_type = "summary"}, -#' the thinning does not affect the computations of the summary statistics in -#' case of pseudo-marginal methods. -#' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be -#' between 0 and 1 (not checked). -#' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -#' total acceptance rate will be smaller. -#' @param S Initial value for the lower triangular matrix of RAM -#' algorithm, so that the covariance matrix of the Gaussian proposal -#' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation, dispersion, and autoregressive parameters -#' of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter -#' space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the -#' burnin period. Default is \code{FALSE}. -#' @param local_approx If \code{TRUE} (default), Gaussian approximation -#' needed for importance sampling is performed at each iteration. -#' If \code{FALSE}, approximation is updated only once at the start of the -#' MCMC using the initial model. -#' @param threads Number of threads for state simulation. The default is 1. -#' Note that parallel computing is only used in the post-correction phase of -#' IS-MCMC and when sampling the states in case of approximate models. -#' @param seed Seed for the random number generator. -#' @param max_iter Maximum number of iterations used in Gaussian approximation, -#' as a positive integer. -#' Default is 100 (although typically only few iterations are needed). -#' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' @param ... Ignored. -#' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @examples #' set.seed(1) #' n <- 50 @@ -357,6 +342,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -497,72 +484,9 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", frequency = frequency(model$y)) out } -#' Bayesian Inference of non-linear state space models -#' -#' Methods for posterior inference of states and parameters. -#' #' @method run_mcmc ssm_nlg -#' @param model Model model. -#' @param iter Number of MCMC iterations. -#' @param particles Number of state samples per MCMC iteration. -#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. -#' @param output_type Either \code{"full"} -#' (default, returns posterior samples of latent states alpha and -#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -#' or \code{"summary"} (return the mean and variance estimates of the states -#' and posterior samples of theta). In case of \code{"summary"}, means and -#' covariances are computed using the full output of particle filter -#' instead of sampling one of these as in case of \code{output_type = "full"}. -#' @param mcmc_type What MCMC algorithm to use? Possible choices are -#' \code{"pm"} for pseudo-marginal MCMC, -#' \code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -#' \code{"approx"} for approximate inference based on the Gaussian -#' approximation of the model, -#' \code{"ekf"} for approximate inference using extended Kalman filter, -#' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each -#' MCMC iteration independently), -#' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of -#' particles used for -#' weight computations is proportional to the length of the jump chain block. -#' @param sampling_method If \code{"bsf"} (default), bootstrap filter is used -#' for state sampling. -#' If \code{"ekf"}, particle filter based on EKF-proposals are used. -#' If \code{"psi"}, \eqn{\psi}-APF is used. -#' @param burnin Length of the burn-in period which is disregarded from the -#' results. Defaults to \code{iter / 2}. -#' @param thin Thinning rate. Defaults to 1. Increase for large models in -#' order to save memory. For IS-corrected methods, larger -#' value can also be statistically more effective. -#' Note: With \code{output_type = "summary"}, the thinning does not affect the -#' computations of the summary statistics in case of pseudo-marginal methods. -#' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be -#' between 0 and 1 (not checked). -#' @param target_acceptance Target acceptance ratio for RAM. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -#' total acceptance rate will be smaller. -#' @param S Initial value for the lower triangular matrix of RAM -#' algorithm, so that the covariance matrix of the Gaussian proposal -#' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of -#' bsm_ng models) the sampling -#' is done for transformed parameters with internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin -#' period. Default is \code{FALSE}. -#' @param threads Number of threads for state simulation. -#' @param seed Seed for the random number generator. -#' @param max_iter Maximum number of iterations used in Gaussian approximation. -#' @param conv_tol Tolerance parameter used in Gaussian approximation. -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is -#' used with \code{iekf_iter} iterations in place of standard EKF. -#' Defaults to zero. -#' @param ... Ignored. +#' @rdname run_mcmc #' @export -#' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "bsf", burnin = floor(iter / 2), thin = 1, @@ -573,6 +497,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -584,6 +510,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } else { particles <- check_integer(particles, "particles") } + threads <- check_integer(threads, "threads") max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -707,57 +634,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", frequency = frequency(model$y)) out } -#' Bayesian Inference of SDE -#' -#' Methods for posterior inference of states and parameters. -#' #' @method run_mcmc ssm_sde -#' @param model Model model. -#' @param iter Number of MCMC iterations. -#' @param particles Number of state samples per MCMC iteration. -#' @param output_type Either \code{"full"} -#' (default, returns posterior samples of latent states alpha and -#' hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -#' or \code{"summary"} (return the mean and variance estimates of the states -#' and posterior samples of theta). In case of \code{"summary"}, means and -#' covariances are computed using the full output of particle filter -#' instead of sampling one of these as in case of \code{output_type = "full"}. -#' If \code{particles = 0}, this is argument ignored and set to \code{"theta"}. -#' @param mcmc_type What MCMC algorithm to use? Possible choices are -#' \code{"pm"} for pseudo-marginal MCMC, -#' \code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -#' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each -#' MCMC iteration independently), -#' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of -#' particles used for -#' weight computations is proportional to the length of the jump chain block. -#' @param burnin Length of the burn-in period which is disregarded from the -#' results. Defaults to \code{iter / 2}. -#' @param thin Thinning rate. Defaults to 1. Increase for large models in -#' order to save memory. For IS-corrected methods, larger -#' value can also be statistically more effective. -#' Note: With \code{output_type = "summary"}, the thinning does not affect the -#' computations of the summary statistics in case of pseudo-marginal methods. -#' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be -#' between 0 and 1 (not checked). -#' @param target_acceptance Target acceptance ratio for RAM. Defaults to 0.234. -#' For DA-MCMC, this corresponds to first stage acceptance rate, i.e., -#' the total acceptance rate will be smaller. -#' @param S Initial value for the lower triangular matrix of RAM -#' algorithm, so that the covariance matrix of the Gaussian proposal -#' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation and dispersion parameters of -#' bsm_ng models) the sampling is done for transformed parameters with -#' internal_theta = log(theta). -#' @param end_adaptive_phase If \code{TRUE}, S is held fixed after the burnin -#' period. Default is \code{FALSE}. -#' @param threads Number of threads for state simulation. -#' @param L_c,L_f Integer values defining the discretization levels for first -#' and second stages (defined as 2^L). For PM methods, maximum of these is used. -#' @param seed Seed for the random number generator. -#' @param ... Ignored. +#' @rdname run_mcmc #' @export #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based @@ -778,6 +656,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 655bf245..3b61aced 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -7,12 +7,11 @@ #' For non-Gaussian/non-linear models, the simulation is based on the #' approximating Gaussian model. #' -#' @param model Model object. -#' @param nsim Positive integer defining the number of independent samples. -#' @param use_antithetic Use an antithetic variable for location? -#' Default is \code{FALSE}. Currently not used for multivariate models. -#' @param seed Seed for the random number generator. -#' @param ... Ignored. +#' @inheritParams importance_sample +#' @param model Model of class \code{bsm_lg}, \code{ar1_lg} +#' \code{ssm_ulg}, or \code{ssm_mlg}, or one of the non-gaussian models +#' \code{bsm_ng}, \code{ar1_ng} \code{svm}, +#' \code{ssm_ung}, or \code{ssm_mng}. #' @return An array containing the generated samples. #' @export #' @rdname sim_smoother @@ -24,16 +23,17 @@ #' sim <- sim_smoother(model, nsim = 4, use_antithetic = TRUE, seed = 1) #' ts.plot(sim[, 1, ]) #' cor(sim[, 1, ]) -sim_smoother <- function(model, nsim, seed, use_antithetic = FALSE, ...) { +sim_smoother <- function(model, nsim, seed, use_antithetic = TRUE, ...) { UseMethod("sim_smoother", model) } #' @method sim_smoother gaussian #' @rdname sim_smoother #' @export sim_smoother.gaussian <- function(model, nsim = 1, - seed = sample(.Machine$integer.max, size = 1), use_antithetic = FALSE, ...) { + seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { nsim <- check_integer(nsim, "nsim") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) stop("Argument 'use_antithetic' should be TRUE or FALSE. ") @@ -46,9 +46,10 @@ sim_smoother.gaussian <- function(model, nsim = 1, #' @rdname sim_smoother #' @export sim_smoother.nongaussian <- function(model, nsim = 1, - seed = sample(.Machine$integer.max, size = 1), use_antithetic = FALSE, ...) { + seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { nsim <- check_integer(nsim, "nsim") + seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) stop("Argument 'use_antithetic' should be TRUE or FALSE. ") diff --git a/R/smoother.R b/R/smoother.R index 26e76a37..1f68f819 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -7,7 +7,7 @@ #' For non-Gaussian models, the smoothing is based on the approximate Gaussian #' model. #' -#' @param model Model model. +#' @inheritParams gaussian_approx #' @param ... Ignored. #' @return Matrix containing the smoothed estimates of states, or a list #' with the smoothed states and the variances. @@ -82,9 +82,7 @@ smoother.nongaussian <- function(model, ...) { #' variances. Function \code{ekf_fast_smoother} computes only smoothed #' estimates of the states. #' -#' @param model Model model -#' @param iekf_iter If \code{iekf_iter > 0}, iterated extended Kalman filter is -#' used with \code{iekf_iter} iterations. +#' @inheritParams ekf #' @return List containing the log-likelihood, #' smoothed state estimates \code{alphahat}, and the corresponding variances #' \code{Vt} and \code{Ptt}. diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 16dba8de..406e690f 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -46,7 +46,7 @@ bootstrap_filter(model, particles, ...) \item{...}{Ignored.} -\item{seed}{Seed for RNG.} +\item{seed}{Seed for RNG (non-negative integer).} \item{L}{Positive integer defining the discretization level for SDE models.} } diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index 30bd9b13..239c44e8 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -73,10 +73,10 @@ are just for consistent naming). } \examples{ # create uniform prior on [-1, 1] for one parameter with initial value 0.2: -uniform(0.2, -1.0, 1.0) +uniform(init = 0.2, min = -1.0, max = 1.0) # two normal priors at once i.e. for coefficients beta: normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) -# Gamma +# Gamma prior gamma(init = 0.1, shape = 2.5, rate = 1.1) # Same as gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) @@ -84,4 +84,19 @@ gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) halfnormal(init = 0.01, sd = 0.1) # Truncated normal tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) + +# Further examples for diagnostic purposes: +uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +tnormal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +halfnormal(c(0, 0.2), c(1.0, 1.2)) +gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) + +# longer versions: +uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) +tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) +gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) + } diff --git a/man/ekf.Rd b/man/ekf.Rd index 70332546..da575ebb 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -7,10 +7,11 @@ ekf(model, iekf_iter = 0) } \arguments{ -\item{model}{Model model} +\item{model}{Model of class \code{ssm_nlg}.} -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter -is used with \code{iekf_iter} iterations.} +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +with \code{iekf_iter} iterations.} } \value{ List containing the log-likelihood, diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index e14f6ea0..aff8ed1c 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -10,10 +10,11 @@ ekf_smoother(model, iekf_iter = 0) ekf_fast_smoother(model, iekf_iter = 0) } \arguments{ -\item{model}{Model model} +\item{model}{Model of class \code{ssm_nlg}.} -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is -used with \code{iekf_iter} iterations.} +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +with \code{iekf_iter} iterations.} } \value{ List containing the log-likelihood, diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index e9ba8cd2..0efd86f0 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -15,13 +15,13 @@ ekpf_filter(object, particles, ...) ) } \arguments{ -\item{object}{of class \code{ssm_nlg}.} +\item{object}{Model of class \code{ssm_nlg}.} -\item{particles}{Number of particles.} +\item{particles}{Number of particles as a positive integer.} \item{...}{Ignored.} -\item{seed}{Seed for RNG.} +\item{seed}{Seed for RNG (positive integer).} } \value{ A list containing samples, filtered estimates and the @@ -51,7 +51,7 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state") -out <- ekpf_filter(model_nlg, 100) +out <- ekpf_filter(model_nlg, particles = 100) ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) } diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 8bb9336f..a5247904 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -28,7 +28,8 @@ is less than \code{conv_tol}.} \item{...}{Ignored.} \item{iekf_iter}{For non-linear models, non-negative number of iterations in -iterated EKF (defaults to 0).} +iterated EKF (defaults to 0, i.e. normal EKF). Used only for models of class +\code{ssm_nlg}.} } \value{ Returns linear-Gaussian SSM of class \code{ssm_ulg} or diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index eea9f4fb..e3939973 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -18,12 +18,10 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) ) } \arguments{ -\item{model}{Model to be approximated. Should be of class -\code{bsm_ng}, \code{ar1_ng} \code{svm}, -\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or -non-linear \code{bssm_model}.} +\item{model}{Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}.} -\item{nsim}{Number of samples.} +\item{nsim}{Number of samples (positive integer).} \item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic variable for location in simulation smoothing. Ignored for \code{ssm_mng} @@ -36,7 +34,7 @@ Default is 100 (although typically only few iterations are needed).} is claimed to be converged when the mean squared difference of the modes of is less than \code{conv_tol}.} -\item{seed}{Seed for the random number generator.} +\item{seed}{Seed for the random number generator (positive integer).} \item{...}{Ignored.} } diff --git a/man/kfilter.Rd b/man/kfilter.Rd index 8049a822..6e625b80 100644 --- a/man/kfilter.Rd +++ b/man/kfilter.Rd @@ -13,7 +13,8 @@ kfilter(model, ...) \method{kfilter}{nongaussian}(model, ...) } \arguments{ -\item{model}{Model Model object.} +\item{model}{Model of class \code{gaussian}, \code{nongaussian} or +\code{ssm_nlg}.} \item{...}{Ignored.} } diff --git a/man/logLik.Rd b/man/logLik.Rd deleted file mode 100644 index feeeaf40..00000000 --- a/man/logLik.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.gaussian} -\alias{logLik.gaussian} -\title{Log-likelihood of a Gaussian State Space Model} -\usage{ -\method{logLik}{gaussian}(object, ...) -} -\arguments{ -\item{object}{Model model.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a linear-Gaussian state space model of -\code{bssm} package. -} -\examples{ -model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) -logLik(model) -} diff --git a/man/logLik.nongaussian.Rd b/man/logLik.nongaussian.Rd deleted file mode 100644 index 0798a399..00000000 --- a/man/logLik.nongaussian.Rd +++ /dev/null @@ -1,62 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.nongaussian} -\alias{logLik.nongaussian} -\title{Log-likelihood of a Non-Gaussian State Space Model} -\usage{ -\method{logLik}{nongaussian}( - object, - particles, - method = "psi", - max_iter = 100, - conv_tol = 1e-08, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter or -importance sampling. If 0, -approximate log-likelihood based on the Gaussian approximation is returned.} - -\item{method}{Sampling method, default is psi-auxiliary filter -(\code{"psi"}). Other choices are \code{"bsf"} bootstrap particle filter, -and \code{"spdk"}, which uses the importance sampling approach by -Shephard and Pitt (1997) and Durbin and Koopman (1997).} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. -Default is 100 (although typically only few iterations are needed). -Used in \eqn{\psi}-APF.} - -\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. -Default is 1e-8. Used \eqn{\psi}-APF.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a non-Gaussian state space model of -\code{bssm} package. -} -\examples{ -model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, - distribution = "poisson") - -model2 <- bsm_ng(y = c(1,4,3), sd_level = 0.5, P1 = 2, - distribution = "poisson") -logLik(model, particles = 0) -logLik(model2, particles = 0) -logLik(model, particles = 10, seed = 1) -logLik(model2, particles = 10, seed = 1) -} -\references{ -Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation -Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. - -Shephard, N., & Pitt, M. (1997). Likelihood Analysis of -Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. -} diff --git a/man/logLik.ssm_nlg.Rd b/man/logLik.ssm_nlg.Rd deleted file mode 100644 index 9cd321ad..00000000 --- a/man/logLik.ssm_nlg.Rd +++ /dev/null @@ -1,50 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_nlg} -\alias{logLik.ssm_nlg} -\title{Log-likelihood of a Non-linear State Space Model} -\usage{ -\method{logLik}{ssm_nlg}( - object, - particles, - method = "bsf", - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter. If 0, -approximate log-likelihood is returned either based on the Gaussian -approximation or EKF, depending on the \code{method} argument.} - -\item{method}{Sampling method. Default is the bootstrap particle filter -(\code{"bsf"}). Other choices are \code{"psi"} which uses -psi-auxiliary filter (or approximating Gaussian model in the case of -\code{particles = 0}), and \code{"ekf"} which uses EKF-based particle -filter (or just EKF approximation in the case of \code{particles = 0}).} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. -Default is 100 (although typically only few iterations are needed). -Used in \eqn{\psi}-APF.} - -\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. -Default is 1e-8. Used \eqn{\psi}-APF.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter -is used with -\code{iekf_iter} iterations in place of standard EKF. Defaults to zero.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class -\code{ssm_nlg} package. -} diff --git a/man/logLik.ssm_sde.Rd b/man/logLik.ssm_sde.Rd deleted file mode 100644 index 8129d19c..00000000 --- a/man/logLik.ssm_sde.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/loglik.R -\name{logLik.ssm_sde} -\alias{logLik.ssm_sde} -\title{Log-likelihood of a State Space Model with SDE dynamics} -\usage{ -\method{logLik}{ssm_sde}( - object, - particles, - L, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{object}{Model model.} - -\item{particles}{Number of samples for particle filter.} - -\item{L}{Integer defining the discretization level defined as (2^L).} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Computes the log-likelihood of a state space model of class -\code{ssm_sde} package. -} diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index f6edce33..1f830068 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -59,23 +59,22 @@ For Gaussian and non-Gaussian models with linear dynamics, options are \code{"bsf"} (bootstrap particle filter, default for non-linear models) and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and -for non-linear models options \code{"ekf"} (extended Kalman particle filter) +for non-linear models option \code{"ekf"} (extended Kalman particle filter) is also available.} -\item{seed}{Seed for RNG.} +\item{seed}{Seed for RNG (non-negative integer).} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, as a positive integer. -Default is 100 (although typically only few iterations are needed). -Used in \eqn{\psi}-APF.} +Default is 100 (although typically only few iterations are needed).} \item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. -Default is 1e-8. Used \eqn{\psi}-APF.} +Default is 1e-8.} -\item{iekf_iter}{If zero (default), first approximation for non-linear -Gaussian models is obtained from extended Kalman filter. If -\code{iekf_iter > 0}, iterated extended Kalman filter is used with -\code{iekf_iter} iterations.} +\item{iekf_iter}{Non-negative integer. If zero (default), first +approximation for non-linear Gaussian models is obtained from extended +Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations.} \item{L}{Positive integer defining the discretization level for SDE model.} } diff --git a/man/post_correct.Rd b/man/post_correct.Rd index a05bd663..da8f3503 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -34,7 +34,7 @@ MCMC iteration independently), particles used forweight computations is proportional to the length of the jump chain block.} -\item{seed}{Seed for the random number generator.} +\item{seed}{Seed for the random number generator (positive integer).} } \value{ List with suggested number of particles \code{N} and matrix diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 16189ee4..27e36f3c 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -37,7 +37,7 @@ future, using posterior samples of (theta, alpha_T+1) i.e. the posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} -\item{seed}{Seed for RNG.} +\item{seed}{Seed for RNG (positive integer).} \item{...}{Ignored.} } diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 38dd1cca..a84e724a 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -2,26 +2,347 @@ % Please edit documentation in R/run_mcmc.R \name{run_mcmc} \alias{run_mcmc} +\alias{run_mcmc.gaussian} +\alias{run_mcmc.nongaussian} +\alias{run_mcmc.ssm_nlg} +\alias{run_mcmc.ssm_sde} \title{Bayesian Inference of State Space Models} \usage{ -run_mcmc(model, iter, ...) +run_mcmc(model, ...) + +\method{run_mcmc}{gaussian}( + model, + iter, + output_type = "full", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{run_mcmc}{nongaussian}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "psi", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + local_approx = TRUE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + ... +) + +\method{run_mcmc}{ssm_nlg}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + sampling_method = "bsf", + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + ... +) + +\method{run_mcmc}{ssm_sde}( + model, + iter, + particles, + output_type = "full", + mcmc_type = "is2", + L_c, + L_f, + burnin = floor(iter/2), + thin = 1, + gamma = 2/3, + target_acceptance = 0.234, + S, + end_adaptive_phase = FALSE, + threads = 1, + seed = sample(.Machine$integer.max, size = 1), + ... +) } \arguments{ -\item{model}{State space model model of \code{bssm} package.} +\item{model}{Model of class \code{bssm_model}.} + +\item{...}{Ignored.} + +\item{iter}{Positive integer defining the total number of MCMC iterations.} + +\item{output_type}{Either \code{"full"} +(default, returns posterior samples from the posterior +\eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of +theta), or \code{"summary"} (return the mean and variance estimates of the +states and posterior samples of theta). See details.} + +\item{burnin}{Positive integer defining the length of the burn-in period +which is disregarded from the results. Defaults to \code{iter / 2}. +Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the +burn-in period in order to find good proposal distribution.} + +\item{thin}{Positive integer defining the thinning rate. All MCMC algorithms +in \code{bssm} use the jump chain representation (see ref [2]), and the +thinning is applied to these blocks. Defaults to 1. +For IS-corrected methods, larger value can also be +statistically more effective. Note: With \code{output_type = "summary"}, +the thinning does not affect the computations of the summary statistics in +case of pseudo-marginal methods.} + +\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be +between 0 and 1.} + +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. +Must be between 0 and 1.} + +\item{S}{Matrix defining the initial value for the lower triangular matrix +of the RAM algorithm, so that the covariance matrix of the Gaussian proposal +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation, dispersion, and autoregressive parameters +of the BSM and AR(1) models) the sampling is done in unconstrained parameter +space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} -\item{iter}{Number of MCMC iterations.} +\item{end_adaptive_phase}{Logical, if \code{TRUE}, S is held fixed after the +burnin period. Default is \code{FALSE}.} -\item{...}{Parameters to specific methods. See -\code{\link{run_mcmc.gaussian}}, -\code{\link{run_mcmc.nongaussian}}, \code{\link{run_mcmc.ssm_nlg}}, -and \code{\link{run_mcmc.ssm_sde}} for details.} +\item{threads}{Number of threads for state simulation. Positive integer +(default is 1). +Note that parallel computing is only used in the post-correction phase of +IS-MCMC and when sampling the states in case of (approximate) Gaussian +models.} + +\item{seed}{Seed for the random number generator (positive integer).} + +\item{particles}{Number of state samples per MCMC iteration for models other +than linear-Gaussian models. +Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} + +\item{mcmc_type}{What type of MCMC algorithm should be used for models other +than linear-Gaussian models? Possible choices are +\code{"pm"} for pseudo-marginal MCMC, +\code{"da"} for delayed acceptance version of PMCMC , +\code{"approx"} for approximate inference based on the Gaussian +approximation of the model, +\code{"ekf"} for approximate inference using extended Kalman filter +(for \code{ssm_nlg}), +or one of the three importance sampling type weighting schemes: +\code{"is3"} for simple importance sampling (weight is computed for each +MCMC iteration independently), +\code{"is2"} for jump chain importance sampling type weighting (default), or +\code{"is1"} for importance sampling type weighting where the number of +particles used for +weight computations is proportional to the length of the jump chain block.} + +\item{sampling_method}{Method for state sampling when for models other than +linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). +If \code{"spdk"}, non-sequential importance sampling +based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter +is used. If \code{"ekf"}, particle filter based on EKF-proposals are used +(only for \code{ssm_nlg} models).} + +\item{local_approx}{If \code{TRUE} (default), Gaussian approximation +needed for some of the methods is performed at each iteration. +If \code{FALSE}, approximation is updated only once at the start of the +MCMC using the initial model.} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed).} + +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation.} + +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +with \code{iekf_iter} iterations. Used only for models of class +\code{ssm_nlg}.} + +\item{L_c, L_f}{For \code{ssm_sde} models, Positive integer values defining +the discretization levels for first and second stages (defined as 2^L). +For pseudo-marginal methods (\code{"pm"}), maximum of these is used.} } \description{ -Adaptive Markov chain Monte Carlo simulation of state space models using -Robust Adaptive Metropolis algorithm by Vihola (2012). -See specific methods for various model types for details. +Adaptive Markov chain Monte Carlo simulation for SSMs using +Robust Adaptive Metropolis algorithm by Vihola (2012). Several different +MCMC sampling schemes are implemented, see parameter +arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and +Vihola (2021) for details. +} +\details{ +For linear-Gaussian models, option \code{"summary"} does not simulate +states directly but computes the posterior means and variances of states +using fast Kalman smoothing. This is slightly faster, +more memory efficient and more accurate than calculations based on +simulation smoother. In other cases, the means and +covariances are computed using the full output of particle filter +instead of subsampling one of these as in case of +\code{output_type = "full"}. +} +\examples{ +model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(500, 500, 500), + sd_y = halfnormal(1, 10)) + +mcmc_results <- run_mcmc(model, iter = 2e4) +summary(mcmc_results, return_se = TRUE) + +library("dplyr") +sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% + group_by(time) \%>\% + summarise(mean = mean(value), + lwr = quantile(value, 0.025), + upr = quantile(value, 0.975)) +library("ggplot2") +sumr \%>\% ggplot(aes(time, mean)) + + geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + + geom_line() + theme_bw() + + geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), + col = 2) +set.seed(1) +n <- 50 +slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) +level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) +y <- rpois(n, exp(level)) +poisson_model <- bsm_ng(y, + sd_level = halfnormal(0.01, 1), + sd_slope = halfnormal(0.01, 0.1), + P1 = diag(c(10, 0.1)), distribution = "poisson") + +# Note small number of iterations for CRAN checks +mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, + mcmc_type = "da") +summary(mcmc_out, what = "theta", return_se = TRUE) + +set.seed(123) +n <- 50 +sd_level <- 0.1 +drift <- 0.01 +beta <- -0.9 +phi <- 5 + +level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) +x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) +y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) + +model <- bsm_ng(y, xreg = x, + beta = normal(0, 0, 10), + phi = halfnormal(1, 10), + sd_level = halfnormal(0.1, 1), + sd_slope = halfnormal(0.01, 0.1), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + distribution = "negative binomial") + +# run IS-MCMC +# Note small number of iterations for CRAN checks +fit <- run_mcmc(model, iter = 5000, + particles = 10, mcmc_type = "is2", seed = 1) + +# extract states +d_states <- as.data.frame(fit, variable = "states", time = 1:n) + +library("dplyr") +library("ggplot2") + + # compute summary statistics +level_sumr <- d_states \%>\% + filter(variable == "level") \%>\% + group_by(time) \%>\% + summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), + lwr = Hmisc::wtd.quantile(value, weight, + 0.025, normwt = TRUE), + upr = Hmisc::wtd.quantile(value, weight, + 0.975, normwt = TRUE)) + +# visualize +level_sumr \%>\% ggplot(aes(x = time, y = mean)) + + geom_line() + + geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + + geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + + theme_bw() + + theme(legend.title = element_blank()) + + xlab("Time") + ylab("Level") + +# theta +d_theta <- as.data.frame(fit, variable = "theta") +ggplot(d_theta, aes(x = value)) + + geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + + facet_wrap(~ variable, scales = "free") + + theme_bw() + + +# Bivariate Poisson model: + +set.seed(1) +x <- cumsum(c(3, rnorm(19, sd = 0.5))) +y <- cbind( + rpois(20, exp(x)), + rpois(20, exp(x))) + +prior_fn <- function(theta) { + # half-normal prior using transformation + dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term +} + +update_fn <- function(theta) { + list(R = array(exp(theta), c(1, 1, 1))) +} + +model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, + R = 0.1, P1 = 1, distribution = "poisson", + init_theta = log(0.1), + prior_fn = prior_fn, update_fn = update_fn) + +# Note small number of iterations for CRAN checks +out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") + +sumr <- as.data.frame(out, variable = "states") \%>\% + group_by(time) \%>\% mutate(value = exp(value)) \%>\% + summarise(mean = mean(value), + ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) +ggplot(sumr, aes(time, mean)) + +geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + +geom_line() + +geom_line(data = data.frame(mean = y[, 1], time = 1:20), + colour = "tomato") + +geom_line(data = data.frame(mean = y[, 2], time = 1:20), + colour = "tomato") + +theme_bw() + } \references{ -Matti Vihola (2012). Robust adaptive Metropolis algorithm with +[1] Vihola M (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. + +[2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 + +[3] Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/run_mcmc.ssm_nlg.Rd b/man/run_mcmc.ssm_nlg.Rd deleted file mode 100644 index 14df035e..00000000 --- a/man/run_mcmc.ssm_nlg.Rd +++ /dev/null @@ -1,110 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_nlg} -\alias{run_mcmc.ssm_nlg} -\title{Bayesian Inference of non-linear state space models} -\usage{ -\method{run_mcmc}{ssm_nlg}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "bsf", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - iekf_iter = 0, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -\code{"approx"} for approximate inference based on the Gaussian -approximation of the model, -\code{"ekf"} for approximate inference using extended Kalman filter, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of -particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"bsf"} (default), bootstrap filter is used -for state sampling. -If \code{"ekf"}, particle filter based on EKF-proposals are used. -If \code{"psi"}, \eqn{\psi}-APF is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the -computations of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of -bsm_ng models) the sampling -is done for transformed parameters with internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin -period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation.} - -\item{conv_tol}{Tolerance parameter used in Gaussian approximation.} - -\item{iekf_iter}{If \code{iekf_iter > 0}, iterated extended Kalman filter is -used with \code{iekf_iter} iterations in place of standard EKF. -Defaults to zero.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -} diff --git a/man/run_mcmc.ssm_sde.Rd b/man/run_mcmc.ssm_sde.Rd deleted file mode 100644 index 3d558280..00000000 --- a/man/run_mcmc.ssm_sde.Rd +++ /dev/null @@ -1,95 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.ssm_sde} -\alias{run_mcmc.ssm_sde} -\title{Bayesian Inference of SDE} -\usage{ -\method{run_mcmc}{ssm_sde}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - L_c, - L_f, - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}. -If \code{particles = 0}, this is argument ignored and set to \code{"theta"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of pseudo-marginal MCMC, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of - particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{L_c, L_f}{Integer values defining the discretization levels for first -and second stages (defined as 2^L). For PM methods, maximum of these is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger -value can also be statistically more effective. -Note: With \code{output_type = "summary"}, the thinning does not affect the -computations of the summary statistics in case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance ratio for RAM. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., -the total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation and dispersion parameters of -bsm_ng models) the sampling is done for transformed parameters with -internal_theta = log(theta).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the burnin -period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -} diff --git a/man/run_mcmc_g.Rd b/man/run_mcmc_g.Rd deleted file mode 100644 index b83458ae..00000000 --- a/man/run_mcmc_g.Rd +++ /dev/null @@ -1,95 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.gaussian} -\alias{run_mcmc.gaussian} -\title{Bayesian Inference of Linear-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{gaussian}( - model, - iter, - output_type = "full", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{output_type}{Type of output. Default is \code{"full"}, which returns -samples from the posterior \eqn{p(\alpha, \theta)}. -Option \code{"summary"} does not simulate -states directly but computes the posterior means and variances of states -using fast Kalman smoothing. This is slightly faster, -more memory efficient and more accurate than calculations based on -simulation smoother. Using option \code{"theta"} will only -return samples from the marginal posterior of the hyperparameters -\eqn{\theta}.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of -\code{bssm} used adaptive MCMC during the burn-in period in order to find -good proposal.} - -\item{thin}{Thinning rate. All MCMC algorithms in \code{bssm} use the -jump chain representation, and the thinning is applied to these blocks. -Defaults to 1.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation, dispersion, and autoregressive parameters -of bsm_lg and ar1_lg models) the sampling is done in unconstrained parameter -space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the -burnin period. Default is \code{FALSE}.} - -\item{threads}{Number of threads for state simulation. The default is 1.} - -\item{seed}{Seed for the random number generator.} - -\item{...}{Ignored.} -} -\description{ -Bayesian Inference of Linear-Gaussian State Space Models -} -\examples{ -model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(500, 500, 500), - sd_y = halfnormal(1, 10)) - -mcmc_results <- run_mcmc(model, iter = 2e4) -summary(mcmc_results, return_se = TRUE) - -library("dplyr") -sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% - group_by(time) \%>\% - summarise(mean = mean(value), - lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) -library("ggplot2") -sumr \%>\% ggplot(aes(time, mean)) + - geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + - geom_line() + theme_bw() + - geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), - col = 2) -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -} diff --git a/man/run_mcmc_ng.Rd b/man/run_mcmc_ng.Rd deleted file mode 100644 index 3ebb8808..00000000 --- a/man/run_mcmc_ng.Rd +++ /dev/null @@ -1,226 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/run_mcmc.R -\name{run_mcmc.nongaussian} -\alias{run_mcmc.nongaussian} -\title{Bayesian Inference of Non-Gaussian State Space Models} -\usage{ -\method{run_mcmc}{nongaussian}( - model, - iter, - particles, - output_type = "full", - mcmc_type = "is2", - sampling_method = "psi", - burnin = floor(iter/2), - thin = 1, - gamma = 2/3, - target_acceptance = 0.234, - S, - end_adaptive_phase = FALSE, - local_approx = TRUE, - threads = 1, - seed = sample(.Machine$integer.max, size = 1), - max_iter = 100, - conv_tol = 1e-08, - ... -) -} -\arguments{ -\item{model}{Model model.} - -\item{iter}{Number of MCMC iterations.} - -\item{particles}{Number of state samples per MCMC iteration. -Ignored if \code{mcmc_type} is \code{"approx"}.} - -\item{output_type}{Either \code{"full"} -(default, returns posterior samples of latent states alpha and -hyperparameters theta), \code{"theta"} (for marginal posterior of theta), -or \code{"summary"} (return the mean and variance estimates of the states -and posterior samples of theta). In case of \code{"summary"}, means and -covariances are computed using the full output of particle filter -instead of sampling one of these as in case of \code{output_type = "full"}.} - -\item{mcmc_type}{What MCMC algorithm to use? Possible choices are -\code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of PMCMC , -\code{"approx"} for approximate inference based on the Gaussian -approximation of the model, -or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each -MCMC iteration independently), -\code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of -particles used for -weight computations is proportional to the length of the jump chain block.} - -\item{sampling_method}{If \code{"psi"}, \eqn{\psi}-APF is used for state -sampling (default). If \code{"spdk"}, non-sequential importance sampling - based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -is used.} - -\item{burnin}{Length of the burn-in period which is disregarded from the -results. Defaults to \code{iter / 2}.} - -\item{thin}{Thinning rate. Defaults to 1. Increase for large models in -order to save memory. For IS-corrected methods, larger value can also be -statistically more effective. Note: With \code{output_type = "summary"}, -the thinning does not affect the computations of the summary statistics in -case of pseudo-marginal methods.} - -\item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be -between 0 and 1 (not checked).} - -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. -For DA-MCMC, this corresponds to first stage acceptance rate, i.e., the -total acceptance rate will be smaller.} - -\item{S}{Initial value for the lower triangular matrix of RAM -algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation, dispersion, and autoregressive parameters -of bsm_ng and ar1_ng models) the sampling is done in unconstrained parameter -space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} - -\item{end_adaptive_phase}{If \code{TRUE}, S is held fixed after the -burnin period. Default is \code{FALSE}.} - -\item{local_approx}{If \code{TRUE} (default), Gaussian approximation -needed for importance sampling is performed at each iteration. -If \code{FALSE}, approximation is updated only once at the start of the -MCMC using the initial model.} - -\item{threads}{Number of threads for state simulation. The default is 1. -Note that parallel computing is only used in the post-correction phase of - IS-MCMC and when sampling the states in case of approximate models.} - -\item{seed}{Seed for the random number generator.} - -\item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. -Default is 100 (although typically only few iterations are needed).} - -\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation.} - -\item{...}{Ignored.} -} -\description{ -Methods for posterior inference of states and parameters. -} -\examples{ -set.seed(1) -n <- 50 -slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) -level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) -y <- rpois(n, exp(level)) -poisson_model <- bsm_ng(y, - sd_level = halfnormal(0.01, 1), - sd_slope = halfnormal(0.01, 0.1), - P1 = diag(c(10, 0.1)), distribution = "poisson") - -# Note small number of iterations for CRAN checks -mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, - mcmc_type = "da") -summary(mcmc_out, what = "theta", return_se = TRUE) - -set.seed(123) -n <- 50 -sd_level <- 0.1 -drift <- 0.01 -beta <- -0.9 -phi <- 5 - -level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) -x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) -y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) - -model <- bsm_ng(y, xreg = x, - beta = normal(0, 0, 10), - phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), - sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), - distribution = "negative binomial") - -# run IS-MCMC -# Note small number of iterations for CRAN checks -fit <- run_mcmc(model, iter = 5000, - particles = 10, mcmc_type = "is2", seed = 1) - -# extract states -d_states <- as.data.frame(fit, variable = "states", time = 1:n) - -library("dplyr") -library("ggplot2") - - # compute summary statistics -level_sumr <- d_states \%>\% - filter(variable == "level") \%>\% - group_by(time) \%>\% - summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), - lwr = Hmisc::wtd.quantile(value, weight, - 0.025, normwt = TRUE), - upr = Hmisc::wtd.quantile(value, weight, - 0.975, normwt = TRUE)) - -# visualize -level_sumr \%>\% ggplot(aes(x = time, y = mean)) + - geom_line() + - geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + - geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + - theme_bw() + - theme(legend.title = element_blank()) + - xlab("Time") + ylab("Level") - -# theta -d_theta <- as.data.frame(fit, variable = "theta") -ggplot(d_theta, aes(x = value)) + - geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + - facet_wrap(~ variable, scales = "free") + - theme_bw() - - -# Bivariate Poisson model: - -set.seed(1) -x <- cumsum(c(3, rnorm(19, sd = 0.5))) -y <- cbind( - rpois(20, exp(x)), - rpois(20, exp(x))) - -prior_fn <- function(theta) { - # half-normal prior using transformation - dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term -} - -update_fn <- function(theta) { - list(R = array(exp(theta), c(1, 1, 1))) -} - -model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, - R = 0.1, P1 = 1, distribution = "poisson", - init_theta = log(0.1), - prior_fn = prior_fn, update_fn = update_fn) - -# Note small number of iterations for CRAN checks -out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") - -sumr <- as.data.frame(out, variable = "states") \%>\% - group_by(time) \%>\% mutate(value = exp(value)) \%>\% - summarise(mean = mean(value), - ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) -ggplot(sumr, aes(time, mean)) + -geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + -geom_line() + -geom_line(data = data.frame(mean = y[, 1], time = 1:20), - colour = "tomato") + -geom_line(data = data.frame(mean = y[, 2], time = 1:20), - colour = "tomato") + -theme_bw() - -} -\references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -} diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 5025763b..918f15d6 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -6,13 +6,13 @@ \alias{sim_smoother.nongaussian} \title{Simulation Smoothing} \usage{ -sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) +sim_smoother(model, nsim, seed, use_antithetic = TRUE, ...) \method{sim_smoother}{gaussian}( model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, + use_antithetic = TRUE, ... ) @@ -20,19 +20,23 @@ sim_smoother(model, nsim, seed, use_antithetic = FALSE, ...) model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), - use_antithetic = FALSE, + use_antithetic = TRUE, ... ) } \arguments{ -\item{model}{Model object.} +\item{model}{Model of class \code{bsm_lg}, \code{ar1_lg} +\code{ssm_ulg}, or \code{ssm_mlg}, or one of the non-gaussian models +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}.} -\item{nsim}{Positive integer defining the number of independent samples.} +\item{nsim}{Number of samples (positive integer).} -\item{seed}{Seed for the random number generator.} +\item{seed}{Seed for the random number generator (positive integer).} -\item{use_antithetic}{Use an antithetic variable for location? -Default is \code{FALSE}. Currently not used for multivariate models.} +\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic +variable for location in simulation smoothing. Ignored for \code{ssm_mng} +models.} \item{...}{Ignored.} } diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index db51d0fe..6b40ad30 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -27,7 +27,7 @@ test. Default is \code{seq(10, 100, by = 10)}.} \item{replications}{How many replications should be used for computing the standard deviations? Default is 100.} -\item{seed}{Seed for the random number generator.} +\item{seed}{Seed for the random number generator (positive integer).} } \value{ List with suggested number of particles \code{N} and matrix diff --git a/man/ukf.Rd b/man/ukf.Rd index c3dd6883..0ae7ec2a 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -7,7 +7,7 @@ ukf(model, alpha = 0.001, beta = 2, kappa = 0) } \arguments{ -\item{model}{Model model} +\item{model}{Model of class \code{ssm_nlg}.} \item{alpha}{Positive tuning parameter of the UKF. Default is 0.001. Smaller the value, closer the sigma point are to the mean of the state.} From 184feed322f1cbeee42f36963b1d65522c3e2307 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 00:24:53 +0300 Subject: [PATCH 091/180] update Rd files --- man/smoother.Rd | 5 ++++- man/ssm_nlg.Rd | 25 +++++++++++++++---------- 2 files changed, 19 insertions(+), 11 deletions(-) diff --git a/man/smoother.Rd b/man/smoother.Rd index c9ca93c5..a7207278 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -16,7 +16,10 @@ smoother(model, ...) \method{smoother}{gaussian}(model, ...) } \arguments{ -\item{model}{Model model.} +\item{model}{Model to be approximated. Should be of class +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +non-linear \code{bssm_model}.} \item{...}{Ignored.} } diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index 9feaf8c5..33e76c17 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -28,15 +28,18 @@ ssm_nlg( \item{y}{Observations as multivariate time series (or matrix) of length \eqn{n}.} -\item{Z, H, T, R}{An external pointers for the C++ functions which -define the corresponding model functions.} +\item{Z, H, T, R}{An external pointers (object of class \code{externalptr}) for the +C++ functions which define the corresponding model functions.} -\item{Z_gn, T_gn}{An external pointers for the C++ functions which -define the gradients of the corresponding model functions.} +\item{Z_gn, T_gn}{An external pointers (object of class \code{externalptr}) for +the C++ functions which define the gradients of the corresponding model +functions.} -\item{a1}{Prior mean for the initial state as a vector of length m.} +\item{a1}{Prior mean for the initial state as object of class +\code{externalptr}} -\item{P1}{Prior covariance matrix for the initial state as m x m matrix.} +\item{P1}{Prior covariance matrix for the initial state as object of class +\code{externalptr}} \item{theta}{Parameter vector passed to all model functions.} @@ -46,11 +49,13 @@ functions.} \item{known_tv_params}{Matrix of known parameters passed to all model functions.} -\item{n_states}{Number of states in the model.} +\item{n_states}{Number of states in the model (positive integer).} -\item{n_etas}{Dimension of the noise term of the transition equation.} +\item{n_etas}{Dimension of the noise term of the transition equation +(positive integer).} -\item{log_prior_pdf}{An external pointer for the C++ function which +\item{log_prior_pdf}{An external pointer (object of class +\code{externalptr}) for the C++ function which computes the log-prior density given theta.} \item{time_varying}{Optional logical vector of length 4, denoting whether @@ -58,7 +63,7 @@ the values of Z, H, T, and R vary with respect to time variable (given identical states). If used, this can speed up some computations.} -\item{state_names}{Names for the states.} +\item{state_names}{Vector containing names for the states.} } \value{ Object of class \code{ssm_nlg}. From 530dcd8c1b6c6f76ec847b207e23ff871765c521 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 00:33:25 +0300 Subject: [PATCH 092/180] check_prop --- R/run_mcmc.R | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index c0cdebb5..acc19466 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -151,8 +151,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - check_target(target_acceptance) - + check_prop(target_acceptance) + check_prop(gamma, "gamma") output_type <- pmatch(output_type, c("full", "summary", "theta")) if (inherits(model, "bsm_lg")) { @@ -368,7 +368,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - check_target(target_acceptance) + check_prop(target_acceptance) + check_prop(gamma, "gamma") output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), "approx")) @@ -522,7 +523,9 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - check_target(target_acceptance) + + check_prop(target_acceptance) + check_prop(gamma, "gamma") output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), @@ -675,7 +678,9 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - check_target(target_acceptance) + + check_prop(target_acceptance) + check_prop(gamma, "gamma") output_type <- pmatch(output_type, c("full", "summary", "theta")) mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3))) From 1acc9f21c593f051c542869a10a0dde46da23efc Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 00:41:41 +0300 Subject: [PATCH 093/180] fix typo --- R/run_mcmc.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index acc19466..84604710 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -146,7 +146,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", thin <- check_integer(thin, "thin", max = 100) iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_integer(burnin, "burnin", max = 1e12) - check_targer(gamma) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() From 4fe2f02ab474ed5054029fa22f8dc1e97011f0d6 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 07:46:48 +0300 Subject: [PATCH 094/180] wrap some examples with \donttest --- R/as_draws.R | 6 +++--- R/ekpf_filter.R | 4 ++-- R/kfilter.R | 6 ++++-- R/loglik.R | 1 + R/models.R | 8 +++++--- R/smoother.R | 4 ++-- man/as_draws.Rd | 6 +++--- man/ekf.Rd | 3 ++- man/ekf_smoother.Rd | 4 ++-- man/ekpf_filter.Rd | 4 ++-- man/ssm_nlg.Rd | 4 ++-- man/ssm_sde.Rd | 4 +++- man/ukf.Rd | 3 ++- 13 files changed, 33 insertions(+), 24 deletions(-) diff --git a/R/as_draws.R b/R/as_draws.R index 402366c4..d764ade7 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -20,7 +20,7 @@ #' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), #' a1 = 1000, P1 = 500^2) #' -#' fit1 <- run_mcmc(model, iter = 2e4) +#' fit1 <- run_mcmc(model, iter = 2000) #' library("posterior") #' draws <- as_draws(fit1) #' head(draws, 4) @@ -29,9 +29,9 @@ #' #' # More chains: #' model$theta[] <- c(50, 150) # change initial value -#' fit2 <- run_mcmc(model, iter = 2e4) +#' fit2 <- run_mcmc(model, iter = 2000) #' model$theta[] <- c(150, 50) # change initial value -#' fit3 <- run_mcmc(model, iter = 2e4) +#' fit3 <- run_mcmc(model, iter = 2000) #' #' draws <- bind_draws(as_draws(fit1), #' as_draws(fit2), as_draws(fit3), along = "chain") diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 3bc41a63..62fc7315 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,7 +21,7 @@ ekpf_filter <- function(object, particles, ...) { #' @export #' @rdname ekpf_filter #' @examples -#' +#' \donttest{ # Takes a while #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) @@ -42,7 +42,7 @@ ekpf_filter <- function(object, particles, ...) { #' #' out <- ekpf_filter(model_nlg, particles = 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -#' +#'} ekpf_filter.ssm_nlg <- function(object, particles, seed = sample(.Machine$integer.max, size = 1), ...) { diff --git a/R/kfilter.R b/R/kfilter.R index 29d3f32b..42bd549e 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -65,7 +65,7 @@ kfilter.nongaussian <- function(model, ...) { #' @rdname ekf #' @export #' @examples -#' +#' \donttest{ # Takes a while on CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -91,6 +91,7 @@ kfilter.nongaussian <- function(model, ...) { #' out_ekf <- ekf(model_nlg, iekf_iter = 0) #' out_iekf <- ekf(model_nlg, iekf_iter = 5) #' ts.plot(cbind(x, out_ekf$att, out_iekf$att), col = 1:3) +#' } ekf <- function(model, iekf_iter = 0) { iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) @@ -131,6 +132,7 @@ ekf <- function(model, iekf_iter = 0) { #' @rdname ukf #' @export #' @examples +#' \donttest{ # Takes a while on CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -156,7 +158,7 @@ ekf <- function(model, iekf_iter = 0) { #' out_iekf <- ekf(model_nlg, iekf_iter = 5) #' out_ukf <- ukf(model_nlg, alpha = 0.01, beta = 2, kappa = 1) #' ts.plot(cbind(x, out_iekf$att, out_ukf$att), col = 1:3) -#' +#' } ukf <- function(model, alpha = 0.001, beta = 2, kappa = 0) { if (alpha <= 0) stop("Parameter 'alpha' should be positive. ") diff --git a/R/loglik.R b/R/loglik.R index daea8748..d3923003 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -4,6 +4,7 @@ #' package. #' #' @inheritParams particle_smoother +#' @param object Model of class \code{bssm_model}. #' @param particles Number of samples for particle filter. If 0, #' approximate log-likelihood is returned either based on the Gaussian #' approximation or EKF, depending on the \code{method} argument. diff --git a/R/models.R b/R/models.R index 34d84479..1e017991 100644 --- a/R/models.R +++ b/R/models.R @@ -1473,7 +1473,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @return Object of class \code{ssm_nlg}. #' @export #' @examples -#' +#' \donttest{ # Takes a while on CRAN #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) @@ -1494,7 +1494,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' #' out <- ekf(model_nlg, iekf_iter = 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) -#' +#' } ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, known_params = NA, known_tv_params = matrix(NA), n_states, n_etas, log_prior_pdf, time_varying = rep(TRUE, 4), @@ -1550,6 +1550,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' @export #' @examples #' +#' \donttest{ # Takes a while on CRAN #' library("sde") #' set.seed(1) #' # theta_0 = rho = 0.5 @@ -1575,11 +1576,12 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' est$alphahat + 2*sqrt(c(est$Vt))), #' col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) #' +#' #' # Takes time with finer mesh, parallelization with IS-MCMC helps a lot #' out <- run_mcmc(sde_model, L_c = 4, L_f = 8, #' particles = 50, iter = 2e4, #' threads = 4L) -#' +#' } #' ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive) { diff --git a/R/smoother.R b/R/smoother.R index 1f68f819..2952a16e 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -89,7 +89,7 @@ smoother.nongaussian <- function(model, ...) { #' @export #' @rdname ekf_smoother #' @examples -#' +#' \donttest{ # Takes a while on CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -115,7 +115,7 @@ smoother.nongaussian <- function(model, ...) { #' out_ekf <- ekf_smoother(model_nlg, iekf_iter = 0) #' out_iekf <- ekf_smoother(model_nlg, iekf_iter = 1) #' ts.plot(cbind(x, out_ekf$alphahat, out_iekf$alphahat), col = 1:3) -#' +#' } ekf_smoother <- function(model, iekf_iter = 0) { iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) diff --git a/man/as_draws.Rd b/man/as_draws.Rd index fbab327f..afcef498 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -31,7 +31,7 @@ model <- bsm_lg(Nile, sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), a1 = 1000, P1 = 500^2) -fit1 <- run_mcmc(model, iter = 2e4) +fit1 <- run_mcmc(model, iter = 2000) library("posterior") draws <- as_draws(fit1) head(draws, 4) @@ -40,9 +40,9 @@ summary(fit1, return_se = TRUE) # More chains: model$theta[] <- c(50, 150) # change initial value -fit2 <- run_mcmc(model, iter = 2e4) +fit2 <- run_mcmc(model, iter = 2000) model$theta[] <- c(150, 50) # change initial value -fit3 <- run_mcmc(model, iter = 2e4) +fit3 <- run_mcmc(model, iter = 2000) draws <- bind_draws(as_draws(fit1), as_draws(fit2), as_draws(fit3), along = "chain") diff --git a/man/ekf.Rd b/man/ekf.Rd index da575ebb..94c5cf67 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -26,7 +26,7 @@ and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ - +\donttest{ # Takes a while on CRAN set.seed(1) mu <- -0.2 rho <- 0.7 @@ -53,3 +53,4 @@ out_ekf <- ekf(model_nlg, iekf_iter = 0) out_iekf <- ekf(model_nlg, iekf_iter = 5) ts.plot(cbind(x, out_ekf$att, out_iekf$att), col = 1:3) } +} diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index aff8ed1c..a8e633ae 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -29,7 +29,7 @@ variances. Function \code{ekf_fast_smoother} computes only smoothed estimates of the states. } \examples{ - +\donttest{ # Takes a while on CRAN set.seed(1) mu <- -0.2 rho <- 0.7 @@ -55,5 +55,5 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, out_ekf <- ekf_smoother(model_nlg, iekf_iter = 0) out_iekf <- ekf_smoother(model_nlg, iekf_iter = 1) ts.plot(cbind(x, out_ekf$alphahat, out_iekf$alphahat), col = 1:3) - +} } diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 0efd86f0..6abfe370 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -32,7 +32,7 @@ Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ - +\donttest{ # Takes a while set.seed(1) n <- 50 x <- y <- numeric(n) @@ -53,7 +53,7 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, out <- ekpf_filter(model_nlg, particles = 100) ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) - +} } \references{ Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index 33e76c17..caa9b25d 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -90,7 +90,7 @@ the user, as you must provide the several small C++ snippets which define the model structure. See examples in the vignette. } \examples{ - +\donttest{ # Takes a while on CRAN set.seed(1) n <- 50 x <- y <- numeric(n) @@ -111,5 +111,5 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, out <- ekf(model_nlg, iekf_iter = 100) ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) - +} } diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 4b8f1cc0..d496593a 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -54,6 +54,7 @@ which define the model structure. See vignettes for an example. } \examples{ +\donttest{ # Takes a while on CRAN library("sde") set.seed(1) # theta_0 = rho = 0.5 @@ -79,10 +80,11 @@ ts.plot(cbind(x, est$alphahat, est$alphahat + 2*sqrt(c(est$Vt))), col = c(2, 1, 1, 1), lty = c(1, 1, 2, 2)) + # Takes time with finer mesh, parallelization with IS-MCMC helps a lot out <- run_mcmc(sde_model, L_c = 4, L_f = 8, particles = 50, iter = 2e4, threads = 4L) - +} } diff --git a/man/ukf.Rd b/man/ukf.Rd index 0ae7ec2a..35f9113d 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -31,6 +31,7 @@ and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ +\donttest{ # Takes a while on CRAN set.seed(1) mu <- -0.2 rho <- 0.7 @@ -56,5 +57,5 @@ model_nlg <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, out_iekf <- ekf(model_nlg, iekf_iter = 5) out_ukf <- ukf(model_nlg, alpha = 0.01, beta = 2, kappa = 1) ts.plot(cbind(x, out_iekf$att, out_ukf$att), col = 1:3) - +} } From 67321daa22081e55381ec829d534f29aeac52f90 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 07:50:47 +0300 Subject: [PATCH 095/180] alias bssm_prior and \dontshow --- R/priors.R | 5 +++-- man/bssm_prior.Rd | 4 +++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/R/priors.R b/R/priors.R index 8778996b..b7442dbc 100644 --- a/R/priors.R +++ b/R/priors.R @@ -15,7 +15,7 @@ #' are just for consistent naming). #' #' @rdname bssm_prior -#' @aliases bssm_prior_list +#' @aliases bssm_prior bssm_prior_list #' @param init Initial value for the parameter, used in initializing the model #' components and as a starting values in MCMC. #' @param min Lower bound of the uniform and truncated normal prior. @@ -42,6 +42,7 @@ #' # Truncated normal #' tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) #' +#' \dontshow{ #' # Further examples for diagnostic purposes: #' uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) @@ -55,7 +56,7 @@ #' tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) #' halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) #' gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) -#' +#' } uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index 239c44e8..2415c800 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -2,6 +2,7 @@ % Please edit documentation in R/priors.R \name{uniform_prior} \alias{uniform_prior} +\alias{bssm_prior} \alias{bssm_prior_list} \alias{uniform} \alias{halfnormal_prior} @@ -85,6 +86,7 @@ halfnormal(init = 0.01, sd = 0.1) # Truncated normal tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) +\dontshow{ # Further examples for diagnostic purposes: uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) @@ -98,5 +100,5 @@ normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) - +} } From caa30d997b42e1517e74090bc7fafe0493392858 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 08:57:14 +0300 Subject: [PATCH 096/180] new file --- man/logLik_bssm.Rd | 120 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 man/logLik_bssm.Rd diff --git a/man/logLik_bssm.Rd b/man/logLik_bssm.Rd new file mode 100644 index 00000000..47b9e94e --- /dev/null +++ b/man/logLik_bssm.Rd @@ -0,0 +1,120 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/loglik.R +\name{logLik.gaussian} +\alias{logLik.gaussian} +\alias{logLik.nongaussian} +\alias{logLik.ssm_nlg} +\alias{logLik.ssm_sde} +\title{Extract Log-likelihood of a State Space Model of class \code{bssm_model}} +\usage{ +\method{logLik}{gaussian}(object, ...) + +\method{logLik}{nongaussian}( + object, + particles, + method = "psi", + max_iter = 100, + conv_tol = 1e-08, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{logLik}{ssm_nlg}( + object, + particles, + method = "bsf", + max_iter = 100, + conv_tol = 1e-08, + iekf_iter = 0, + seed = sample(.Machine$integer.max, size = 1), + ... +) + +\method{logLik}{ssm_sde}( + object, + particles, + L, + seed = sample(.Machine$integer.max, size = 1), + ... +) +} +\arguments{ +\item{object}{Model of class \code{bssm_model}.} + +\item{...}{Ignored.} + +\item{particles}{Number of samples for particle filter. If 0, +approximate log-likelihood is returned either based on the Gaussian +approximation or EKF, depending on the \code{method} argument.} + +\item{method}{Sampling method. For Gaussian and non-Gaussian models with +linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default +for non-linear models) +and \code{"psi"} (\eqn{\psi}-APF, the default for other models). +For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle +filter (or just EKF/IEKF approximation in the case of \code{particles = 0}).} + +\item{max_iter}{Maximum number of iterations used in Gaussian approximation, +as a positive integer. +Default is 100 (although typically only few iterations are needed).} + +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +Default is 1e-8.} + +\item{seed}{Seed for RNG (non-negative integer).} + +\item{iekf_iter}{Non-negative integer. If zero (default), first +approximation for non-linear Gaussian models is obtained from extended +Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is +used with \code{iekf_iter} iterations.} + +\item{L}{Integer defining the discretization level defined as (2^L).} +} +\description{ +Computes the log-likelihood of a state space model defined by \code{bssm} +package. +} +\examples{ + +model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) +logLik(model) +model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, + distribution = "poisson") + +model2 <- bsm_ng(y = c(1,4,3), sd_level = 0.5, P1 = 2, + distribution = "poisson") + +logLik(model, particles = 0) +logLik(model2, particles = 0) +logLik(model, particles = 10, seed = 1) +logLik(model2, particles = 10, seed = 1) +} +\references{ +Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation +Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. + +Shephard, N., & Pitt, M. (1997). Likelihood Analysis of +Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. + +Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +IEE Proceedings-F, 140, 107-113. + +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 + +Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +The unscented particle filter. +In Advances in neural information processing systems, p 584-590. + +Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +Academic Press. + +Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +nonlinear state space models. +Journal of Computational and Graphical Statistics, 5, 1-25. +} +\seealso{ +particle_smoother +} From 04be11eddfe0c7a82a4af3f01c5cad49de3c9993 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 09:20:43 +0300 Subject: [PATCH 097/180] parameter types for postcorrect --- R/post_correction.R | 14 +++++++------- man/post_correct.Rd | 4 ++-- man/suggest_N.Rd | 8 ++++---- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/R/post_correction.R b/R/post_correction.R index 77b98024..c01e69fa 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -21,16 +21,16 @@ get_map <- function(x) { #' estimate from the (approximate) MCMC run. Can also be an output from #' \code{run_mcmc} which is then used to compute the MAP #' estimate of theta. -#' @param candidates Vector containing the candidate number of particles to -#' test. Default is \code{seq(10, 100, by = 10)}. -#' @param replications How many replications should be used for computing -#' the standard deviations? Default is 100. +#' @param candidates Vector of positive integers containing the candidate +#' number of particles to test. Default is \code{seq(10, 100, by = 10)}. +#' @param replications Positive integer, how many replications should be used +#' for computing the standard deviations? Default is 100. #' @param seed Seed for the random number generator (positive integer). #' @return List with suggested number of particles \code{N} and matrix #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. #' @references -#'Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). +#' Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). #' Efficient implementation of Markov chain Monte Carlo when using an #' unbiased likelihood estimator, Biometrika, 102(2) p. 295-313, #' https://doi.org/10.1093/biomet/asu075 @@ -138,8 +138,8 @@ suggest_N <- function(model, theta, #' While the intended use assumes this is from approximate MCMC, it is not #' actually checked, i.e., it is also possible to input previous #' (asymptotically) exact output. -#' @param particles Number of particles for \eqn{\psi}-APF. -#' @param threads Number of parallel threads. +#' @param particles Number of particles for \eqn{\psi}-APF (positive integer). +#' @param threads Number of parallel threads (positive integer, default is 1). #' @param is_type Type of IS-correction. Possible choices are #'\code{"is3"} for simple importance sampling (weight is computed for each #'MCMC iteration independently), diff --git a/man/post_correct.Rd b/man/post_correct.Rd index da8f3503..c30a4775 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -22,9 +22,9 @@ While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., it is also possible to input previous (asymptotically) exact output.} -\item{particles}{Number of particles for \eqn{\psi}-APF.} +\item{particles}{Number of particles for \eqn{\psi}-APF (positive integer).} -\item{threads}{Number of parallel threads.} +\item{threads}{Number of parallel threads (positive integer, default is 1).} \item{is_type}{Type of IS-correction. Possible choices are \code{"is3"} for simple importance sampling (weight is computed for each diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index 6b40ad30..fdcab0a8 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -21,11 +21,11 @@ estimate from the (approximate) MCMC run. Can also be an output from \code{run_mcmc} which is then used to compute the MAP estimate of theta.} -\item{candidates}{Vector containing the candidate number of particles to -test. Default is \code{seq(10, 100, by = 10)}.} +\item{candidates}{Vector of positive integers containing the candidate +number of particles to test. Default is \code{seq(10, 100, by = 10)}.} -\item{replications}{How many replications should be used for computing -the standard deviations? Default is 100.} +\item{replications}{Positive integer, how many replications should be used +for computing the standard deviations? Default is 100.} \item{seed}{Seed for the random number generator (positive integer).} } From a85504e3608a9265ff19ef64aeb5d667d0485e71 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 19 Sep 2021 09:40:25 +0300 Subject: [PATCH 098/180] switch nlg models in tests --- tests/testthat/test_post_correct.R | 44 ++++++++++++++++++++---------- tests/testthat/test_predict.R | 6 ++-- 2 files changed, 33 insertions(+), 17 deletions(-) diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index 2ed64b01..0ee8e247 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -46,22 +46,39 @@ test_that("Test post correction for AR1 model", { test_that("Test post correction for non-linear model", { skip_on_cran() set.seed(1) - n <- 10 - x <- y <- numeric(n) - y[1] <- rnorm(1, exp(x[1]), 0.1) - for(i in 1:(n-1)) { - x[i+1] <- rnorm(1, sin(x[i]), 0.1) - y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) - } - y[2:3] <- NA - pntrs <- cpp_example_model("nlg_sin_exp") - expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, + p1 <- 50 # population size at t = 1 + K <- 500 # carrying capacity + H <- 1 # standard deviation of obs noise + R_1 <- 0.05 # standard deviation of the noise on logit-growth + R_2 <- 1 # standard deviation of the noise in population level + #sample time + dT <- .1 + + #observation times + t <- seq(0.1, 10, dT) + n <- length(t) + r <- plogis(cumsum(c(-1.5, rnorm(n - 1, sd = R_1)))) + p <- numeric(n) + p[1] <- p1 + for(i in 2:n) + p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / (K + p[i-1] * (exp(r[i-1] * dT) - 1)), R_2) + # observations + y <- p + rnorm(n, 0, H) + y[2:15] <- NA + pntrs <- cpp_example_model("nlg_growth") + + initial_theta <- c(log_H = 0, log_R1 = log(0.05), log_R2 = 0) + + # dT, K, a1 and the prior variances of first and second state (logit r and and p) + known_params <- c(dT = dT, K = K, a11 = -1, a12 = 50, P11 = 1, P12 = 100) + + expect_error(model <- ssm_nlg(y = y, a1=pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, - theta = c(log_H = log(0.1), log_R = log(0.1)), - log_prior_pdf = pntrs$log_prior_pdf, - n_states = 1, n_etas = 1, state_names = "state"), NA) + theta = initial_theta, log_prior_pdf = pntrs$log_prior_pdf, + known_params = known_params, known_tv_params = matrix(1), + n_states = 2, n_etas = 2, state_names = c("logit_r", "p")), NA) expect_error(out_approx <- run_mcmc(model, mcmc_type = "approx", @@ -80,5 +97,4 @@ test_that("Test post correction for non-linear model", { expect_lt(sum(out_is2$Vt), Inf) expect_lt(max(out_is2$weights), Inf) expect_gt(max(out_is2$weights), 0) - }) diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 54011a27..daf10d68 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -149,16 +149,16 @@ test_that("Predictions for nlg_ssm work", { x <- y <- numeric(n) y[1] <- rnorm(1, exp(x[1]), 0.1) for(i in 1:(n-1)) { - x[i+1] <- rnorm(1, sin(x[i]), 0.1) + x[i+1] <- rnorm(1, 0.9 * x[i], 0.1) y[i+1] <- rnorm(1, exp(x[i+1]), 0.1) } - pntrs <- cpp_example_model("nlg_sin_exp") + pntrs <- cpp_example_model("nlg_ar_exp") expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn, - theta = c(log_H = log(0.1), log_R = log(0.1)), + theta = c(mu = 0, rho = 0.9, log_R = log(0.1), log_H = log(0.1)), log_prior_pdf = pntrs$log_prior_pdf, n_states = 1, n_etas = 1, state_names = "state"), NA) From d7f16b07ae182fb27e6b66013e37024395c62f27 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 20 Sep 2021 09:04:23 +0300 Subject: [PATCH 099/180] starting with SRR --- DESCRIPTION | 4 +- R/bssm-package.R | 12 +- R/srr-stats-standards.R | 133 ++++++++++++ README.Rmd | 166 +++++++++++++++ README.html | 347 +++++++++++++++++++++++++++++++ README.md | 36 +++- man/ar1_lg.Rd | 16 +- man/ar1_ng.Rd | 30 +-- man/as.data.frame.mcmc_output.Rd | 12 +- man/as_bssm.Rd | 6 +- man/as_draws.Rd | 12 +- man/bootstrap_filter.Rd | 12 +- man/bsm_lg.Rd | 24 +-- man/bsm_ng.Rd | 40 ++-- man/bssm.Rd | 37 ++-- man/bssm_prior.Rd | 26 +-- man/cpp_example_model.Rd | 6 +- man/drownings.Rd | 6 +- man/ekf.Rd | 12 +- man/ekf_smoother.Rd | 14 +- man/ekpf_filter.Rd | 8 +- man/exchange.Rd | 4 +- man/expand_sample.Rd | 18 +- man/gaussian_approx.Rd | 34 +-- man/importance_sample.Rd | 16 +- man/kfilter.Rd | 14 +- man/logLik_bssm.Rd | 48 ++--- man/particle_smoother.Rd | 54 ++--- man/post_correct.Rd | 38 ++-- man/predict.mcmc_output.Rd | 32 +-- man/run_mcmc.Rd | 108 +++++----- man/sim_smoother.Rd | 12 +- man/smoother.Rd | 8 +- man/ssm_mlg.Rd | 38 ++-- man/ssm_mng.Rd | 44 ++-- man/ssm_nlg.Rd | 26 +-- man/ssm_sde.Rd | 10 +- man/ssm_ulg.Rd | 38 ++-- man/ssm_ung.Rd | 52 ++--- man/suggest_N.Rd | 36 ++-- man/summary.mcmc_output.Rd | 18 +- man/svm.Rd | 18 +- man/ukf.Rd | 14 +- vignettes/bssm.Rmd | 13 ++ 44 files changed, 1172 insertions(+), 480 deletions(-) create mode 100644 R/srr-stats-standards.R create mode 100644 README.Rmd create mode 100644 README.html diff --git a/DESCRIPTION b/DESCRIPTION index dd63b830..7fa8a2c1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -41,10 +41,12 @@ Suggests: Imports: checkmate, coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -RoxygenNote: 7.1.1 VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues URL: https://github.com/helske/bssm ByteCompile: true Encoding: UTF-8 NeedsCompilation: yes +RoxygenNote: 7.1.2 +Roxygen: list(markdown = TRUE, + roclets = c("namespace", "rd", "srr::srr_stats_roclet")) diff --git a/R/bssm-package.R b/R/bssm-package.R index cf548006..05eae06e 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -13,10 +13,14 @@ #' #' * Model with continuous SDE dynamics. #' -#' For formal definition of the currently supported models and methods, as -#' well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, -#' see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package -#' vignettes. +#' The \code{bssm} package includes several MCMC sampling and sequential Monte +#' Carlo methods for models outside classic linear-Gaussian framework. For +#' definitions of the currently supported models and methods, as +#' well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF +#' algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and +#' the package vignettes. +#' +#' @srrstatsTODO {G1.4,G1.4a} Package uses roxygen2 for documentation. #' #' @references #' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R new file mode 100644 index 00000000..32a7f987 --- /dev/null +++ b/R/srr-stats-standards.R @@ -0,0 +1,133 @@ +#' srr_stats +#' +#' All of the following standards initially have `@srrstatsTODO` tags. +#' These may be moved at any time to any other locations in your code. +#' Once addressed, please modify the tag from `@srrstatsTODO` to `@srrstats`, +#' or `@srrstatsNA`, ensuring that references to every one of the following +#' standards remain somewhere within your code. +#' (These comments may be deleted at any time.) +#' +#' @srrstatsVerbose TRUE +#' +#' @srrstatsTODO {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* +#' @srrstatsTODO {G2.0a} Provide explicit secondary documentation of any expectations on lengths of inputs +#' @srrstatsTODO {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* +#' @srrstatsTODO {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* +#' @srrstatsTODO {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* +#' @srrstatsTODO {G2.3} *For univariate character input:* +#' @srrstatsTODO {G2.3a} *Use `match.arg()` or equivalent where applicable to only permit expected values.* +#' @srrstatsTODO {G2.3b} *Either: use `tolower()` or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.* +#' @srrstatsTODO {G2.4} *Provide appropriate mechanisms to convert between different data types, potentially including:* +#' @srrstatsTODO {G2.4a} *explicit conversion to `integer` via `as.integer()`* +#' @srrstatsTODO {G2.4b} *explicit conversion to continuous via `as.numeric()`* +#' @srrstatsTODO {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* +#' @srrstatsTODO {G2.4d} *explicit conversion to factor via `as.factor()`* +#' @srrstatsTODO {G2.4e} *explicit conversion from factor via `as...()` functions* +#' @srrstatsTODO {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* +#' @srrstatsTODO {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* +#' @srrstatsTODO {G2.7} *Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.* +#' @srrstatsTODO {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* +#' @srrstatsTODO {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* +#' @srrstatsTODO {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* +#' @srrstatsTODO {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* +#' @srrstatsTODO {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* +#' @srrstatsTODO {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* +#' @srrstatsTODO {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* +#' @srrstatsTODO {G2.14a} *error on missing data* +#' @srrstatsTODO {G2.14b} *ignore missing data with default warnings or messages issued* +#' @srrstatsTODO {G2.14c} *replace missing data with appropriately imputed values* +#' @srrstatsTODO {G2.15} *Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default `na.rm = FALSE`-type parameters (such as [`mean()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/mean.html), [`sd()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) or [`cor()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html)).* +#' @srrstatsTODO {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* +#' @srrstatsTODO {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* +#' @srrstatsTODO {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* +#' @srrstatsTODO {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* +#' @srrstatsTODO {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* +#' @srrstatsTODO {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* +#' @srrstatsTODO {G5.1} *Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.* +#' @srrstatsTODO {G5.2} *Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,* +#' @srrstatsTODO {G5.2a} *Every message produced within R code by `stop()`, `warning()`, `message()`, or equivalent should be unique* +#' @srrstatsTODO {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* +#' @srrstatsTODO {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* +#' @srrstatsTODO {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* +#' @srrstatsTODO {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* +#' @srrstatsTODO {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* +#' @srrstatsTODO {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* +#' @srrstatsTODO {G5.5} *Correctness tests should be run with a fixed random seed* +#' @srrstatsTODO {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* +#' @srrstatsTODO {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* +#' @srrstatsTODO {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* +#' @srrstatsTODO {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* +#' @srrstatsTODO {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* +#' @srrstatsTODO {G5.8a} *Zero-length data* +#' @srrstatsTODO {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* +#' @srrstatsTODO {G5.8c} *Data with all-`NA` fields or columns or all identical fields or columns* +#' @srrstatsTODO {G5.8d} *Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)* +#' @srrstatsTODO {G5.9} **Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:* +#' @srrstatsTODO {G5.9a} *Adding trivial noise (for example, at the scale of `.Machine$double.eps`) to data does not meaningfully change results* +#' @srrstatsTODO {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results* +#' @srrstatsTODO {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* +#' @srrstatsTODO {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* +#' @srrstatsTODO {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* +#' @srrstatsTODO {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* +#' +#' @srrstatsTODO {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* +#' @srrstatsTODO {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* +#' @srrstatsTODO {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* +#' @srrstatsTODO {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* +#' @srrstatsTODO {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* +#' @srrstatsTODO {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* +#' @srrstatsTODO {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* +#' @srrstatsTODO {BS2.1a} *The effects of such routines should be tested.* +#' @srrstatsTODO {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* +#' @srrstatsTODO {BS2.3} *Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).* +#' @srrstatsTODO {BS2.4} *Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)* +#' @srrstatsTODO {BS2.5} *Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.* +#' @srrstatsTODO {BS2.6} *Check that values for computational parameters lie within plausible ranges.* +#' @srrstatsTODO {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* +#' @srrstatsTODO {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* +#' @srrstatsTODO {BS2.9} *Ensure each chain is started with a different seed by default.* +#' @srrstatsTODO {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* +#' @srrstatsTODO {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* +#' @srrstatsTODO {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* +#' @srrstatsTODO {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* +#' @srrstatsTODO {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* +#' @srrstatsTODO {BS2.15} *Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.* +#' @srrstatsTODO {BS3.0} *Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (`NA`, `Inf`) values, and that such values, or entire rows including any such values, will be automatically removed from input data.* +#' @srrstatsTODO {BS3.1} *Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings* +#' @srrstatsTODO {BS3.2} *Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms* +#' @srrstatsTODO {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* +#' @srrstatsTODO {BS4.1} *Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).* +#' @srrstatsTODO {BS4.3} *Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.* +#' @srrstatsTODO {BS4.4} *Enable computations to be stopped on convergence (although not necessarily by default).* +#' @srrstatsTODO {BS4.5} *Ensure that appropriate mechanisms are provided for models which do not converge.* +#' @srrstatsTODO {BS4.6} *Implement tests to confirm that results with convergence checker are statistically equivalent to results from equivalent fixed number of samples without convergence checking.* +#' @srrstatsTODO {BS4.7} *Where convergence checkers are themselves parametrised, the effects of such parameters should also be tested. For threshold parameters, for example, lower values should result in longer sequence lengths.* +#' @srrstatsTODO {BS5.0} *Return values should include starting value(s) or seed(s), including values for each sequence where multiple sequences are included* +#' @srrstatsTODO {BS5.1} *Return values should include appropriate metadata on types (or classes) and dimensions of input data* +#' @srrstatsTODO {BS5.2} *Bayesian Software should either return the input function or prior distributional specification in the return object; or enable direct access to such via additional functions which accept the return object as single argument.* +#' @srrstatsTODO {BS5.3} *Bayesian Software should return convergence statistics or equivalent* +#' @srrstatsTODO {BS5.4} *Where multiple checkers are enabled, Bayesian Software should return details of convergence checker used* +#' @srrstatsTODO {BS5.5} *Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.* +#' @srrstatsTODO {BS6.0} *Software should implement a default `print` method for return objects* +#' @srrstatsTODO {BS6.1} *Software should implement a default `plot` method for return objects* +#' @srrstatsTODO {BS6.2} *Software should provide and document straightforward abilities to plot sequences of posterior samples, with burn-in periods clearly distinguished* +#' @srrstatsTODO {BS6.3} *Software should provide and document straightforward abilities to plot posterior distributional estimates* +#' @srrstatsTODO {BS6.4} *Software may provide `summary` methods for return objects* +#' @srrstatsTODO {BS6.5} *Software may provide abilities to plot both sequences of posterior samples and distributional estimates together in single graphic* +#' @srrstatsTODO {BS7.0} *Software should demonstrate and confirm recovery of parametric estimates of a prior distribution* +#' @srrstatsTODO {BS7.1} *Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information* +#' @srrstatsTODO {BS7.2} *Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data* +#' @srrstatsTODO {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* +#' @srrstatsTODO {BS7.4} *Bayesian software should implement tests which confirm that predicted or fitted values are on (approximately) the same scale as input values.* +#' @srrstatsTODO {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* +#' @noRd +NULL + +#' NA_standards +#' +#' Any non-applicable standards can have their tags changed from `@srrstatsTODO` +#' to `@srrstatsNA`, and placed together in this block, along with explanations +#' for why each of these standards have been deemed not applicable. +#' (These comments may also be deleted at any time.) +#' @noRd +NULL diff --git a/README.Rmd b/README.Rmd new file mode 100644 index 00000000..e5514f67 --- /dev/null +++ b/README.Rmd @@ -0,0 +1,166 @@ + +# bssm: an R package for Bayesian inference of state space models + +[![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) +[![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) +[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) + +```{r srr-tags, eval = FALSE, echo = FALSE} +#' @srrstats {G1.0} Cites of the paper introducing to the package, as well as +#' published paper proposing the IS-MCMC method used in \code{bssm}. +#' @srrstats {G1.1} The \code{bssm} package is +#' first to implement the IS-MCMC method as well as psi-APF particle filter +#' both introduced in Vihola, Helske, Franks (2020). +#' To our knowledge, the package is also the first R package to +#' implement delayed acceptance pseudo-marginal MCMC for general state space +#' models. +#' Note that the IS-MCMC method is also available in \code{walker} package for +#' limited class of models (subset of the models supported by \code{bssm}). +#' @srrstatsTODO {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* +#' @srrstats {G1.3} Terminology is introduced in the vignettes as well as in the corresponding papers. +#' @srrstatsTODO {G1.5} *Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.* +#' @srrstatsTODO {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* + +``` + +Efficient methods for Bayesian inference of state space models via particle +Markov chain Monte Carlo and importance sampling type weighted MCMC. +Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation +densities with linear-Gaussian state dynamics, as well as general non-linear +Gaussian models and discretely observed latent diffusion processes are +supported. + +For details, see + + * [the bssm paper on ArXiv](https://arxiv.org/abs/2101.08492), + * [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) + * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) + +There are also couple posters and a talk related to IS-correction methodology and bssm package: + + * [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) + * [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) + * [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) + +You can install the latest development version from R-universe with + +```R +install.packages("bssm", repos = "https://helske.r-universe.dev") +``` + +Or by using the devtools package: + +```R +install.packages("devtools") +devtools::install_github("helske/bssm") +``` + +## Recent changes (For all changes, see NEWS file.) + +#### bssm 1.1.6 (Release date: 2021-09-06) + + * Cleaned codes and added more comprehensive tests in line with pkgcheck + tests. This resulted in finding and fixing multiple bugs: + * Fixed a bug in EKF-based particle filter which returned filtered estimates + also in place of one-step ahead predictions. + * Fixed a bug which caused an error in suggest_N for nlg_ssm. + * Fixed a bug which caused incorrect sampling of smoothing distribution for + ar1_lg model when predicting past or when using simulation smoother. + * Fixed a bug which caused an error when predicting past values in + multivariate time series case. + * Fixed sampling of negative binomial distribution in predict method, which + used std::negative_binomial which converts non-integer phi to integer. + Sampling now uses Gamma-Poisson mixture for simulation. + +#### bssm 1.1.4 (Release date: 2021-04-13) + + * Better documentation for SV model, and changed ordering of arguments to emphasise the + recommended parameterization. + * Fixed predict method for SV model. + +#### bssm 1.1.3-2 (Release date: 2021-02-24) + + * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. + * Added pandoc version >= 1.12.3 to system requirements. + +#### bssm 1.1.3-1 (Release date: 2021-02-22) + + * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. + * Added vignette for SDE models. + * Updated citation information and streamlined the main vignette. + +#### bssm 1.1.2 (Release date: 2021-02-08) + + * Some bug fixes, see NEWS for details. + +#### bssm 1.1.0 (Release date: 2021-01-19) + + + * Added function `suggest_N` which can be used to choose + suitable number of particles for IS-MCMC. + * Added function `post_correct` which can be used to update + previous approximate MCMC with IS-weights. + * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. + * The adaptation of the proposal distribution now continues also after the burn-in by default. + * Changed default MCMC type to typically most efficient and robust IS2. + * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). + * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. + This resulted error within MCMC algorithms. + * Fixed a dimension drop bug in the predict method which caused error for univariate models. + * Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. + +#### bssm 1.0.1-1 (Release date: 2020-11-12) + + + * Added an argument `future` for predict method which allows + predictions for current time points by supplying the original model + (e.g., for posterior predictive checks). + At the same time the argument name `future_model` was changed to `model`. + * Fixed a bug in summary.mcmc_run which resulted error when + trying to obtain summary for states only. + * Added a check for Kalman filter for a degenerate case where all + observational level and state level variances are zero. + * Renamed argument `n_threads` to `threads` for consistency + with `iter` and `burnin` arguments. + * Improved documentation, added examples. + * Added a vignette regarding psi-APF for non-linear models. + +#### bssm 1.0.0 (Release date: 2020-06-09) + +Major update + + * Major changes for model definitions, now model updating and priors + can be defined via R functions (non-linear and SDE models still rely on C++ snippets). + * Added support for multivariate non-Gaussian models. + * Added support for gamma distributions. + * Added the function as.data.frame for mcmc output which converts the MCMC samples + to data.frame format for easier post-processing. + * Added truncated normal prior. + * Many argument names and model building functions have been changed for clarity and consistency. + * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. + * Allow zero as initial value for positive-constrained parameters of bsm models. + * Small changes to summary method which can now return also only summaries of the states. + * Fixed a bug in initializing run_mcmc for negative binomial model. + * Fixed a bug in phi-APF for non-linear models. + * Reimplemented predict method which now always produces data frame of samples. + +#### bssm 0.1.11 (Release date: 2020-02-25) + + * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, + as it seems to work better with noisy likelihood estimates. + * Print and summary methods for MCMC output are now coherent in their output. + +#### bssm 0.1.10 (Release date: 2020-02-04) + + * Fixed missing weight update for IS-SPDK without OPENMP flag. + * Removed unused usage argument ... from expand_sample. + +#### bssm 0.1.9 (Release date: 2020-01-27) + + * Fixed state sampling for PM-MCMC with SPDK. + * Added ts attribute for svm model. + * Corrected asymptotic variance for summary methods. + +For older versions, see NEWS file. diff --git a/README.html b/README.html new file mode 100644 index 00000000..53b8a442 --- /dev/null +++ b/README.html @@ -0,0 +1,347 @@ + + + + + + + + + + + + + +README.knit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

bssm: an R package for Bayesian inference of state space models

+

Project Status: Active - The project has reached a stable, usable state and is being actively developed R-CMD-check Codecov test coverage cran version downloads

+

Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type weighted MCMC. Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported.

+

For details, see

+ +

There are also couple posters and a talk related to IS-correction methodology and bssm package:

+ +

You can install the latest development version from R-universe with

+
install.packages("bssm", repos = "https://helske.r-universe.dev")
+

Or by using the devtools package:

+
install.packages("devtools")
+devtools::install_github("helske/bssm")
+
+

Recent changes (For all changes, see NEWS file.)

+
+

bssm 1.1.6 (Release date: 2021-09-06)

+
    +
  • Cleaned codes and added more comprehensive tests in line with pkgcheck tests. This resulted in finding and fixing multiple bugs:
    • +
  • Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions.
    • +
  • Fixed a bug which caused an error in suggest_N for nlg_ssm.
    • +
  • Fixed a bug which caused incorrect sampling of smoothing distribution for ar1_lg model when predicting past or when using simulation smoother.
    • +
  • Fixed a bug which caused an error when predicting past values in multivariate time series case.
    • +
  • Fixed sampling of negative binomial distribution in predict method, which used std::negative_binomial which converts non-integer phi to integer. Sampling now uses Gamma-Poisson mixture for simulation.
    • +
+
+
+

bssm 1.1.4 (Release date: 2021-04-13)

+
    +
  • Better documentation for SV model, and changed ordering of arguments to emphasise the recommended parameterization.
    • +
  • Fixed predict method for SV model.
    • +
+
+
+

bssm 1.1.3-2 (Release date: 2021-02-24)

+
    +
  • Fixed missing parenthesis causing compilation fail in case of no OpenMP support.
    • +
  • Added pandoc version >= 1.12.3 to system requirements.
    • +
+
+
+

bssm 1.1.3-1 (Release date: 2021-02-22)

+
    +
  • Fixed PM-MCMC and DA-MCMC for SDE models and added an example to ssm_sde.
    • +
  • Added vignette for SDE models.
    • +
  • Updated citation information and streamlined the main vignette.
    • +
+
+
+

bssm 1.1.2 (Release date: 2021-02-08)

+
    +
  • Some bug fixes, see NEWS for details.
    • +
+
+
+

bssm 1.1.0 (Release date: 2021-01-19)

+
    +
  • Added function suggest_N which can be used to choose suitable number of particles for IS-MCMC.
    • +
  • Added function post_correct which can be used to update previous approximate MCMC with IS-weights.
    • +
  • Gamma priors are now supported in easy-to-use models such as bsm_lg.
    • +
  • The adaptation of the proposal distribution now continues also after the burn-in by default.
    • +
  • Changed default MCMC type to typically most efficient and robust IS2.
    • +
  • Renamed nsim argument to particles in most of the R functions (nsim also works with a warning).
    • +
  • Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. This resulted error within MCMC algorithms.
    • +
  • Fixed a dimension drop bug in the predict method which caused error for univariate models.
    • +
  • Fixed few typos in vignette (thanks Kyle Hussman) and added more examples.
    • +
+
+
+

bssm 1.0.1-1 (Release date: 2020-11-12)

+
    +
  • Added an argument future for predict method which allows predictions for current time points by supplying the original model (e.g., for posterior predictive checks). At the same time the argument name future_model was changed to model.
    • +
  • Fixed a bug in summary.mcmc_run which resulted error when trying to obtain summary for states only.
    • +
  • Added a check for Kalman filter for a degenerate case where all observational level and state level variances are zero.
    • +
  • Renamed argument n_threads to threads for consistency with iter and burnin arguments.
    • +
  • Improved documentation, added examples.
    • +
  • Added a vignette regarding psi-APF for non-linear models.
    • +
+
+
+

bssm 1.0.0 (Release date: 2020-06-09)

+

Major update

+
    +
  • Major changes for model definitions, now model updating and priors can be defined via R functions (non-linear and SDE models still rely on C++ snippets).
    • +
  • Added support for multivariate non-Gaussian models.
    • +
  • Added support for gamma distributions.
    • +
  • Added the function as.data.frame for mcmc output which converts the MCMC samples to data.frame format for easier post-processing.
    • +
  • Added truncated normal prior.
    • +
  • Many argument names and model building functions have been changed for clarity and consistency.
    • +
  • Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size.
    • +
  • Allow zero as initial value for positive-constrained parameters of bsm models.
    • +
  • Small changes to summary method which can now return also only summaries of the states.
    • +
  • Fixed a bug in initializing run_mcmc for negative binomial model.
    • +
  • Fixed a bug in phi-APF for non-linear models.
    • +
  • Reimplemented predict method which now always produces data frame of samples.
    • +
+
+
+

bssm 0.1.11 (Release date: 2020-02-25)

+
    +
  • Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, as it seems to work better with noisy likelihood estimates.
    • +
  • Print and summary methods for MCMC output are now coherent in their output.
    • +
+
+
+

bssm 0.1.10 (Release date: 2020-02-04)

+
    +
  • Fixed missing weight update for IS-SPDK without OPENMP flag.
    • +
  • Removed unused usage argument … from expand_sample.
    • +
+
+
+

bssm 0.1.9 (Release date: 2020-01-27)

+
    +
  • Fixed state sampling for PM-MCMC with SPDK.
    • +
  • Added ts attribute for svm model.
    • +
  • Corrected asymptotic variance for summary methods.
    • +
+

For older versions, see NEWS file.

+
+
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/README.md b/README.md index 12fa5309..2c6685e0 100644 --- a/README.md +++ b/README.md @@ -8,12 +8,38 @@ bssm: an R package for Bayesian inference of state space models ========================================================================== -Efficient methods for Bayesian inference of state space models via particle Markov -chain Monte Carlo and importance sampling type weighted Markov chain Monte Carlo. -Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities -and linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported. +```{r srr-tags, eval = FALSE, echo = FALSE} +#' @srrstats {G1.0} Cites of the paper introducing to the package, as well as +#' published paper proposing the IS-MCMC method used in \code{bssm}. +#' @srrstats {G1.1} The \code{bssm} package is +#' first to implement the IS-MCMC method as well as psi-APF particle filter +#' both introduced in Vihola, Helske, Franks (2020). +#' To our knowledge, the package is also the first R package to +#' implement delayed acceptance pseudo-marginal MCMC for general state space +#' models. +#' Note that the IS-MCMC method is also available in \code{walker} package for +#' limited class of models (subset of the models supported by \code{bssm}). +#' @srrstatsTODO {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* +#' @srrstats {G1.3} Terminology is introduced in the vignettes as well as in the corresponding papers. -For details, see [paper on ArXiv](https://arxiv.org/abs/2101.08492), [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) and paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492). There are also couple posters related to IS-correction methodology: [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) and [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) and [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) +``` + +Efficient methods for Bayesian inference of state space models via particle +Markov chain Monte Carlo and importance sampling type weighted MCMC. +Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation +densities with linear-Gaussian state dynamics, as well as general non-linear +Gaussian models and discretely observed latent diffusion processes are +supported. + +For details, see +* [the bssm paper on ArXiv](https://arxiv.org/abs/2101.08492), +* [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +* Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) + +There are also couple posters and a talk related to IS-correction methodology and bssm package: +* [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) +* [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) +* [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) You can install the latest development version from R-universe with diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index ce87a280..3038e24c 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -9,30 +9,30 @@ ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) \arguments{ \item{y}{Vector or a \code{ts} object of observations.} -\item{rho}{Prior for autoregressive coefficient. +\item{rho}{Prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for the standard deviation of noise of the AR-process. +\item{sigma}{Prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}} -\item{mu}{A fixed value or a prior for the stationary mean of the latent -AR(1) process. Should be an object of class \code{bssm_prior} or scalar +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Should be an object of class \code{bssm_prior} or scalar value defining a fixed mean such as 0.} \item{sd_y}{A prior for the standard deviation of observation equation.} -\item{beta}{Prior for the regression coefficients. -Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the +\item{xreg}{Matrix containing covariates with number of rows matching the length of \code{y}.} } \value{ Object of class \code{ar1_lg}. } \description{ -Constructs a simple Gaussian model where the state dynamics +Constructs a simple Gaussian model where the state dynamics follow an AR(1) process. } \examples{ diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index b4b9912a..6bac2bf0 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -9,42 +9,42 @@ ar1_ng(y, rho, sigma, mu, distribution, phi, u, beta, xreg = NULL) \arguments{ \item{y}{Vector or a \code{ts} object of observations.} -\item{rho}{Prior for autoregressive coefficient. +\item{rho}{Prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for the standard deviation of noise of the AR-process. +\item{sigma}{Prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}} -\item{mu}{A fixed value or a prior for the stationary mean of the latent -AR(1) process. Should be an object of class \code{bssm_prior} or scalar +\item{mu}{A fixed value or a prior for the stationary mean of the latent +AR(1) process. Should be an object of class \code{bssm_prior} or scalar value defining a fixed mean such as 0.} -\item{distribution}{Distribution of the observed time series. Possible -choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\item{distribution}{Distribution of the observed time series. Possible +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} \item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma -distribution this is the shape parameter, and for other distributions this -is ignored. Should an object of class \code{bssm_prior} or +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{beta}{Prior for the regression coefficients. -Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the +\item{xreg}{Matrix containing covariates with number of rows matching the length of \code{y}.} } \value{ Object of class \code{ar1_ng}. } \description{ -Constructs a simple non-Gaussian model where the state dynamics follow an +Constructs a simple non-Gaussian model where the state dynamics follow an AR(1) process. } \examples{ diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index 7f594f62..5683b7f9 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -22,18 +22,18 @@ \item{optional}{Ignored.} -\item{variable}{Return samples of \code{"theta"} (default) or +\item{variable}{Return samples of \code{"theta"} (default) or \code{"states"}?} -\item{times}{Vector of indices. In case of states, +\item{times}{Vector of indices. In case of states, what time points to return? Default is all.} -\item{states}{Vector of indices. In case of states, +\item{states}{Vector of indices. In case of states, what states to return? Default is all.} -\item{expand}{Should the jump-chain be expanded? -Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. -For \code{expand = FALSE} and always for IS-MCMC, +\item{expand}{Should the jump-chain be expanded? +Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. +For \code{expand = FALSE} and always for IS-MCMC, the resulting data.frame contains variable weight (= counts * IS-weights).} \item{...}{Ignored.} diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index dd678871..9062382b 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -16,12 +16,12 @@ used to replace exact diffuse elements of the original model.} (such as prior and updating functions).} } \value{ -Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. } \description{ -Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} -model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} +model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. } \examples{ diff --git a/man/as_draws.Rd b/man/as_draws.Rd index afcef498..00a5eb2e 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -16,12 +16,12 @@ as_draws.mcmc_output(x) A \code{draws_df} object. } \description{ -Converts MCMC output from \code{run_mcmc} call to a -\code{draws_df} format of the \code{posterior} package. This enables the use -of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} -packages. Note though that if \code{run_mcmc} used IS-MCMC -method, the resulting \code{weight} column of the output is -ignored by the aforementioned packages, i.e. the results correspond to +Converts MCMC output from \code{run_mcmc} call to a +\code{draws_df} format of the \code{posterior} package. This enables the use +of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} +packages. Note though that if \code{run_mcmc} used IS-MCMC +method, the resulting \code{weight} column of the output is +ignored by the aforementioned packages, i.e. the results correspond to approximate MCMC. } \examples{ diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 406e690f..cb361adf 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -51,13 +51,13 @@ bootstrap_filter(model, particles, ...) \item{L}{Positive integer defining the discretization level for SDE models.} } \value{ -List with samples (\code{alpha}) from the filtering distribution and -corresponding weights (\code{weights}), as well as filtered and predicted -states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, +List with samples (\code{alpha}) from the filtering distribution and +corresponding weights (\code{weights}), as well as filtered and predicted +states and corresponding covariances (\code{at}, \code{att}, \code{Pt}, \code{Ptt}), and estimated log-likelihood (\code{logLik}). } \description{ -Function \code{bootstrap_filter} performs a bootstrap filtering with +Function \code{bootstrap_filter} performs a bootstrap filtering with stratification resampling. } \examples{ @@ -79,7 +79,7 @@ ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2) } \references{ -Gordon, NJ, Salmond, DJ, Smith, AFM (1993) Novel approach to -nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, +Gordon, NJ, Salmond, DJ, Smith, AFM (1993) Novel approach to +nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F, 140(2), p. 107-113. } diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 9cd12f64..180fed9a 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -26,28 +26,28 @@ bsm_lg( Should be an object of class \code{bssm_prior} or scalar.} \item{sd_level}{Standard deviation of the noise of level equation. -Should be an object of class \code{bssm_prior} or scalar +Should be an object of class \code{bssm_prior} or scalar value defining a known value such as 0.} \item{sd_slope}{Standard deviation of the noise of slope equation. -Should be an object of class \code{bssm_prior}, scalar -value defining a known value such as 0, or missing, in which case the slope +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the slope term is omitted from the model.} \item{sd_seasonal}{Standard deviation of the noise of seasonal equation. -Should be an object of class \code{bssm_prior}, scalar -value defining a known value such as 0, or missing, in which case the +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the seasonal term is omitted from the model.} -\item{beta}{Prior for the regression coefficients. -Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the +\item{xreg}{Matrix containing covariates with number of rows matching the length of \code{y}.} -\item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be a positive integer greater than 2 +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be a positive integer greater than 2 and less than the length of the input time series.} \item{a1}{Prior means for the initial states (level, slope, seasonals). @@ -57,14 +57,14 @@ Defaults to vector of zeros.} Default is diagonal matrix with 1000 on the diagonal.} \item{D, C}{Intercept terms for observation and -state equations, given as a length n vector and m times n matrix +state equations, given as a length n vector and m times n matrix respectively (or scalar and m times 1 matrix).} } \value{ Object of class \code{bsm_lg}. } \description{ -Constructs a basic structural model with local level or local trend +Constructs a basic structural model with local level or local trend component and seasonal component. } \examples{ diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 57fbd788..c72688ee 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -25,46 +25,46 @@ bsm_ng( \item{y}{Vector or a \code{ts} object of observations.} \item{sd_level}{Standard deviation of the noise of level equation. -Should be an object of class \code{bssm_prior} or scalar +Should be an object of class \code{bssm_prior} or scalar value defining a known value such as 0.} \item{sd_slope}{Standard deviation of the noise of slope equation. -Should be an object of class \code{bssm_prior}, scalar -value defining a known value such as 0, or missing, in which case the slope +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the slope term is omitted from the model.} \item{sd_seasonal}{Standard deviation of the noise of seasonal equation. -Should be an object of class \code{bssm_prior}, scalar -value defining a known value such as 0, or missing, in which case the +Should be an object of class \code{bssm_prior}, scalar +value defining a known value such as 0, or missing, in which case the seasonal term is omitted from the model.} -\item{sd_noise}{Prior for the standard deviation of the additional noise -term to be added to linear predictor, defined as an object of class +\item{sd_noise}{Prior for the standard deviation of the additional noise +term to be added to linear predictor, defined as an object of class \code{bssm_prior}. If missing, no additional noise term is used.} -\item{distribution}{Distribution of the observed time series. Possible -choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\item{distribution}{Distribution of the observed time series. Possible +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} \item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma -distribution this is the shape parameter, and for other distributions this -is ignored. Should an object of class \code{bssm_prior} or +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{beta}{Prior for the regression coefficients. -Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} +\item{beta}{Prior for the regression coefficients. +Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the +\item{xreg}{Matrix containing covariates with number of rows matching the length of \code{y}.} -\item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be a positive integer greater than 2 +\item{period}{Length of the seasonal pattern. +Default is \code{frequency(y)}. Must be a positive integer greater than 2 and less than the length of the input time series.} \item{a1}{Prior means for the initial states (level, slope, seasonals). @@ -73,7 +73,7 @@ Defaults to vector of zeros.} \item{P1}{Prior covariance for the initial states (level, slope, seasonals). Default is diagonal matrix with 1000 on the diagonal.} -\item{C}{Intercept terms for state equation, given as a m x n or m x 1 +\item{C}{Intercept terms for state equation, given as a m x n or m x 1 matrix.} } \value{ diff --git a/man/bssm.Rd b/man/bssm.Rd index b96daa8b..1791a110 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -5,31 +5,32 @@ \alias{bssm} \title{Bayesian Inference of State Space Models} \description{ -This package contains functions for efficient Bayesian inference of state +This package contains functions for efficient Bayesian inference of state space models, where model is assumed to be either } \details{ -* Exponential family state space models, where the state equation is linear - Gaussian, and the conditional observation density is either Gaussian, - Poisson, binomial, negative binomial or Gamma density. - -* Basic stochastic volatility model. - -* General non-linear model with Gaussian noise terms. - -* Model with continuous SDE dynamics. +\itemize{ +\item Exponential family state space models, where the state equation is linear +Gaussian, and the conditional observation density is either Gaussian, +Poisson, binomial, negative binomial or Gamma density. +\item Basic stochastic volatility model. +\item General non-linear model with Gaussian noise terms. +\item Model with continuous SDE dynamics. +} -For formal definition of the currently supported models and methods, as -well as some theory behind the IS-MCMC and \eqn{\psi}{psi}-APF, -see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package -vignettes. +The \code{bssm} package includes several MCMC sampling and sequential Monte +Carlo methods for models outside classic linear-Gaussian framework. For +definitions of the currently supported models and methods, as +well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF +algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and +the package vignettes. } \references{ -Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. ArXiv 2101.08492, +Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. ArXiv 2101.08492, . -Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators -based on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index 2415c800..f14251a9 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -36,14 +36,14 @@ gamma_prior(init, shape, rate) gamma(init, shape, rate) } \arguments{ -\item{init}{Initial value for the parameter, used in initializing the model +\item{init}{Initial value for the parameter, used in initializing the model components and as a starting values in MCMC.} \item{min}{Lower bound of the uniform and truncated normal prior.} \item{max}{Upper bound of the uniform and truncated normal prior.} -\item{sd}{Positive value defining the standard deviation of the +\item{sd}{Positive value defining the standard deviation of the (underlying i.e. non-truncated) Normal distribution.} \item{mean}{Mean of the Normal prior.} @@ -53,23 +53,23 @@ components and as a starting values in MCMC.} \item{rate}{Positive rate parameter of the Gamma prior.} } \value{ -object of class \code{bssm_prior} or \code{bssm_prior_list} in case +object of class \code{bssm_prior} or \code{bssm_prior_list} in case of multiple priors (i.e. multiple regression coefficients). } \description{ -These simple objects of class \code{bssm_prior} are used to construct a -prior distributions for the some of the model objects of \code{bssm} -package. Currently supported priors are uniform -(\code{uniform()}), half-normal (\code{halfnormal()}), -normal (\code{normal()}), gamma (\code{gamma}), and -truncated normal distribution (\code{tnormal()}). All parameters are -vectorized so for regression coefficient vector beta you can define prior +These simple objects of class \code{bssm_prior} are used to construct a +prior distributions for the some of the model objects of \code{bssm} +package. Currently supported priors are uniform +(\code{uniform()}), half-normal (\code{halfnormal()}), +normal (\code{normal()}), gamma (\code{gamma}), and +truncated normal distribution (\code{tnormal()}). All parameters are +vectorized so for regression coefficient vector beta you can define prior for example as \code{normal(0, 0, c(10, 20))}. } \details{ -The longer name versions of the prior functions with \code{_prior} ending -are identical with shorter versions and they are available only to -avoid clash with R's primitive function \code{gamma} (other long prior names +The longer name versions of the prior functions with \code{_prior} ending +are identical with shorter versions and they are available only to +avoid clash with R's primitive function \code{gamma} (other long prior names are just for consistent naming). } \examples{ diff --git a/man/cpp_example_model.Rd b/man/cpp_example_model.Rd index 124d5e17..b71cdaf1 100644 --- a/man/cpp_example_model.Rd +++ b/man/cpp_example_model.Rd @@ -7,14 +7,14 @@ cpp_example_model(example, return_code = FALSE) } \arguments{ -\item{example}{Name of the example model. +\item{example}{Name of the example model. Run \code{cpp_example_model("abc")} to get the names of possible models.} -\item{return_code}{If TRUE, will not compile the model but only returns the +\item{return_code}{If TRUE, will not compile the model but only returns the corresponding code.} } \value{ -Returns pointers to the C++ snippets defining the model, or in case +Returns pointers to the C++ snippets defining the model, or in case of \code{return_code = TRUE}, returns the example code without compiling. } \description{ diff --git a/man/drownings.Rd b/man/drownings.Rd index 2357edb0..fc33d1ae 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -8,14 +8,14 @@ A time series object containing 51 observations. } \source{ -Statistics Finland +Statistics Finland \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. } \description{ Dataset containing number of deaths by drowning in Finland in 1969-2019, corresponding population sizes (in hundreds of thousands), and -yearly average summer temperatures (June to August), based on simple -unweighted average of three weather stations: Helsinki (Southern Finland), +yearly average summer temperatures (June to August), based on simple +unweighted average of three weather stations: Helsinki (Southern Finland), Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). } \examples{ diff --git a/man/ekf.Rd b/man/ekf.Rd index 94c5cf67..342de6be 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -9,20 +9,20 @@ ekf(model, iekf_iter = 0) \arguments{ \item{model}{Model of class \code{ssm_nlg}.} -\item{iekf_iter}{Non-negative integer. The default zero corresponds to -normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF with \code{iekf_iter} iterations.} } \value{ List containing the log-likelihood, one-step-ahead predictions \code{at} and filtered estimates \code{att} of states, and the corresponding variances \code{Pt} and - \code{Ptt}. +\code{Ptt}. } \description{ -Function \code{ekf} runs the (iterated) extended Kalman filter for the given -non-linear Gaussian model of class \code{ssm_nlg}, -and returns the filtered estimates and one-step-ahead predictions of the +Function \code{ekf} runs the (iterated) extended Kalman filter for the given +non-linear Gaussian model of class \code{ssm_nlg}, +and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ diff --git a/man/ekf_smoother.Rd b/man/ekf_smoother.Rd index a8e633ae..659035f6 100644 --- a/man/ekf_smoother.Rd +++ b/man/ekf_smoother.Rd @@ -12,20 +12,20 @@ ekf_fast_smoother(model, iekf_iter = 0) \arguments{ \item{model}{Model of class \code{ssm_nlg}.} -\item{iekf_iter}{Non-negative integer. The default zero corresponds to -normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF with \code{iekf_iter} iterations.} } \value{ List containing the log-likelihood, -smoothed state estimates \code{alphahat}, and the corresponding variances +smoothed state estimates \code{alphahat}, and the corresponding variances \code{Vt} and \code{Ptt}. } \description{ -Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother -for the given non-linear Gaussian model of class \code{ssm_nlg}, -and returns the smoothed estimates of the states and the corresponding -variances. Function \code{ekf_fast_smoother} computes only smoothed +Function \code{ekf_smoother} runs the (iterated) extended Kalman smoother +for the given non-linear Gaussian model of class \code{ssm_nlg}, +and returns the smoothed estimates of the states and the corresponding +variances. Function \code{ekf_fast_smoother} computes only smoothed estimates of the states. } \examples{ diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 6abfe370..984b96f3 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -24,11 +24,11 @@ ekpf_filter(object, particles, ...) \item{seed}{Seed for RNG (positive integer).} } \value{ -A list containing samples, filtered estimates and the +A list containing samples, filtered estimates and the corresponding covariances, weights, and an estimate of log-likelihood. } \description{ -Function \code{ekpf_filter} performs a extended Kalman particle filtering +Function \code{ekpf_filter} performs a extended Kalman particle filtering with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ @@ -56,7 +56,7 @@ ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) } } \references{ -Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. -(2001). The unscented particle filter. In Advances in neural +Van Der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. A. +(2001). The unscented particle filter. In Advances in neural information processing systems (pp. 584-590). } diff --git a/man/exchange.Rd b/man/exchange.Rd index cb9b93da..1104774d 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -11,7 +11,7 @@ A vector of length 945. \url{http://www.ssfpack.com/DKbook.html}. } \description{ -Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] +Dataset containing daily log-returns from 1/10/81-28/6/85 as in \link{1} } \examples{ data("exchange") @@ -22,7 +22,7 @@ out <- particle_smoother(model, particles = 500) plot.ts(cbind(model$y, exp(out$alphahat))) } \references{ -James Durbin, Siem Jan Koopman (2012). +James Durbin, Siem Jan Koopman (2012). Time Series Analysis by State Space Methods. Oxford University Press. } \keyword{datasets} diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index f306a242..c1238c88 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -11,21 +11,21 @@ expand_sample(x, variable = "theta", times, states, by_states = TRUE) \item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} -\item{times}{Vector of indices. In case of states, +\item{times}{Vector of indices. In case of states, what time points to expand? Default is all.} -\item{states}{Vector of indices. In case of states, +\item{states}{Vector of indices. In case of states, what states to expand? Default is all.} -\item{by_states}{If \code{TRUE} (default), return list by states. +\item{by_states}{If \code{TRUE} (default), return list by states. Otherwise by time.} } \description{ -The MCMC algorithms of \code{bssm} use a jump chain representation where we -store the accepted values and the number of times we stayed in the current -value. Although this saves bit memory and is especially convenient for -IS-corrected MCMC, sometimes we want to have the usual sample paths. -Function \code{expand_sample} returns the expanded sample based on the -counts. Note that for IS-corrected output the expanded +The MCMC algorithms of \code{bssm} use a jump chain representation where we +store the accepted values and the number of times we stayed in the current +value. Although this saves bit memory and is especially convenient for +IS-corrected MCMC, sometimes we want to have the usual sample paths. +Function \code{expand_sample} returns the expanded sample based on the +counts. Note that for IS-corrected output the expanded sample corresponds to the approximate posterior. } diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index a5247904..5189e3e7 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -13,33 +13,33 @@ gaussian_approx(model, max_iter, conv_tol, ...) \method{gaussian_approx}{ssm_nlg}(model, max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, ...) } \arguments{ -\item{model}{Model to be approximated. Should be of class -\code{bsm_ng}, \code{ar1_ng} \code{svm}, -\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +\item{model}{Model to be approximated. Should be of class +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or non-linear \code{bssm_model}.} -\item{max_iter}{Maximum number of iterations as a positive integer. +\item{max_iter}{Maximum number of iterations as a positive integer. Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation -is claimed to be converged when the mean squared difference of the modes of +\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation +is claimed to be converged when the mean squared difference of the modes of is less than \code{conv_tol}.} \item{...}{Ignored.} -\item{iekf_iter}{For non-linear models, non-negative number of iterations in -iterated EKF (defaults to 0, i.e. normal EKF). Used only for models of class +\item{iekf_iter}{For non-linear models, non-negative number of iterations in +iterated EKF (defaults to 0, i.e. normal EKF). Used only for models of class \code{ssm_nlg}.} } \value{ -Returns linear-Gaussian SSM of class \code{ssm_ulg} or -\code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as - the original model. +Returns linear-Gaussian SSM of class \code{ssm_ulg} or +\code{ssm_mlg} which has the same conditional mode of p(alpha|y, theta) as +the original model. } \description{ -Returns the approximating Gaussian model which has the same conditional -mode of p(alpha|y, theta) as the original model. -This function is rarely needed itself, and is mainly available for +Returns the approximating Gaussian model which has the same conditional +mode of p(alpha|y, theta) as the original model. +This function is rarely needed itself, and is mainly available for testing and debugging purposes. } \examples{ @@ -53,10 +53,10 @@ for(i in 1:7) } \references{ -Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space +Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press. -Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index e3939973..92ec2a22 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -18,20 +18,20 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) ) } \arguments{ -\item{model}{Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, +\item{model}{Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}.} \item{nsim}{Number of samples (positive integer).} -\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic -variable for location in simulation smoothing. Ignored for \code{ssm_mng} +\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic +variable for location in simulation smoothing. Ignored for \code{ssm_mng} models.} -\item{max_iter}{Maximum number of iterations as a positive integer. +\item{max_iter}{Maximum number of iterations as a positive integer. Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation -is claimed to be converged when the mean squared difference of the modes of +\item{conv_tol}{Positive tolerance parameter. Default is 1e-8. Approximation +is claimed to be converged when the mean squared difference of the modes of is less than \code{conv_tol}.} \item{seed}{Seed for the random number generator (positive integer).} @@ -39,8 +39,8 @@ is less than \code{conv_tol}.} \item{...}{Ignored.} } \description{ -Returns \code{nsim} samples from the approximating Gaussian model with -corresponding (scaled) importance weights. +Returns \code{nsim} samples from the approximating Gaussian model with +corresponding (scaled) importance weights. Probably mostly useful for comparing KFAS and bssm packages. } \examples{ diff --git a/man/kfilter.Rd b/man/kfilter.Rd index 6e625b80..303d76bf 100644 --- a/man/kfilter.Rd +++ b/man/kfilter.Rd @@ -13,24 +13,24 @@ kfilter(model, ...) \method{kfilter}{nongaussian}(model, ...) } \arguments{ -\item{model}{Model of class \code{gaussian}, \code{nongaussian} or +\item{model}{Model of class \code{gaussian}, \code{nongaussian} or \code{ssm_nlg}.} \item{...}{Ignored.} } \value{ -List containing the log-likelihood -(approximate in non-Gaussian case), one-step-ahead predictions \code{at} -and filtered estimates \code{att} of states, and the corresponding +List containing the log-likelihood +(approximate in non-Gaussian case), one-step-ahead predictions \code{at} +and filtered estimates \code{att} of states, and the corresponding variances \code{Pt} and \code{Ptt}. } \description{ -Function \code{kfilter} runs the Kalman filter for the given model, -and returns the filtered estimates and one-step-ahead predictions of the +Function \code{kfilter} runs the Kalman filter for the given model, +and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } \details{ -For non-Gaussian models, the filtering is based on the approximate +For non-Gaussian models, the filtering is based on the approximate Gaussian model. } \examples{ diff --git a/man/logLik_bssm.Rd b/man/logLik_bssm.Rd index 47b9e94e..97485d8f 100644 --- a/man/logLik_bssm.Rd +++ b/man/logLik_bssm.Rd @@ -43,35 +43,35 @@ \item{...}{Ignored.} -\item{particles}{Number of samples for particle filter. If 0, -approximate log-likelihood is returned either based on the Gaussian +\item{particles}{Number of samples for particle filter. If 0, +approximate log-likelihood is returned either based on the Gaussian approximation or EKF, depending on the \code{method} argument.} -\item{method}{Sampling method. For Gaussian and non-Gaussian models with -linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default -for non-linear models) -and \code{"psi"} (\eqn{\psi}-APF, the default for other models). -For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle +\item{method}{Sampling method. For Gaussian and non-Gaussian models with +linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default +for non-linear models) +and \code{"psi"} (\eqn{\psi}-APF, the default for other models). +For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle filter (or just EKF/IEKF approximation in the case of \code{particles = 0}).} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. +as a positive integer. Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. Default is 1e-8.} \item{seed}{Seed for RNG (non-negative integer).} -\item{iekf_iter}{Non-negative integer. If zero (default), first -approximation for non-linear Gaussian models is obtained from extended +\item{iekf_iter}{Non-negative integer. If zero (default), first +approximation for non-linear Gaussian models is obtained from extended Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is used with \code{iekf_iter} iterations.} \item{L}{Integer defining the discretization level defined as (2^L).} } \description{ -Computes the log-likelihood of a state space model defined by \code{bssm} +Computes the log-likelihood of a state space model defined by \code{bssm} package. } \examples{ @@ -90,29 +90,29 @@ logLik(model, particles = 10, seed = 1) logLik(model2, particles = 10, seed = 1) } \references{ -Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation -Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. +Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation +Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615. -Shephard, N., & Pitt, M. (1997). Likelihood Analysis of +Shephard, N., & Pitt, M. (1997). Likelihood Analysis of Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667. -Gordon, NJ, Salmond, DJ, Smith, AFM (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107-113. -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators -based on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). -The unscented particle filter. +Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +The unscented particle filter. In Advances in neural information processing systems, p 584-590. -Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press. -Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian -nonlinear state space models. +Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25. } \seealso{ diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 1f830068..d9f7dc8c 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -54,43 +54,43 @@ particle_smoother(model, particles, ...) \item{...}{Ignored.} -\item{method}{Choice of particle filter algorithm. +\item{method}{Choice of particle filter algorithm. For Gaussian and non-Gaussian models with linear dynamics, -options are \code{"bsf"} (bootstrap particle filter, default for -non-linear models) -and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and -for non-linear models option \code{"ekf"} (extended Kalman particle filter) +options are \code{"bsf"} (bootstrap particle filter, default for +non-linear models) +and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and +for non-linear models option \code{"ekf"} (extended Kalman particle filter) is also available.} \item{seed}{Seed for RNG (non-negative integer).} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. +as a positive integer. Default is 100 (although typically only few iterations are needed).} -\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. +\item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. Default is 1e-8.} -\item{iekf_iter}{Non-negative integer. If zero (default), first -approximation for non-linear Gaussian models is obtained from extended +\item{iekf_iter}{Non-negative integer. If zero (default), first +approximation for non-linear Gaussian models is obtained from extended Kalman filter. If \code{iekf_iter > 0}, iterated extended Kalman filter is used with \code{iekf_iter} iterations.} \item{L}{Positive integer defining the discretization level for SDE model.} } \value{ -List with samples (\code{alpha}) from the smoothing distribution +List with samples (\code{alpha}) from the smoothing distribution and corresponding weights (\code{weights}), - as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) - of the states and - estimated log-likelihood (\code{logLik}). +as well as smoothed means and covariances (\code{alphahat} and \code{Vt}) +of the states and +estimated log-likelihood (\code{logLik}). } \description{ -Function \code{particle_smoother} performs particle smoothing -based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary -particle filter (\eqn{\psi}-APF) [2], -or extended Kalman particle filter [3] (or its iterated version [4]). -The smoothing phase is based on the filter-smoother algorithm by [5]. +Function \code{particle_smoother} performs particle smoothing +based on either bootstrap particle filter \link{1}, \eqn{\psi}-auxiliary +particle filter (\eqn{\psi}-APF) \link{2}, +or extended Kalman particle filter \link{3} (or its iterated version \link{4}). +The smoothing phase is based on the filter-smoother algorithm by \link{5}. } \details{ See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. @@ -108,22 +108,22 @@ ts.plot(out$alphahat, rowMeans(out2), col = 1:2) } \references{ -[1] Gordon, NJ, Salmond, DJ, Smith, AFM (1993). -Novel approach to nonlinear/non-Gaussian Bayesian state estimation. +\link{1} Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107-113. -[2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators -based on approximate marginal Markov chain Monte Carlo. +\link{2} Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -[3] Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). -The unscented particle filter. +\link{3} Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +The unscented particle filter. In Advances in neural information processing systems, p 584-590. -[4] Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +\link{4} Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press. -[5] Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian -nonlinear state space models. +\link{5} Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25. } diff --git a/man/post_correct.Rd b/man/post_correct.Rd index c30a4775..4f8860fa 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -16,37 +16,37 @@ post_correct( \arguments{ \item{model}{Model of class \code{nongaussian} or \code{ssm_nlg}.} -\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP -estimate of theta. -While the intended use assumes this is from approximate MCMC, it is not -actually checked, i.e., it is also possible to input previous +\item{mcmc_output}{An output from \code{run_mcmc} used to compute the MAP +estimate of theta. +While the intended use assumes this is from approximate MCMC, it is not +actually checked, i.e., it is also possible to input previous (asymptotically) exact output.} \item{particles}{Number of particles for \eqn{\psi}-APF (positive integer).} \item{threads}{Number of parallel threads (positive integer, default is 1).} -\item{is_type}{Type of IS-correction. Possible choices are -\code{"is3"} for simple importance sampling (weight is computed for each +\item{is_type}{Type of IS-correction. Possible choices are +\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), \code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of -particles used forweight computations is proportional to the length of the +\code{"is1"} for importance sampling type weighting where the number of +particles used forweight computations is proportional to the length of the jump chain block.} \item{seed}{Seed for the random number generator (positive integer).} } \value{ -List with suggested number of particles \code{N} and matrix -containing estimated standard deviations of the log-weights and +List with suggested number of particles \code{N} and matrix +containing estimated standard deviations of the log-weights and corresponding number of particles. } \description{ -Function \code{post_correct} updates previously obtained approximate MCMC -output with post-correction weights leading to asymptotically exact +Function \code{post_correct} updates previously obtained approximate MCMC +output with post-correction weights leading to asymptotically exact weighted posterior, and returns updated MCMC output where components - \code{weights}, \code{posterior}, \code{alpha}, \code{alphahat}, and - \code{Vt} are updated (depending on the original output type). +\code{weights}, \code{posterior}, \code{alpha}, \code{alphahat}, and +\code{Vt} are updated (depending on the original output type). } \examples{ \donttest{ @@ -120,12 +120,12 @@ ggplot(aes(time, mean, colour = method)) + } } \references{ -A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). -Efficient implementation of Markov chain Monte Carlo when using an unbiased -likelihood estimator. Biometrika, 102, 2, 295-313, +A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). +Efficient implementation of Markov chain Monte Carlo when using an unbiased +likelihood estimator. Biometrika, 102, 2, 295-313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 27e36f3c..969970e6 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -16,25 +16,25 @@ ) } \arguments{ -\item{object}{Results object of class \code{mcmc_output} from +\item{object}{Results object of class \code{mcmc_output} from \code{\link{run_mcmc}}} -\item{model}{A \code{bssm_model} object.. -Should have same structure and class as the original model which was used in -\code{run_mcmc}, in order to plug the posterior samples of the model -parameters to the right places. -It is also possible to input the original model for obtaining predictions -for past time points. In this case, set argument +\item{model}{A \code{bssm_model} object.. +Should have same structure and class as the original model which was used in +\code{run_mcmc}, in order to plug the posterior samples of the model +parameters to the right places. +It is also possible to input the original model for obtaining predictions +for past time points. In this case, set argument \code{future} to \code{FALSE}.} \item{nsim}{Positive integer defining number of samples to draw.} -\item{type}{Type of predictions. Possible choices are +\item{type}{Type of predictions. Possible choices are \code{"mean"} \code{"response"}, or \code{"state"} level.} -\item{future}{Default is \code{TRUE}, in which case predictions are for the -future, using posterior samples of (theta, alpha_T+1) i.e. the -posterior samples of hyperparameters and latest states. +\item{future}{Default is \code{TRUE}, in which case predictions are for the +future, using posterior samples of (theta, alpha_T+1) i.e. the +posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} \item{seed}{Seed for RNG (positive integer).} @@ -42,15 +42,15 @@ Otherwise it is assumed that \code{model} corresponds to the original model.} \item{...}{Ignored.} } \value{ -A \code{data.frame} consisting of samples from the predictive +A \code{data.frame} consisting of samples from the predictive posterior distribution. } \description{ -Draw samples from the posterior predictive distribution for future -time points given the posterior draws of hyperparameters \eqn{\theta} and -latent state \eqn{alpha_{n+1}}. Function can also be used to draw samples +Draw samples from the posterior predictive distribution for future +time points given the posterior draws of hyperparameters \eqn{\theta} and +latent state \eqn{alpha_{n+1}}. Function can also be used to draw samples from the posterior predictive distribution - \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. +\eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. } \examples{ library("graphics") diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index a84e724a..b88acd63 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -93,111 +93,111 @@ run_mcmc(model, ...) \item{iter}{Positive integer defining the total number of MCMC iterations.} -\item{output_type}{Either \code{"full"} -(default, returns posterior samples from the posterior -\eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of -theta), or \code{"summary"} (return the mean and variance estimates of the +\item{output_type}{Either \code{"full"} +(default, returns posterior samples from the posterior +\eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of +theta), or \code{"summary"} (return the mean and variance estimates of the states and posterior samples of theta). See details.} -\item{burnin}{Positive integer defining the length of the burn-in period -which is disregarded from the results. Defaults to \code{iter / 2}. -Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the +\item{burnin}{Positive integer defining the length of the burn-in period +which is disregarded from the results. Defaults to \code{iter / 2}. +Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the burn-in period in order to find good proposal distribution.} -\item{thin}{Positive integer defining the thinning rate. All MCMC algorithms -in \code{bssm} use the jump chain representation (see ref [2]), and the -thinning is applied to these blocks. Defaults to 1. -For IS-corrected methods, larger value can also be -statistically more effective. Note: With \code{output_type = "summary"}, -the thinning does not affect the computations of the summary statistics in +\item{thin}{Positive integer defining the thinning rate. All MCMC algorithms +in \code{bssm} use the jump chain representation (see ref \link{2}), and the +thinning is applied to these blocks. Defaults to 1. +For IS-corrected methods, larger value can also be +statistically more effective. Note: With \code{output_type = "summary"}, +the thinning does not affect the computations of the summary statistics in case of pseudo-marginal methods.} \item{gamma}{Tuning parameter for the adaptation of RAM algorithm. Must be between 0 and 1.} -\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. +\item{target_acceptance}{Target acceptance rate for MCMC. Defaults to 0.234. Must be between 0 and 1.} -\item{S}{Matrix defining the initial value for the lower triangular matrix +\item{S}{Matrix defining the initial value for the lower triangular matrix of the RAM algorithm, so that the covariance matrix of the Gaussian proposal -distribution is \eqn{SS'}. Note that for some parameters -(currently the standard deviation, dispersion, and autoregressive parameters -of the BSM and AR(1) models) the sampling is done in unconstrained parameter +distribution is \eqn{SS'}. Note that for some parameters +(currently the standard deviation, dispersion, and autoregressive parameters +of the BSM and AR(1) models) the sampling is done in unconstrained parameter space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient).} \item{end_adaptive_phase}{Logical, if \code{TRUE}, S is held fixed after the burnin period. Default is \code{FALSE}.} -\item{threads}{Number of threads for state simulation. Positive integer +\item{threads}{Number of threads for state simulation. Positive integer (default is 1). Note that parallel computing is only used in the post-correction phase of -IS-MCMC and when sampling the states in case of (approximate) Gaussian +IS-MCMC and when sampling the states in case of (approximate) Gaussian models.} \item{seed}{Seed for the random number generator (positive integer).} -\item{particles}{Number of state samples per MCMC iteration for models other +\item{particles}{Number of state samples per MCMC iteration for models other than linear-Gaussian models. Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} -\item{mcmc_type}{What type of MCMC algorithm should be used for models other +\item{mcmc_type}{What type of MCMC algorithm should be used for models other than linear-Gaussian models? Possible choices are \code{"pm"} for pseudo-marginal MCMC, -\code{"da"} for delayed acceptance version of PMCMC , -\code{"approx"} for approximate inference based on the Gaussian +\code{"da"} for delayed acceptance version of PMCMC , +\code{"approx"} for approximate inference based on the Gaussian approximation of the model, -\code{"ekf"} for approximate inference using extended Kalman filter -(for \code{ssm_nlg}), +\code{"ekf"} for approximate inference using extended Kalman filter +(for \code{ssm_nlg}), or one of the three importance sampling type weighting schemes: -\code{"is3"} for simple importance sampling (weight is computed for each +\code{"is3"} for simple importance sampling (weight is computed for each MCMC iteration independently), \code{"is2"} for jump chain importance sampling type weighting (default), or -\code{"is1"} for importance sampling type weighting where the number of +\code{"is1"} for importance sampling type weighting where the number of particles used for weight computations is proportional to the length of the jump chain block.} -\item{sampling_method}{Method for state sampling when for models other than -linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). +\item{sampling_method}{Method for state sampling when for models other than +linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). If \code{"spdk"}, non-sequential importance sampling based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -is used. If \code{"ekf"}, particle filter based on EKF-proposals are used +is used. If \code{"ekf"}, particle filter based on EKF-proposals are used (only for \code{ssm_nlg} models).} \item{local_approx}{If \code{TRUE} (default), Gaussian approximation -needed for some of the methods is performed at each iteration. -If \code{FALSE}, approximation is updated only once at the start of the +needed for some of the methods is performed at each iteration. +If \code{FALSE}, approximation is updated only once at the start of the MCMC using the initial model.} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, -as a positive integer. +as a positive integer. Default is 100 (although typically only few iterations are needed).} \item{conv_tol}{Positive tolerance parameter used in Gaussian approximation.} -\item{iekf_iter}{Non-negative integer. The default zero corresponds to -normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF -with \code{iekf_iter} iterations. Used only for models of class +\item{iekf_iter}{Non-negative integer. The default zero corresponds to +normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +with \code{iekf_iter} iterations. Used only for models of class \code{ssm_nlg}.} -\item{L_c, L_f}{For \code{ssm_sde} models, Positive integer values defining -the discretization levels for first and second stages (defined as 2^L). +\item{L_c, L_f}{For \code{ssm_sde} models, Positive integer values defining +the discretization levels for first and second stages (defined as 2^L). For pseudo-marginal methods (\code{"pm"}), maximum of these is used.} } \description{ Adaptive Markov chain Monte Carlo simulation for SSMs using -Robust Adaptive Metropolis algorithm by Vihola (2012). Several different -MCMC sampling schemes are implemented, see parameter -arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and +Robust Adaptive Metropolis algorithm by Vihola (2012). Several different +MCMC sampling schemes are implemented, see parameter +arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and Vihola (2021) for details. } \details{ For linear-Gaussian models, option \code{"summary"} does not simulate -states directly but computes the posterior means and variances of states -using fast Kalman smoothing. This is slightly faster, -more memory efficient and more accurate than calculations based on -simulation smoother. In other cases, the means and -covariances are computed using the full output of particle filter -instead of subsampling one of these as in case of +states directly but computes the posterior means and variances of states +using fast Kalman smoothing. This is slightly faster, +more memory efficient and more accurate than calculations based on +simulation smoother. In other cases, the means and +covariances are computed using the full output of particle filter +instead of subsampling one of these as in case of \code{output_type = "full"}. } \examples{ @@ -332,17 +332,17 @@ theme_bw() } \references{ -[1] Vihola M (2012). Robust adaptive Metropolis algorithm with +\link{1} Vihola M (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. -[2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +\link{2} Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -[3] Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and +\link{3} Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 918f15d6..838ca01f 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -27,15 +27,15 @@ sim_smoother(model, nsim, seed, use_antithetic = TRUE, ...) \arguments{ \item{model}{Model of class \code{bsm_lg}, \code{ar1_lg} \code{ssm_ulg}, or \code{ssm_mlg}, or one of the non-gaussian models -\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}.} \item{nsim}{Number of samples (positive integer).} \item{seed}{Seed for the random number generator (positive integer).} -\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic -variable for location in simulation smoothing. Ignored for \code{ssm_mng} +\item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic +variable for location in simulation smoothing. Ignored for \code{ssm_mng} models.} \item{...}{Ignored.} @@ -44,12 +44,12 @@ models.} An array containing the generated samples. } \description{ -Function \code{sim_smoother} performs simulation smoothing i.e. simulates -the states from the conditional distribution \eqn{p(\alpha | y, \theta)} +Function \code{sim_smoother} performs simulation smoothing i.e. simulates +the states from the conditional distribution \eqn{p(\alpha | y, \theta)} for linear-Gaussian models. } \details{ -For non-Gaussian/non-linear models, the simulation is based on the +For non-Gaussian/non-linear models, the simulation is based on the approximating Gaussian model. } \examples{ diff --git a/man/smoother.Rd b/man/smoother.Rd index a7207278..c41fd10c 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -16,9 +16,9 @@ smoother(model, ...) \method{smoother}{gaussian}(model, ...) } \arguments{ -\item{model}{Model to be approximated. Should be of class -\code{bsm_ng}, \code{ar1_ng} \code{svm}, -\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or +\item{model}{Model to be approximated. Should be of class +\code{bsm_ng}, \code{ar1_ng} \code{svm}, +\code{ssm_ung}, or \code{ssm_mng}, or \code{ssm_nlg}, i.e. non-gaussian or non-linear \code{bssm_model}.} \item{...}{Ignored.} @@ -33,7 +33,7 @@ computes only smoothed estimates of the states, and function \code{smoother} computes also smoothed variances. } \details{ -For non-Gaussian models, the smoothing is based on the approximate Gaussian +For non-Gaussian models, the smoothing is based on the approximate Gaussian model. } \examples{ diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index e78f17b3..84af16c2 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -21,19 +21,19 @@ ssm_mlg( ) } \arguments{ -\item{y}{Observations as multivariate time series or matrix with +\item{y}{Observations as multivariate time series or matrix with dimensions n x p.} -\item{Z}{System matrix Z of the observation equation as p x m matrix or +\item{Z}{System matrix Z of the observation equation as p x m matrix or p x m x n array.} -\item{H}{Lower triangular matrix H of the observation. Either a scalar or +\item{H}{Lower triangular matrix H of the observation. Either a scalar or a vector of length n.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a m x m x n array.} -\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array.} \item{a1}{Prior mean for the initial state as a vector of length m.} @@ -48,28 +48,28 @@ or a m x k x n array.} \item{state_names}{Names for the states.} -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and -\code{C}, where each element matches the dimensions of the original model -It's best to check the internal dimensions with \code{str(model_object)} as +\code{C}, where each element matches the dimensions of the original model +It's best to check the internal dimensions with \code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions. -If any of these components is missing, it is assumed to be constant wrt. +If any of these components is missing, it is assumed to be constant wrt. theta.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{Function which returns log of prior density given input vector theta.} } \value{ Object of class \code{ssm_mlg}. } \description{ -Construct an object of class \code{ssm_mlg} by directly defining the +Construct an object of class \code{ssm_mlg} by directly defining the corresponding terms of the model. } \details{ -The general multivariate linear-Gaussian model is defined using the +The general multivariate linear-Gaussian model is defined using the following observational and state equations: \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, @@ -78,19 +78,19 @@ following observational and state equations: (\textrm{transition equation})} where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here p is the number of time series and k is the number of disturbance terms +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. +Here p is the number of time series and k is the number of disturbance terms (which can be less than m, the number of states). -The \code{update_fn} function should take only one +The \code{update_fn} function should take only one vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be +If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_mlg}, -\code{update_fn} should return R as m x k x 1 in this case. +Note that while you can input say R as m x k matrix for \code{ssm_mlg}, +\code{update_fn} should return R as m x k x 1 in this case. It might be useful to first construct the model without updating function } \examples{ diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index 1d37ae1a..b574543d 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -23,16 +23,16 @@ ssm_mng( ) } \arguments{ -\item{y}{Observations as multivariate time series or matrix with dimensions +\item{y}{Observations as multivariate time series or matrix with dimensions n x p.} -\item{Z}{System matrix Z of the observation equation as p x m matrix or +\item{Z}{System matrix Z of the observation equation as p x m matrix or p x m x n array.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a m x m x n array.} -\item{R}{Lower triangular matrix R the state equation. Either a m x k +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array.} @@ -40,19 +40,19 @@ m x k x n array.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} -\item{distribution}{vector of distributions of the observed series. +\item{distribution}{vector of distributions of the observed series. Possible choices are -\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, +\code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, \code{"gamma"}, and \code{"gaussian"}.} \item{phi}{Additional parameters relating to the non-Gaussian distributions. -For negative binomial distribution this is the dispersion term, for -gamma distribution this is the shape parameter, for Gaussian this is +For negative binomial distribution this is the dispersion term, for +gamma distribution this is the shape parameter, for Gaussian this is standard deviation, and for other distributions this is ignored.} -\item{u}{Matrix of positive constants for non-Gaussian models -(of same dimensions as y). For Poisson, gamma, and negative binomial -distribution, this corresponds to the offset term. For binomial, this is the +\item{u}{Matrix of positive constants for non-Gaussian models +(of same dimensions as y). For Poisson, gamma, and negative binomial +distribution, this corresponds to the offset term. For binomial, this is the number of trials.} \item{init_theta}{Initial values for the unknown hyperparameters theta.} @@ -63,28 +63,28 @@ number of trials.} \item{state_names}{Names for the states.} -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and \code{phi}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. -theta. It's best to check the internal dimensions with -\code{str(model_object)} as the dimensions of input arguments can differ +If any of these components is missing, it is assumed to be constant wrt. +theta. It's best to check the internal dimensions with +\code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{Function which returns log of prior density given input vector theta.} } \value{ Object of class \code{ssm_mng}. } \description{ -Construct an object of class \code{ssm_mng} by directly defining the +Construct an object of class \code{ssm_mng} by directly defining the corresponding terms of the model. } \details{ -The general multivariate non-Gaussian model is defined using the following +The general multivariate non-Gaussian model is defined using the following observational and state equations: \deqn{p^i(y^i_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} @@ -92,10 +92,10 @@ observational and state equations: (\textrm{transition equation})} where \eqn{\eta_t \sim N(0, I_k)} and -\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and -\eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or -negative binomial distribution for each observation series \eqn{i=1,...,p}. -Here k is the number of disturbance terms (which can be less than m, +\eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and +\eqn{p^i(y_t | .)} is either Poisson, binomial, gamma, Gaussian, or +negative binomial distribution for each observation series \eqn{i=1,...,p}. +Here k is the number of disturbance terms (which can be less than m, the number of states). } \examples{ diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index caa9b25d..dab2bbd1 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -25,40 +25,40 @@ ssm_nlg( ) } \arguments{ -\item{y}{Observations as multivariate time series (or matrix) of length +\item{y}{Observations as multivariate time series (or matrix) of length \eqn{n}.} -\item{Z, H, T, R}{An external pointers (object of class \code{externalptr}) for the +\item{Z, H, T, R}{An external pointers (object of class \code{externalptr}) for the C++ functions which define the corresponding model functions.} -\item{Z_gn, T_gn}{An external pointers (object of class \code{externalptr}) for -the C++ functions which define the gradients of the corresponding model +\item{Z_gn, T_gn}{An external pointers (object of class \code{externalptr}) for +the C++ functions which define the gradients of the corresponding model functions.} -\item{a1}{Prior mean for the initial state as object of class +\item{a1}{Prior mean for the initial state as object of class \code{externalptr}} -\item{P1}{Prior covariance matrix for the initial state as object of class +\item{P1}{Prior covariance matrix for the initial state as object of class \code{externalptr}} \item{theta}{Parameter vector passed to all model functions.} -\item{known_params}{Vector of known parameters passed to all model +\item{known_params}{Vector of known parameters passed to all model functions.} -\item{known_tv_params}{Matrix of known parameters passed to all model +\item{known_tv_params}{Matrix of known parameters passed to all model functions.} \item{n_states}{Number of states in the model (positive integer).} -\item{n_etas}{Dimension of the noise term of the transition equation +\item{n_etas}{Dimension of the noise term of the transition equation (positive integer).} -\item{log_prior_pdf}{An external pointer (object of class +\item{log_prior_pdf}{An external pointer (object of class \code{externalptr}) for the C++ function which computes the log-prior density given theta.} -\item{time_varying}{Optional logical vector of length 4, denoting whether +\item{time_varying}{Optional logical vector of length 4, denoting whether the values of Z, H, T, and R vary with respect to time variable (given identical states). If used, this can speed up some computations.} @@ -69,7 +69,7 @@ If used, this can speed up some computations.} Object of class \code{ssm_nlg}. } \description{ -Constructs an object of class \code{ssm_nlg} by defining the corresponding +Constructs an object of class \code{ssm_nlg} by defining the corresponding terms of the observation and state equation. } \details{ @@ -82,7 +82,7 @@ The nonlinear Gaussian model is defined as where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, and functions -\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector +\eqn{Z, H, T, R} can depend on \eqn{\alpha_t} and parameter vector \eqn{\theta}. Compared to other models, these general models need a bit more effort from diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index d496593a..89cfa9cc 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -17,15 +17,15 @@ ssm_sde( ) } \arguments{ -\item{y}{Observations as univariate time series (or vector) of length +\item{y}{Observations as univariate time series (or vector) of length \eqn{n}.} -\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions +\item{drift, diffusion, ddiffusion}{An external pointers for the C++ functions which define the drift, diffusion and derivative of diffusion functions of SDE.} \item{obs_pdf}{An external pointer for the C++ function which -computes the observational log-density given the the states and parameter +computes the observational log-density given the the states and parameter vector theta.} \item{prior_pdf}{An external pointer for the C++ function which @@ -48,8 +48,8 @@ as well as the log-density of observation equation. We assume that the observations are measured at integer times (missing values are allowed). } \details{ -As in case of \code{ssm_nlg} models, these general models need a bit more -effort from the user, as you must provide the several small C++ snippets +As in case of \code{ssm_nlg} models, these general models need a bit more +effort from the user, as you must provide the several small C++ snippets which define the model structure. See vignettes for an example. } \examples{ diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 102bb1c2..f5971b60 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -23,17 +23,17 @@ ssm_ulg( \arguments{ \item{y}{Observations as time series (or vector) of length \eqn{n}.} -\item{Z}{System matrix Z of the observation equation. Either a +\item{Z}{System matrix Z of the observation equation. Either a vector of length m, a m x n matrix, or object which can be coerced to such.} -\item{H}{Vector of standard deviations. Either a scalar or a vector of +\item{H}{Vector of standard deviations. Either a scalar or a vector of length n.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a m x m x n array, or object which can be coerced to such.} -\item{R}{Lower triangular matrix R the state equation. Either +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array, or object which can be coerced to such.} \item{a1}{Prior mean for the initial state as a vector of length m.} @@ -50,28 +50,28 @@ m times 1 or m times n matrix.} \item{state_names}{Names for the states.} -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and -\code{C}, where each element matches the dimensions of the original model -It's best to check the internal dimensions with \code{str(model_object)} as +\code{C}, where each element matches the dimensions of the original model +It's best to check the internal dimensions with \code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions. -If any of these components is missing, it is assumed to be constant wrt. +If any of these components is missing, it is assumed to be constant wrt. theta.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{Function which returns log of prior density given input vector theta.} } \value{ Object of class \code{ssm_ulg}. } \description{ -Construct an object of class \code{ssm_ulg} by directly defining the +Construct an object of class \code{ssm_ulg} by directly defining the corresponding terms of the model. } \details{ -The general univariate linear-Gaussian model is defined using the following +The general univariate linear-Gaussian model is defined using the following observational and state equations: \deqn{y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, @@ -81,20 +81,20 @@ observational and state equations: where \eqn{\epsilon_t \sim N(0, 1)}, \eqn{\eta_t \sim N(0, I_k)} and \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other. -Here k is the number of disturbance terms which can be less than m, the +Here k is the number of disturbance terms which can be less than m, the number of states. -The \code{update_fn} function should take only one +The \code{update_fn} function should take only one vector argument which is used to create list with elements named as -\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, +\code{Z}, \code{H} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and \code{C}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. +If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_ulg}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -and then check the expected structure of the model components from the +Note that while you can input say R as m x k matrix for \code{ssm_ulg}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from the output. } \examples{ diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 700e95a0..72b25b7b 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -25,32 +25,32 @@ ssm_ung( \arguments{ \item{y}{Observations as time series (or vector) of length \eqn{n}.} -\item{Z}{System matrix Z of the observation equation. Either a +\item{Z}{System matrix Z of the observation equation. Either a vector of length m, a m x n matrix, or object which can be coerced to such.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a m x m x n array, or object which can be coerced to such.} -\item{R}{Lower triangular matrix R the state equation. Either +\item{R}{Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array, or object which can be coerced to such.} \item{a1}{Prior mean for the initial state as a vector of length m.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} -\item{distribution}{Distribution of the observed time series. Possible -choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and +\item{distribution}{Distribution of the observed time series. Possible +choices are \code{"poisson"}, \code{"binomial"}, \code{"gamma"}, and \code{"negative binomial"}.} \item{phi}{Additional parameter relating to the non-Gaussian distribution. -For negative binomial distribution this is the dispersion term, for gamma -distribution this is the shape parameter, and for other distributions this -is ignored. Should an object of class \code{bssm_prior} or +For negative binomial distribution this is the dispersion term, for gamma +distribution this is the shape parameter, and for other distributions this +is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset +\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} \item{init_theta}{Initial values for the unknown hyperparameters theta.} @@ -63,28 +63,28 @@ m times 1 or m times n matrix.} \item{state_names}{Names for the states.} -\item{update_fn}{Function which returns list of updated model -components given input vector theta. This function should take only one +\item{update_fn}{Function which returns list of updated model +components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and \code{phi}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant wrt. -theta. It's best to check the internal dimensions with -\code{str(model_object)} as the dimensions of input arguments can differ +If any of these components is missing, it is assumed to be constant wrt. +theta. It's best to check the internal dimensions with +\code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{Function which returns log of prior density given input vector theta.} } \value{ Object of class \code{ssm_ung}. } \description{ -Construct an object of class \code{ssm_ung} by directly defining the +Construct an object of class \code{ssm_ung} by directly defining the corresponding terms of the model. } \details{ -The general univariate non-Gaussian model is defined using the following +The general univariate non-Gaussian model is defined using the following observational and state equations: \deqn{p(y_t | D_t + Z_t \alpha_t), (\textrm{observation equation})} @@ -93,22 +93,22 @@ observational and state equations: where \eqn{\eta_t \sim N(0, I_k)} and \eqn{\alpha_1 \sim N(a_1, P_1)} independently of each other, -and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or +and \eqn{p(y_t | .)} is either Poisson, binomial, gamma, or negative binomial distribution. -Here k is the number of disturbance terms which can be less than m, +Here k is the number of disturbance terms which can be less than m, the number of states. -The \code{update_fn} function should take only one +The \code{update_fn} function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{phi} \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, - and \code{C}, +and \code{C}, where each element matches the dimensions of the original model. -If any of these components is missing, it is assumed to be constant +If any of these components is missing, it is assumed to be constant wrt. theta. -Note that while you can input say R as m x k matrix for \code{ssm_ung}, -\code{update_fn} should return R as m x k x 1 in this case. -It might be useful to first construct the model without updating function -and then check the expected structure of the model components from +Note that while you can input say R as m x k matrix for \code{ssm_ung}, +\code{update_fn} should return R as m x k x 1 in this case. +It might be useful to first construct the model without updating function +and then check the expected structure of the model components from the output. } \examples{ diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index fdcab0a8..31f38735 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -15,36 +15,36 @@ suggest_N( \arguments{ \item{model}{Model of class \code{nongaussian} or \code{ssm_nlg}.} -\item{theta}{A vector of theta corresponding to the model, at which point -the standard deviation of the log-likelihood is computed. Typically MAP -estimate from the (approximate) MCMC run. Can also be an output from -\code{run_mcmc} which is then used to compute the MAP +\item{theta}{A vector of theta corresponding to the model, at which point +the standard deviation of the log-likelihood is computed. Typically MAP +estimate from the (approximate) MCMC run. Can also be an output from +\code{run_mcmc} which is then used to compute the MAP estimate of theta.} -\item{candidates}{Vector of positive integers containing the candidate +\item{candidates}{Vector of positive integers containing the candidate number of particles to test. Default is \code{seq(10, 100, by = 10)}.} -\item{replications}{Positive integer, how many replications should be used +\item{replications}{Positive integer, how many replications should be used for computing the standard deviations? Default is 100.} \item{seed}{Seed for the random number generator (positive integer).} } \value{ -List with suggested number of particles \code{N} and matrix -containing estimated standard deviations of the log-weights and +List with suggested number of particles \code{N} and matrix +containing estimated standard deviations of the log-weights and corresponding number of particles. } \description{ -Function \code{estimate_N} estimates suitable number particles needed for +Function \code{estimate_N} estimates suitable number particles needed for accurate post-correction of approximate MCMC. } \details{ -Function \code{suggest_N} estimates the standard deviation of the -logarithm of the post-correction weights at approximate MAP of theta, -using various particle sizes and suggest smallest number of particles -which still leads standard deviation less than 1. Similar approach was +Function \code{suggest_N} estimates the standard deviation of the +logarithm of the post-correction weights at approximate MAP of theta, +using various particle sizes and suggest smallest number of particles +which still leads standard deviation less than 1. Similar approach was suggested in the context of pseudo-marginal MCMC by Doucet et al. (2015), - but see also Section 10.3 in Vihola et al (2020). +but see also Section 10.3 in Vihola et al (2020). } \examples{ @@ -85,12 +85,12 @@ estN$N } \references{ -Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). -Efficient implementation of Markov chain Monte Carlo when using an +Doucet, A, Pitt, MK, Deligiannidis, G, Kohn, R (2015). +Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, Biometrika, 102(2) p. 295-313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators -based on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index c6999a2c..83438330 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -9,30 +9,30 @@ \arguments{ \item{object}{Output from \code{run_mcmc}} -\item{return_se}{if \code{FALSE} (default), computation of standard +\item{return_se}{if \code{FALSE} (default), computation of standard errors and effective sample sizes is omitted.} -\item{variable}{Are the summary statistics computed for either +\item{variable}{Are the summary statistics computed for either \code{"theta"} (default), \code{"states"}, or \code{"both"}?} -\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only +\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only for hyperparameters theta.} \item{...}{Ignored.} } \description{ -This functions returns a list containing mean, standard deviations, -standard errors, and effective sample size estimates for parameters and +This functions returns a list containing mean, standard deviations, +standard errors, and effective sample size estimates for parameters and states. } \details{ -For IS-MCMC two types of standard errors are reported. +For IS-MCMC two types of standard errors are reported. SE-IS can be regarded as the square root of independent IS variance, -whereas SE corresponds to the square root of total asymptotic variance +whereas SE corresponds to the square root of total asymptotic variance (see Remark 3 of Vihola et al. (2020)). } \references{ -Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/svm.Rd b/man/svm.Rd index 3c03656e..d075cf3c 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -9,22 +9,22 @@ svm(y, mu, rho, sd_ar, sigma) \arguments{ \item{y}{Vector or a \code{\link{ts}} object of observations.} -\item{mu}{Prior for mu parameter of transition equation. +\item{mu}{Prior for mu parameter of transition equation. Should be an object of class \code{bssm_prior}.} -\item{rho}{prior for autoregressive coefficient. +\item{rho}{prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} \item{sd_ar}{Prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for sigma parameter of observation equation, internally -denoted as phi. Should be an object of class \code{bssm_prior}. -Ignored if \code{mu} is provided. Note that typically -parametrization using mu is preferred due to better numerical properties and -availability of better Gaussian approximation. -Most notably the global approximation approach does not work with sigma -parameterization as sigma is not a parameter of the resulting approximate +\item{sigma}{Prior for sigma parameter of observation equation, internally +denoted as phi. Should be an object of class \code{bssm_prior}. +Ignored if \code{mu} is provided. Note that typically +parametrization using mu is preferred due to better numerical properties and +availability of better Gaussian approximation. +Most notably the global approximation approach does not work with sigma +parameterization as sigma is not a parameter of the resulting approximate model.} } \value{ diff --git a/man/ukf.Rd b/man/ukf.Rd index 35f9113d..e7344740 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -9,25 +9,25 @@ ukf(model, alpha = 0.001, beta = 2, kappa = 0) \arguments{ \item{model}{Model of class \code{ssm_nlg}.} -\item{alpha}{Positive tuning parameter of the UKF. Default is 0.001. Smaller +\item{alpha}{Positive tuning parameter of the UKF. Default is 0.001. Smaller the value, closer the sigma point are to the mean of the state.} -\item{beta}{Non-negative tuning parameter of the UKF. The default value is +\item{beta}{Non-negative tuning parameter of the UKF. The default value is 2, which is optimal for Gaussian states.} -\item{kappa}{Non-negative tuning parameter of the UKF, which also affects +\item{kappa}{Non-negative tuning parameter of the UKF, which also affects the spread of sigma points. Default value is 0.} } \value{ List containing the log-likelihood, one-step-ahead predictions \code{at} and filtered estimates \code{att} of states, and the corresponding variances \code{Pt} and - \code{Ptt}. +\code{Ptt}. } \description{ -Function \code{ukf} runs the unscented Kalman filter for the given -non-linear Gaussian model of class \code{ssm_nlg}, -and returns the filtered estimates and one-step-ahead predictions of the +Function \code{ukf} runs the unscented Kalman filter for the given +non-linear Gaussian model of class \code{ssm_nlg}, +and returns the filtered estimates and one-step-ahead predictions of the states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index c1b1adc5..ce1b1bce 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -31,6 +31,19 @@ State space models (SSM) are latent variable models which are commonly applied i The motivation behind the `bssm` package is in [@vihola-helske-franks] which suggests a new computationally efficient, parallelisable approach for Bayesian inference of state space models. The core idea is to use fast approximate Markov chain Monte Carlo (MCMC) targeting the approximate marginal posterior of the hyperparameters, which is then used in importance sampling type weighting phase which provides asymptotically exact samples from the joint posterior of hyperparameters and the hidden states. In addition to this the two-stage procedure, standard pseudo-marginal MCMC and so called delayed acceptance pseudo-marginal MCMC are also supported. For more details, see [@helske-vihola2021]. There is also separate vignette for nonlinear models as well as for discretized diffusion models. +```{r srr-tags, eval = FALSE, echo = FALSE} +#' rOpenSci Statistical Software Standards addressed by the vignette +#' +#' @srrstats {G1.1} The \code{bssm} package is +#' first to implement the IS-MCMC method as well as psi-APF particle filter +#' both introduced in Vihola, Helske, Franks (2020). +#' To our knowledge, the package is also the first R package to +#' implement delayed acceptance pseudo-marginal MCMC for general state space +#' models. +#' Note that the IS-MCMC method is also available in \code{walker} package for +#' limited class of models (subset of the models supported by \code{bssm}). +``` + ## State space models with linear-Gaussian dynamics Denote a sequence of observations $(y_1,\ldots,y_T)$ as $y_{1:T}$, and sequence of latent state variables $(\alpha_1,\ldots, \alpha_T)$ as $\alpha_{1:T}$. A general state space model consists of two parts: observation level densities $g_t(y_t | \alpha_t)$ and latent state transition densities $\mu_t(\alpha_{t+1} | \alpha_t)$. We first focus on the case where the state transitions are linear-Gaussian: From d69b50535618550ff2cb21d24abe714bc1fd4800 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 20 Sep 2021 13:23:47 +0300 Subject: [PATCH 100/180] fix seed for predict tests --- DESCRIPTION | 4 ++-- NEWS | 6 +++++- R/predict.R | 4 +++- README.md | 17 ++++++++++++++++- man/predict.mcmc_output.Rd | 4 +++- tests/testthat/test_predict.R | 25 ++++++++++++++++++++----- 6 files changed, 49 insertions(+), 11 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index dd63b830..0ea92de6 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.7 +Version: 1.1.7-1 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -41,7 +41,7 @@ Suggests: Imports: checkmate, coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -RoxygenNote: 7.1.1 +RoxygenNote: 7.1.2 VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues URL: https://github.com/helske/bssm diff --git a/NEWS b/NEWS index 8d326ac8..934f41c2 100644 --- a/NEWS +++ b/NEWS @@ -1,4 +1,8 @@ -bssm 1.1.7 (Release date: 2021-09-15) +bssm 1.1.7-1 (Release date: 2021-09-21) +============== + * Fixed an error in automatic tests due to lack of fixed RNG seed. + +bssm 1.1.7 (Release date: 2021-09-20) ============== * Added a function cpp_example_model which can be used to extract and compile some non-linear and SDE models used in the examples and vignettes. diff --git a/R/predict.R b/R/predict.R index c37b86bc..7f999a8b 100644 --- a/R/predict.R +++ b/R/predict.R @@ -22,7 +22,9 @@ #' future, using posterior samples of (theta, alpha_T+1) i.e. the #' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. -#' @param seed Seed for RNG (positive integer). +#' @param seed Seed for RNG (positive integer). Note that this affects only the +#' C++ side, and \code{predict} also uses R side RNG for subsampling, so for +#' replicable results you should call \code{set.seed} before \code{predict}. #' @param ... Ignored. #' @return A \code{data.frame} consisting of samples from the predictive #' posterior distribution. diff --git a/README.md b/README.md index 12fa5309..871c8880 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,22 @@ devtools::install_github("helske/bssm") Recent changes (For all changes, see NEWS file.) ========================================================================== -bssm 1.1.6 (Release date: ) +bssm 1.1.7-1 (Release date: 2021-09-21) +============== + * Fixed an error in automatic tests due to lack of fixed RNG seed. + +bssm 1.1.7 (Release date: 2021-09-20) +============== + * Added a function cpp_example_model which can be used to extract and + compile some non-linear and SDE models used in the examples and vignettes. + * Added as_draws method for run_mcmc output so samples can be analysed using + the posterior package. + * Added more examples. + * Fixed a tolerance of one MCMC test to pass the test on OSX as well. + * Fixed a bug in iterated extended Kalman smoothing which resulted incorrect + estimates. + +bssm 1.1.6 (Release date: 2021-09-06) ============== * Cleaned codes and added more comprehensive tests in line with pkgcheck tests. This resulted in finding and fixing multiple bugs: diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 27e36f3c..a98e2b67 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -37,7 +37,9 @@ future, using posterior samples of (theta, alpha_T+1) i.e. the posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} -\item{seed}{Seed for RNG (positive integer).} +\item{seed}{Seed for RNG (positive integer). Note that this affects only the +C++ side, and \code{predict} also uses R side RNG for subsampling, so for +replicable results you should call \code{set.seed} before \code{predict}.} \item{...}{Ignored.} } diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index daf10d68..639f2a09 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -14,6 +14,7 @@ test_that("Gaussian predictions work", { mcmc_results <- run_mcmc(model, iter = 1000) future_model <- model future_model$y <- rep(NA, 3) + set.seed(1) pred <- predict(mcmc_results, future_model, type = "mean", nsim = 100) @@ -21,8 +22,10 @@ test_that("Gaussian predictions work", { expect_lt(mean(pred$value[pred$time == 3]), 0.5) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep <- predict(mcmc_results, model, type = "response", future = FALSE, nsim = 100), NA) + set.seed(1) expect_error(meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100), NA) @@ -58,19 +61,21 @@ test_that("Gaussian predictions work", { mcmc_results2$theta[, 2:3] <- log(mcmc_results2$theta[, 2:3]) future_model2 <- model2 future_model2$y <- matrix(NA, 3, 1) + set.seed(1) expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", nsim = 100), NA) expect_equal(pred, pred2) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep2 <- predict(mcmc_results2, model2, type = "response", future = FALSE, nsim = 100), NA) - expect_error(meanrep2 <- predict(mcmc_results2, model2, type = "mean", + set.seed(1) + expect_error(meanrep2 <- predict(mcmc_results2, model2, type = "mean", future = FALSE, nsim = 100), NA) expect_equal(yrep, yrep2) expect_equal(meanrep, meanrep2) expect_error(predict(mcmc_results2, model, type = "response", future = FALSE, nsim = 100)) - }) test_that("Non-gaussian predictions work", { @@ -85,6 +90,7 @@ test_that("Non-gaussian predictions work", { expect_error(mcmc_results <- run_mcmc(model, iter = 1000, particles = 5), NA) future_model <- model future_model$y <- rep(NA, 3) + set.seed(1) expect_error(pred <- predict(mcmc_results, future_model, type = "mean", nsim = 100), NA) @@ -92,12 +98,14 @@ test_that("Non-gaussian predictions work", { expect_lt(mean(pred$value[pred$time == 3]), 1.5) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep <- predict(mcmc_results, model, type = "response", future = FALSE, nsim = 100), NA) + set.seed(1) expect_error(meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100), NA) - expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.5) update_fn <- function(x) { T <- array(x[1]) @@ -128,12 +136,15 @@ test_that("Non-gaussian predictions work", { future_model2 <- model2 future_model2$y <- rep(NA, 3) + set.seed(1) expect_error(pred2 <- predict(mcmc_results2, future_model2, type = "mean", nsim = 100), NA) expect_equal(pred, pred2) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep2 <- predict(mcmc_results2, model2, type = "response", future = FALSE, nsim = 100), NA) + set.seed(1) expect_error(meanrep2 <- predict(mcmc_results2, model2, type = "mean", future = FALSE, nsim = 100), NA) expect_equal(yrep, yrep2) @@ -173,12 +184,14 @@ test_that("Predictions for nlg_ssm work", { expect_lt(mean(pred$value[pred$time == 3]), 1.5) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep <- predict(mcmc_results, model, type = "response", future = FALSE, nsim = 100), NA) + set.seed(1) expect_error(meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100), NA) - expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.5) }) @@ -221,11 +234,13 @@ test_that("Predictions for mng_ssm work", { expect_lt(max(pred$value), 1000) # Posterior predictions for past observations: + set.seed(1) expect_error(yrep <- predict(mcmc_results, model, type = "response", future = FALSE, nsim = 100), NA) + set.seed(1) expect_error(meanrep <- predict(mcmc_results, model, type = "mean", future = FALSE, nsim = 100), NA) - expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.5) }) From 362ac994bb0198d4fd177871ed9d06a57d38f421 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 25 Sep 2021 17:08:27 +0300 Subject: [PATCH 101/180] ... --- NEWS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS b/NEWS index 934f41c2..e5aeec8e 100644 --- a/NEWS +++ b/NEWS @@ -1,6 +1,6 @@ bssm 1.1.7-1 (Release date: 2021-09-21) ============== - * Fixed an error in automatic tests due to lack of fixed RNG seed. + * Fixed an error in automatic tests due to lack of fixed RNG seed. bssm 1.1.7 (Release date: 2021-09-20) ============== From 252e5b4a40f4ea3256edf20ec5207543d29a1959 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 13 Nov 2021 22:01:47 +0200 Subject: [PATCH 102/180] added a fitted method --- .Rbuildignore | 1 + NAMESPACE | 4 + NEWS | 6 ++ R/fitted.R | 112 ++++++++++++++++++++ R/predict.R | 2 +- README.Rmd | 205 ++++++++++++++++++++++--------------- man/fitted.mcmc_output.Rd | 28 +++++ man/predict.mcmc_output.Rd | 2 +- 8 files changed, 274 insertions(+), 86 deletions(-) create mode 100644 R/fitted.R create mode 100644 man/fitted.mcmc_output.Rd diff --git a/.Rbuildignore b/.Rbuildignore index 21c58f1b..5f0fa316 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -15,3 +15,4 @@ ^\.github$ ^codecov\.yml$ ^codemeta\.json$ +^README\.Rmd$ diff --git a/NAMESPACE b/NAMESPACE index 2b118fb4..bd70f0a2 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,6 +8,7 @@ S3method(bootstrap_filter,ssm_sde) S3method(ekpf_filter,ssm_nlg) S3method(fast_smoother,gaussian) S3method(fast_smoother,nongaussian) +S3method(fitted,mcmc_output) S3method(gaussian_approx,nongaussian) S3method(gaussian_approx,ssm_nlg) S3method(importance_sample,nongaussian) @@ -86,13 +87,16 @@ importFrom(coda,mcmc) importFrom(coda,spectrum0.ar) importFrom(diagis,ess) importFrom(diagis,weighted_mean) +importFrom(diagis,weighted_quantile) importFrom(diagis,weighted_se) importFrom(diagis,weighted_var) importFrom(stats,"tsp<-") +importFrom(stats,aggregate) importFrom(stats,as.ts) importFrom(stats,cov) importFrom(stats,dnorm) importFrom(stats,end) +importFrom(stats,fitted) importFrom(stats,frequency) importFrom(stats,is.ts) importFrom(stats,logLik) diff --git a/NEWS b/NEWS index 8d326ac8..5ddec832 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,9 @@ +bssm 1.1.8 (Release date: -) +============== + * Added a fitted method for extraction of summary statistics of posterior + predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. + + bssm 1.1.7 (Release date: 2021-09-15) ============== * Added a function cpp_example_model which can be used to extract and diff --git a/R/fitted.R b/R/fitted.R new file mode 100644 index 00000000..510c2842 --- /dev/null +++ b/R/fitted.R @@ -0,0 +1,112 @@ +#' Fitted for State Space Model +#' +#' Returns summary statistics from the posterior predictive +#' distribution of the mean. +#' +#' @export +#' @importFrom stats fitted aggregate +#' @importFrom diagis weighted_quantile weighted_var weighted_mean weighted_se +#' @name fitted.mcmc_output +#' @param object Results object of class \code{mcmc_output} from +#' \code{\link{run_mcmc}} based on the input model. +#' @param model A \code{bssm_model} object. +#' @param ... Ignored. +#' @examples +#' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +#' model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, +#' sd_slope = prior, sd_seasonal = prior, period = 4) +#' fit <- run_mcmc(model, iter = 2e4) +#' res <- fitted(fit, model) +#' +fitted.mcmc_output <- function(object, model, + probs = c(0.025, 0.975), ...) { + + if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { + if (!identical(nrow(object$alpha) - 1L, nrow(model$y))) { + stop("Number of observations of the model and MCMC output do not match.") + } + } else { + if (!identical(nrow(object$alpha) - 1L, length(model$y))) { + stop("Number of observations of the model and MCMC output do not match.") + } + } + + n <- nrow(object$alpha) - 1L + m <- ncol(object$alpha) + + states <- aperm(object$alpha[1:n, , , drop = FALSE], c(2, 1, 3)) + theta <- t(object$theta) + + switch(attr(object, "model_type"), + ssm_mlg =, + ssm_ulg =, + bsm_lg =, + ar1_lg = { + if (!identical(length(model$a1), m)) { + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) + } + pred <- gaussian_predict_past(model, theta, states, + 2L, 1L, + pmatch(attr(object, "model_type"), + c("ssm_mlg", "ssm_ulg", "bsm_lg", "ar1_lg")) - 1L) + + }, + ssm_mng =, + ssm_ung =, + bsm_ng =, + svm =, + ar1_ng = { + if (!identical(length(model$a1), m)) { + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) + } + model$distribution <- pmatch(model$distribution, + c("svm", "poisson", "binomial", "negative binomial", "gamma", + "gaussian"), + duplicates.ok = TRUE) - 1 + pred <- nongaussian_predict_past(model, theta, states, + 2L, 1L, + pmatch(attr(object, "model_type"), + c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) + + if (anyNA(pred)) + warning("NA or NaN values in predictions, possible under/overflow?") + }, + ssm_nlg = { + if (!identical(model$n_states, m)) { + stop(paste("Model does not correspond to the MCMC output:", + "Wrong number of states. ", sep = " ")) + } + pred <- nonlinear_predict_past(t(model$y), model$Z, + model$H, model$T, model$R, model$Z_gn, + model$T_gn, model$a1, model$P1, + model$log_prior_pdf, model$known_params, + model$known_tv_params, as.integer(model$time_varying), + model$n_states, model$n_etas, + theta, states, 2L, 1L) + + } + , stop("Not yet implemented for ssm_sde. ")) + + variables <- colnames(model$y) + if (is.null(variables)) + variables <- paste("Series", 1:max(1, ncol(model$y))) + w <- object$counts * + (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) + + + d <- data.frame(value = as.numeric(pred), + variable = variables, + time = rep(time(model$y), each = nrow(pred))) + out <- do.call(data.frame, + aggregate(value ~ variable + time, data = d, + FUN = function(x) c( + weighted_mean(x, w), + sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs = probs), + sqrt(asymptotic_var(x, w))))) + names(out)[3:ncol(out)] <- c("Mean", "SD", paste0(probs * 100, "%"), "SE") + out +} + diff --git a/R/predict.R b/R/predict.R index c37b86bc..c664fc1f 100644 --- a/R/predict.R +++ b/R/predict.R @@ -8,7 +8,7 @@ #' #' @param object Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}} -#' @param model A \code{bssm_model} object.. +#' @param model A \code{bssm_model} object. #' Should have same structure and class as the original model which was used in #' \code{run_mcmc}, in order to plug the posterior samples of the model #' parameters to the right places. diff --git a/README.Rmd b/README.Rmd index e5514f67..b28b7d70 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,11 +1,17 @@ - -# bssm: an R package for Bayesian inference of state space models - -[![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) -[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) -[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) +--- +output: github_document +--- + + + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.path = "man/figures/README-", + out.width = "100%" +) +``` ```{r srr-tags, eval = FALSE, echo = FALSE} #' @srrstats {G1.0} Cites of the paper introducing to the package, as well as @@ -24,119 +30,149 @@ #' @srrstatsTODO {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* ``` +# bssm -Efficient methods for Bayesian inference of state space models via particle -Markov chain Monte Carlo and importance sampling type weighted MCMC. + +[![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) +[![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) +[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) + + + +The `bssm` R package provides efficient methods for Bayesian inference of state +space models via particle Markov chain Monte Carlo and importance sampling type +weighted MCMC. Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported. - + For details, see - + * [the bssm paper on ArXiv](https://arxiv.org/abs/2101.08492), * [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) - + There are also couple posters and a talk related to IS-correction methodology and bssm package: + * [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) * [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) * [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) - * [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) -You can install the latest development version from R-universe with +The `bssm` package was originally developed with the support of Academy of +Finland grants 284513, 312605, and 311877. Current development is focused on usability and improvidevelopemnt is -```R -install.packages("bssm", repos = "https://helske.r-universe.dev") +## Installation + +You can install the released version of bssm from [CRAN](https://CRAN.R-project.org) with: + +```{r} +install.packages("bssm") ``` -Or by using the devtools package: +And the development version from [GitHub](https://github.com/) with: -```R -install.packages("devtools") +```{r} +# install.packages("devtools") devtools::install_github("helske/bssm") ``` +Or from R-universe with + +```{r} +install.packages("bssm", repos = "https://helske.r-universe.dev") +``` -## Recent changes (For all changes, see NEWS file.) +## Example -#### bssm 1.1.6 (Release date: 2021-09-06) +This is a basic example which shows you how to solve a common problem: - * Cleaned codes and added more comprehensive tests in line with pkgcheck - tests. This resulted in finding and fixing multiple bugs: - * Fixed a bug in EKF-based particle filter which returned filtered estimates - also in place of one-step ahead predictions. - * Fixed a bug which caused an error in suggest_N for nlg_ssm. - * Fixed a bug which caused incorrect sampling of smoothing distribution for - ar1_lg model when predicting past or when using simulation smoother. - * Fixed a bug which caused an error when predicting past values in - multivariate time series case. - * Fixed sampling of negative binomial distribution in predict method, which - used std::negative_binomial which converts non-integer phi to integer. - Sampling now uses Gamma-Poisson mixture for simulation. - -#### bssm 1.1.4 (Release date: 2021-04-13) +```{r example} +library(bssm) +## basic example code +``` - * Better documentation for SV model, and changed ordering of arguments to emphasise the - recommended parameterization. - * Fixed predict method for SV model. - +## Recent changes (For all changes, see NEWS file.) + +#### bssm 1.1.6 (Release date: 2021-09-06) + + * Cleaned codes and added more comprehensive tests in line with pkgcheck + tests. This resulted in finding and fixing multiple bugs: + * Fixed a bug in EKF-based particle filter which returned filtered estimates + also in place of one-step ahead predictions. + * Fixed a bug which caused an error in suggest_N for nlg_ssm. + * Fixed a bug which caused incorrect sampling of smoothing distribution for + ar1_lg model when predicting past or when using simulation smoother. + * Fixed a bug which caused an error when predicting past values in + multivariate time series case. + * Fixed sampling of negative binomial distribution in predict method, which + used std::negative_binomial which converts non-integer phi to integer. + Sampling now uses Gamma-Poisson mixture for simulation. + +#### bssm 1.1.4 (Release date: 2021-04-13) + + * Better documentation for SV model, and changed ordering of arguments to emphasise the + recommended parameterization. + * Fixed predict method for SV model. + #### bssm 1.1.3-2 (Release date: 2021-02-24) - - * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. - * Added pandoc version >= 1.12.3 to system requirements. - + + * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. + * Added pandoc version >= 1.12.3 to system requirements. + #### bssm 1.1.3-1 (Release date: 2021-02-22) - - * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. - * Added vignette for SDE models. - * Updated citation information and streamlined the main vignette. - + + * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. + * Added vignette for SDE models. + * Updated citation information and streamlined the main vignette. + #### bssm 1.1.2 (Release date: 2021-02-08) - - * Some bug fixes, see NEWS for details. - + + * Some bug fixes, see NEWS for details. + #### bssm 1.1.0 (Release date: 2021-01-19) + + + * Added function `suggest_N` which can be used to choose + suitable number of particles for IS-MCMC. + * Added function `post_correct` which can be used to update + previous approximate MCMC with IS-weights. + * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. + * The adaptation of the proposal distribution now continues also after the burn-in by default. + * Changed default MCMC type to typically most efficient and robust IS2. + * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). + * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. + This resulted error within MCMC algorithms. + * Fixed a dimension drop bug in the predict method which caused error for univariate models. + * Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. - - * Added function `suggest_N` which can be used to choose - suitable number of particles for IS-MCMC. - * Added function `post_correct` which can be used to update - previous approximate MCMC with IS-weights. - * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. - * The adaptation of the proposal distribution now continues also after the burn-in by default. - * Changed default MCMC type to typically most efficient and robust IS2. - * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). - * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. - This resulted error within MCMC algorithms. - * Fixed a dimension drop bug in the predict method which caused error for univariate models. - * Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. - #### bssm 1.0.1-1 (Release date: 2020-11-12) - - + + * Added an argument `future` for predict method which allows - predictions for current time points by supplying the original model - (e.g., for posterior predictive checks). - At the same time the argument name `future_model` was changed to `model`. + predictions for current time points by supplying the original model + (e.g., for posterior predictive checks). + At the same time the argument name `future_model` was changed to `model`. * Fixed a bug in summary.mcmc_run which resulted error when - trying to obtain summary for states only. + trying to obtain summary for states only. * Added a check for Kalman filter for a degenerate case where all - observational level and state level variances are zero. + observational level and state level variances are zero. * Renamed argument `n_threads` to `threads` for consistency - with `iter` and `burnin` arguments. + with `iter` and `burnin` arguments. * Improved documentation, added examples. * Added a vignette regarding psi-APF for non-linear models. #### bssm 1.0.0 (Release date: 2020-06-09) - -Major update - + + Major update + * Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely on C++ snippets). + can be defined via R functions (non-linear and SDE models still rely on C++ snippets). * Added support for multivariate non-Gaussian models. * Added support for gamma distributions. * Added the function as.data.frame for mcmc output which converts the MCMC samples - to data.frame format for easier post-processing. + to data.frame format for easier post-processing. * Added truncated normal prior. * Many argument names and model building functions have been changed for clarity and consistency. * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. @@ -147,20 +183,21 @@ Major update * Reimplemented predict method which now always produces data frame of samples. #### bssm 0.1.11 (Release date: 2020-02-25) - + * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, - as it seems to work better with noisy likelihood estimates. + as it seems to work better with noisy likelihood estimates. * Print and summary methods for MCMC output are now coherent in their output. #### bssm 0.1.10 (Release date: 2020-02-04) - + * Fixed missing weight update for IS-SPDK without OPENMP flag. * Removed unused usage argument ... from expand_sample. #### bssm 0.1.9 (Release date: 2020-01-27) - + * Fixed state sampling for PM-MCMC with SPDK. * Added ts attribute for svm model. * Corrected asymptotic variance for summary methods. -For older versions, see NEWS file. + For older versions, see NEWS file. + diff --git a/man/fitted.mcmc_output.Rd b/man/fitted.mcmc_output.Rd new file mode 100644 index 00000000..4e9d6574 --- /dev/null +++ b/man/fitted.mcmc_output.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/fitted.R +\name{fitted.mcmc_output} +\alias{fitted.mcmc_output} +\title{Fitted for State Space Model} +\usage{ +\method{fitted}{mcmc_output}(object, model, probs = c(0.025, 0.975), ...) +} +\arguments{ +\item{object}{Results object of class \code{mcmc_output} from +\code{\link{run_mcmc}} based on the input model.} + +\item{model}{A \code{bssm_model} object.} + +\item{...}{Ignored.} +} +\description{ +Returns summary statistics from the posterior predictive +distribution of the mean. +} +\examples{ +prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, + sd_slope = prior, sd_seasonal = prior, period = 4) +fit <- run_mcmc(model, iter = 2e4) +res <- fitted(fit, model) + +} diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 969970e6..6ec5add3 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -19,7 +19,7 @@ \item{object}{Results object of class \code{mcmc_output} from \code{\link{run_mcmc}}} -\item{model}{A \code{bssm_model} object.. +\item{model}{A \code{bssm_model} object. Should have same structure and class as the original model which was used in \code{run_mcmc}, in order to plug the posterior samples of the model parameters to the right places. From 1a1fdf27aac2b882202e4dd7f0f64cbb627266f0 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 15 Nov 2021 18:34:07 +0200 Subject: [PATCH 103/180] going through standards --- R/approx.R | 2 +- R/as.data.frame.mcmc_output.R | 2 +- R/as_bssm.R | 10 +-- R/cpp_example_models.R | 2 +- R/fitted.R | 6 ++ R/loglik.R | 11 ++- R/models.R | 3 +- R/particle_smoother.R | 4 +- R/post_correction.R | 2 +- R/predict.R | 6 +- R/print_mcmc.R | 6 +- R/run_mcmc.R | 21 ++--- R/srr-stats-standards.R | 141 +++++++++++++++++++++++----------- README.Rmd | 16 +--- vignettes/bssm.Rmd | 25 +++--- 15 files changed, 155 insertions(+), 102 deletions(-) diff --git a/R/approx.R b/R/approx.R index 1887a7ba..e6cc5c6a 100644 --- a/R/approx.R +++ b/R/approx.R @@ -56,7 +56,7 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, duplicates.ok = TRUE) - 1 out <- gaussian_approx_model(model, model_type(model)) - if (ncol(out$y) == 1) { + if (ncol(out$y) == 1L) { out$y <- ts(c(out$y), start = start(model$y), end = end(model$y), frequency = frequency(model$y)) D <- model$D diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index e856d8d0..9eb9bdcf 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -42,7 +42,7 @@ as.data.frame.mcmc_output <- function(x, times, states, expand = !(x$mcmc_type %in% paste0("is", 1:3)), ...) { - variable <- match.arg(variable, c("theta", "states")) + variable <- match.arg(tolower(variable), c("theta", "states")) if (variable == "theta") { if (expand) { diff --git a/R/as_bssm.R b/R/as_bssm.R index a66865ea..259830ac 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -45,7 +45,7 @@ as_bssm <- function(model, kappa = 100, ...) { } else { R <- model$R * sqrt(c(model$Q)) } - if (attr(model, "p") == 1) { + if (attr(model, "p") == 1L) { Z <- aperm(model$Z, c(2, 3, 1)) dim(Z) <- dim(Z)[1:2] } else { @@ -53,13 +53,13 @@ as_bssm <- function(model, kappa = 100, ...) { } if (any(model$distribution != "gaussian")) { - if (attr(model, "p") == 1) { + if (attr(model, "p") == 1L) { if (model$distribution == "negative binomial" && - length(unique(model$u)) > 1) { + length(unique(model$u)) > 1L) { stop(paste("Time-varying dispersion parameter for negative binomial", "is not (yet) supported in 'bssm'.", sep = " ")) } - if (model$distribution == "gamma" && length(unique(model$u)) > 1) { + if (model$distribution == "gamma" && length(unique(model$u)) > 1L) { stop(paste("Time-varying shape parameter for gamma is not (yet)", "supported in 'bssm'.", sep = " ")) } @@ -129,7 +129,7 @@ as_bssm <- function(model, kappa = 100, ...) { } } else { - if (attr(model, "p") == 1) { + if (attr(model, "p") == 1L) { out <- ssm_ulg(y = model$y, Z = Z, H = sqrt(c(model$H)), T = model$T, R = R, a1 = c(model$a1), P1 = model$P1, state_names = rownames(model$a1), ...) diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 294c8206..7a6e5c09 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -13,7 +13,7 @@ #' cpp_example_model <- function(example, return_code = FALSE) { - example <- match.arg(example, c("nlg_linear_gaussian", "nlg_sin_exp", + example <- match.arg(tolower(example), c("nlg_linear_gaussian", "nlg_sin_exp", "nlg_growth", "nlg_ar_exp", "sde_poisson_OU")) code <- switch(example, diff --git a/R/fitted.R b/R/fitted.R index 510c2842..17d51fe5 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -17,10 +17,14 @@ #' sd_slope = prior, sd_seasonal = prior, period = 4) #' fit <- run_mcmc(model, iter = 2e4) #' res <- fitted(fit, model) +#' head(res) #' fitted.mcmc_output <- function(object, model, probs = c(0.025, 0.975), ...) { + if (!inherits(model, "bbsm_model")) { + stop("Argument 'model' should be an object of class 'bssm_model'.") + } if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { if (!identical(nrow(object$alpha) - 1L, nrow(model$y))) { stop("Number of observations of the model and MCMC output do not match.") @@ -31,6 +35,8 @@ fitted.mcmc_output <- function(object, model, } } + if (any(probs < 0 | probs > 1)) stop("'probs' outside [0, 1].") + n <- nrow(object$alpha) - 1L m <- ncol(object$alpha) diff --git a/R/loglik.R b/R/loglik.R index d3923003..bb03398a 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -69,8 +69,8 @@ logLik.nongaussian <- function(object, particles, method = "psi", max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1), ...) { - object$max_iter <- max_iter - object$conv_tol <- conv_tol + object$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + object$conv_tol <- check_positive_real(conv_tol, "conv_tol") if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -81,7 +81,7 @@ logLik.nongaussian <- function(object, particles, method = "psi", } } - method <- match.arg(method, c("psi", "bsf", "spdk")) + method <- match.arg(tolower(method), c("psi", "bsf", "spdk")) method <- pmatch(method, c("psi", "bsf", "spdk")) if (method == 2 && particles == 0) stop("'particles' must be positive for bootstrap filter.") @@ -113,6 +113,9 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", stop("'particles' must be positive for bootstrap particle filter.") method <- pmatch(method, c("psi", "bsf", NA, "ekf")) + max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + conv_tol <- check_positive_real(conv_tol, "conv_tol") + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) nonlinear_loglik(t(object$y), object$Z, object$H, object$T, @@ -128,6 +131,7 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", #' @export logLik.ssm_sde <- function(object, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { + if (L <= 0) stop("Discretization level L must be larger than 0.") if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -138,6 +142,7 @@ logLik.ssm_sde <- function(object, particles, L, } } seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + loglik_sde(object$y, object$x0, object$positive, object$drift, object$diffusion, object$ddiffusion, object$prior_pdf, object$obs_pdf, object$theta, diff --git a/R/models.R b/R/models.R index 1e017991..a06fff5a 100644 --- a/R/models.R +++ b/R/models.R @@ -265,7 +265,8 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' m times 1 or m times n matrix. #' @param D Intercept terms \eqn{D_t} for the observations equation, given as a #' scalar or vector of length n. -#' @param init_theta Initial values for the unknown hyperparameters theta. +#' @param init_theta Initial values for the unknown hyperparameters theta +#' (i.e. unknown variables excluding latent state variables). #' @param update_fn Function which returns list of updated model #' components given input vector theta. This function should take only one #' vector argument which is used to create list with elements named as diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 7a5a01a8..24f75eaa 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -149,7 +149,7 @@ particle_smoother.nongaussian <- function(model, particles, model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") - method <- match.arg(method, c("bsf", "psi")) + method <- match.arg(tolower(method), c("bsf", "psi")) model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), @@ -199,7 +199,7 @@ particle_smoother.ssm_nlg <- function(model, particles, conv_tol <- check_positive_real(conv_tol, "conv_tol") iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) - method <- match.arg(method, c("bsf", "psi", "ekf")) + method <- match.arg(tolower(method), c("bsf", "psi", "ekf")) out <- switch(method, psi = psi_smoother_nlg(t(model$y), model$Z, model$H, model$T, diff --git a/R/post_correction.R b/R/post_correction.R index c01e69fa..1bb8fa86 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -241,7 +241,7 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") - is_type <- pmatch(match.arg(is_type, paste0("is", 1:3)), paste0("is", 1:3)) + is_type <- pmatch(match.arg(tolower(is_type), paste0("is", 1:3)), paste0("is", 1:3)) a <- proc.time() if (inherits(model, "nongaussian")) { diff --git a/R/predict.R b/R/predict.R index c664fc1f..96ce1800 100644 --- a/R/predict.R +++ b/R/predict.R @@ -118,15 +118,15 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", future = TRUE, seed = sample(.Machine$integer.max, size = 1), ...) { - if (!inherits(model, "bssm_model")) { - stop("Argument 'model' should be of class 'bssm_model'. ") + if (!inherits(model, "bbsm_model")) { + stop("Argument 'model' should be an object of class 'bssm_model'.") } nsim <- check_integer(nsim, "nsim") seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") - type <- match.arg(type, c("response", "mean", "state")) + type <- match.arg(tolower(type), c("response", "mean", "state")) if (object$output_type != 1) stop("MCMC output must contain posterior samples of the states.") diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 9a6004fc..23e67a7f 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -145,7 +145,7 @@ print.mcmc_output <- function(x, ...) { #' @param variable Are the summary statistics computed for either #' \code{"theta"} (default), \code{"states"}, or \code{"both"}? #' @param only_theta Deprecated. If \code{TRUE}, summaries are computed only -#' for hyperparameters theta. +#' for hyperparameters theta, not latent states alpha. #' @param ... Ignored. #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based @@ -166,7 +166,7 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", warning(paste("Argument 'only_theta' is deprecated. Use argument", "'variable' instead. ", sep = " ")) } - variable <- match.arg(variable, c("theta", "states", "both")) + variable <- match.arg(tolower(variable), c("theta", "states", "both")) if (variable %in% c("theta", "both")) { if (object$mcmc_type %in% paste0("is", 1:3)) { @@ -296,7 +296,7 @@ expand_sample <- function(x, variable = "theta", times, states, if (!test_flag(by_states)) stop("Argument 'by_states' should be TRUE or FALSE. ") - variable <- match.arg(variable, c("theta", "states")) + variable <- match.arg(tolower(variable), c("theta", "states")) if (x$mcmc_type %in% paste0("is", 1:3)) warning(paste("Input is based on a IS-weighted MCMC, the results", "correspond to the approximate posteriors.", sep = " ")) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 84604710..5df12c13 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -153,7 +153,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", check_prop(target_acceptance) check_prop(gamma, "gamma") - output_type <- pmatch(output_type, c("full", "summary", "theta")) + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) if (inherits(model, "bsm_lg")) { names_ind <- !model$fixed & c(TRUE, TRUE, model$slope, model$seasonal) @@ -371,15 +371,16 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", check_prop(target_acceptance) check_prop(gamma, "gamma") - output_type <- pmatch(output_type, c("full", "summary", "theta")) - mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), "approx")) + output_type <- pmatch(tolwer(output_type), c("full", "summary", "theta")) + mcmc_type <- match.arg(tolower(mcmc_type), + c("pm", "da", paste0("is", 1:3), "approx")) if (mcmc_type == "approx") particles <- 0 if (particles < 2 && mcmc_type != "approx") stop(paste("Number of state samples less than 2, use 'mcmc_type' 'approx'", "instead.", sep = " ")) sampling_method <- - pmatch(match.arg(sampling_method, c("psi", "bsf", "spdk")), + pmatch(match.arg(tolower(sampling_method), c("psi", "bsf", "spdk")), c("psi", "bsf", "spdk")) dists <- @@ -527,12 +528,12 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", check_prop(target_acceptance) check_prop(gamma, "gamma") - output_type <- pmatch(output_type, c("full", "summary", "theta")) - mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3), + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) + mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3), "ekf", "approx")) if (mcmc_type %in% c("ekf", "approx")) particles <- 0 - sampling_method <- pmatch(match.arg(sampling_method, c("psi", "bsf", "ekf")), - c("psi", "bsf", NA, "ekf")) + sampling_method <- pmatch(match.arg(tolower(sampling_method), + c("psi", "bsf", "ekf")), c("psi", "bsf", NA, "ekf")) if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) @@ -682,8 +683,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", check_prop(target_acceptance) check_prop(gamma, "gamma") - output_type <- pmatch(output_type, c("full", "summary", "theta")) - mcmc_type <- match.arg(mcmc_type, c("pm", "da", paste0("is", 1:3))) + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) + mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3))) if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 32a7f987..17d150bb 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -8,55 +8,78 @@ #' (These comments may be deleted at any time.) #' #' @srrstatsVerbose TRUE +#' +#' +#' ### Standards for general statistical software ### +#' +#' # General documentation, addressed by the paper vignette and the corresponding R Journal paper +#' +#' @srrstats {G1.0} *Statistical Software should list at least one primary reference from published academic literature.* +#' @srrstats {G1.1} *Statistical Software should document whether the algorithm(s) it implements are:* - *The first implementation of a novel algorithm*; or - *The first implementation within **R** of an algorithm which has previously been implemented in other languages or contexts*; or - *An improvement on other implementations of similar algorithms in **R***. +#' @srrstats {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* +#' @srrstats {G1.3} *All statistical terminology should be clarified and unambiguously defined.* +#' @srrstats {G1.4} *Software should use [`roxygen2`](https://roxygen2.r-lib.org/) to document all functions.* +#' @srrstats {G1.4a} *All internal (non-exported) functions should also be documented in standard [`roxygen2`](https://roxygen2.r-lib.org/) format, along with a final `@noRd` tag to suppress automatic generation of `.Rd` files.* +#' @srrstats {G1.5} *Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.* +#' @srrstats {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* +#' +#' # As tested by autotest +#' @srrstats {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* +#' @srrstats {G2.0a} Provide explicit secondary documentation of any expectations on lengths of inputs +#' @srrstats {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* +#' @srrstats {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* +#' @srrstats {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* #' -#' @srrstatsTODO {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* -#' @srrstatsTODO {G2.0a} Provide explicit secondary documentation of any expectations on lengths of inputs -#' @srrstatsTODO {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* -#' @srrstatsTODO {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* -#' @srrstatsTODO {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* -#' @srrstatsTODO {G2.3} *For univariate character input:* -#' @srrstatsTODO {G2.3a} *Use `match.arg()` or equivalent where applicable to only permit expected values.* -#' @srrstatsTODO {G2.3b} *Either: use `tolower()` or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.* -#' @srrstatsTODO {G2.4} *Provide appropriate mechanisms to convert between different data types, potentially including:* -#' @srrstatsTODO {G2.4a} *explicit conversion to `integer` via `as.integer()`* -#' @srrstatsTODO {G2.4b} *explicit conversion to continuous via `as.numeric()`* -#' @srrstatsTODO {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* -#' @srrstatsTODO {G2.4d} *explicit conversion to factor via `as.factor()`* -#' @srrstatsTODO {G2.4e} *explicit conversion from factor via `as...()` functions* -#' @srrstatsTODO {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* -#' @srrstatsTODO {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* -#' @srrstatsTODO {G2.7} *Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.* -#' @srrstatsTODO {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* -#' @srrstatsTODO {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* -#' @srrstatsTODO {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* -#' @srrstatsTODO {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* -#' @srrstatsTODO {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* -#' @srrstatsTODO {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* -#' @srrstatsTODO {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* -#' @srrstatsTODO {G2.14a} *error on missing data* -#' @srrstatsTODO {G2.14b} *ignore missing data with default warnings or messages issued* -#' @srrstatsTODO {G2.14c} *replace missing data with appropriately imputed values* +#' ## Explicit conversions are used where necessary +#' @srrstats {G2.4} *Provide appropriate mechanisms to convert between different data types, potentially including:* +#' @srrstats {G2.4a} *explicit conversion to `integer` via `as.integer()`* +#' @srrstats {G2.4b} *explicit conversion to continuous via `as.numeric()`* +#' @srrstats {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* +#' +#' @srrstats {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* +#' +#' ## match.arg and tolower are used where applicable. +#' @srrstats {G2.3} *For univariate character input:* +#' @srrstats {G2.3a} *Use `match.arg()` or equivalent where applicable to only permit expected values.* +#' @srrstats {G2.3b} *Either: use `tolower()` or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.* + +#' ## Only matrix/mts/arrays are supported, not data.frame style objects. +#' @srrstats {G2.7} *Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.* +#' @srrstats {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* +#' @srrstats {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* +#' +#' ## Missing observations are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere +#' @srrstats {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* +#' @srrstats {G2.14a} *error on missing data* +#' @srrstats {G2.14b} *ignore missing data with default warnings or messages issued* +#' @srrstats {G2.14c} *replace missing data with appropriately imputed values* +#' +#' # This is currently checked when building the model, should there be another check in say run_mcmc in case user manually alters the input model? #' @srrstatsTODO {G2.15} *Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default `na.rm = FALSE`-type parameters (such as [`mean()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/mean.html), [`sd()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) or [`cor()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html)).* #' @srrstatsTODO {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* +#' #' @srrstatsTODO {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* -#' @srrstatsTODO {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* -#' @srrstatsTODO {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* -#' @srrstatsTODO {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* -#' @srrstatsTODO {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* -#' @srrstatsTODO {G5.1} *Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.* -#' @srrstatsTODO {G5.2} *Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,* -#' @srrstatsTODO {G5.2a} *Every message produced within R code by `stop()`, `warning()`, `message()`, or equivalent should be unique* +#' +#' # Simulated datasets are used in several occasions +#' @srrstats {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* +#' @srrstats {G5.1} *Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.* + +#' @srrstats {G5.2} *Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,* +#' @srrstats {G5.2a} *Every message produced within R code by `stop()`, `warning()`, `message()`, or equivalent should be unique* #' @srrstatsTODO {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* #' @srrstatsTODO {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* -#' @srrstatsTODO {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* -#' @srrstatsTODO {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* -#' @srrstatsTODO {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* -#' @srrstatsTODO {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* -#' @srrstatsTODO {G5.5} *Correctness tests should be run with a fixed random seed* -#' @srrstatsTODO {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* -#' @srrstatsTODO {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* -#' @srrstatsTODO {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* -#' @srrstatsTODO {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* + +#' ## Correctness is demonstrated in Helske & Vihola (2021) and Vihola & Helske & Franks (2020) +#' ## Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation +#' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* +#' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* +#' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* +#' @srrstats {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* +#' @srrstats {G5.5} *Correctness tests should be run with a fixed random seed* +#' @srrstats {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* +#' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* +#' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* +#' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* #' @srrstatsTODO {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* #' @srrstatsTODO {G5.8a} *Zero-length data* #' @srrstatsTODO {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* @@ -69,9 +92,35 @@ #' @srrstatsTODO {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* #' @srrstatsTODO {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* #' @srrstatsTODO {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* -#' -#' @srrstatsTODO {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* -#' @srrstatsTODO {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* + + +#' # Standards not applicable +#' +#' ## Factor types are not used nor supported: +#' @srrstatsNA {G2.4d} *explicit conversion to factor via `as.factor()`* +#' @srrstatsNA {G2.4e} *explicit conversion from factor via `as...()` functions* +#' @srrstatsNA {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* +#' +#' # No data.frame style tabular data is used/supported as input +#' @srrstatsNA {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* +#' @srrstatsNA {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* +#' @srrstatsNA {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* +#' @srrstatsNA {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* + + +#' @srrstatsNA {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* +#' @srrstatsNA {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* + +#' # No output is written to local files +#' @srrstatsNA {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* + + +# Standards for Bayesian software + +#' ## addressed by the paper vignette and the corresponding R Journal paper +#' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* +#' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* + #' @srrstatsTODO {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* #' @srrstatsTODO {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* #' @srrstatsTODO {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* diff --git a/README.Rmd b/README.Rmd index b28b7d70..60d75445 100644 --- a/README.Rmd +++ b/README.Rmd @@ -14,21 +14,7 @@ knitr::opts_chunk$set( ``` ```{r srr-tags, eval = FALSE, echo = FALSE} -#' @srrstats {G1.0} Cites of the paper introducing to the package, as well as -#' published paper proposing the IS-MCMC method used in \code{bssm}. -#' @srrstats {G1.1} The \code{bssm} package is -#' first to implement the IS-MCMC method as well as psi-APF particle filter -#' both introduced in Vihola, Helske, Franks (2020). -#' To our knowledge, the package is also the first R package to -#' implement delayed acceptance pseudo-marginal MCMC for general state space -#' models. -#' Note that the IS-MCMC method is also available in \code{walker} package for -#' limited class of models (subset of the models supported by \code{bssm}). -#' @srrstatsTODO {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* -#' @srrstats {G1.3} Terminology is introduced in the vignettes as well as in the corresponding papers. -#' @srrstatsTODO {G1.5} *Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.* -#' @srrstatsTODO {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* - +#' @srrstatsTODO {G1.2} Contains project status badge. ``` # bssm diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index ce1b1bce..7df31143 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -25,24 +25,29 @@ if (!requireNamespace("rmarkdown") || knitr::opts_chunk$set(echo = TRUE) ``` -# Introduction - -State space models (SSM) are latent variable models which are commonly applied in analysing time series data due to their flexible and general framework [cf. @DK2012]. For `R` [@r-core], there is large number of packages available for state space modelling, especially for the two special cases. First special case is linear-Gaussian SSM (LGSSM) where both the observation and state densities are Gaussian with linear relationships with the states. Another special case is SSM with discrete state space, which are sometimes called hidden Markov models (HMM). What is special about these two classes of models is that the marginal likelihood function, and the conditional state distributions (conditioned on the observations) of these models are analytically tractable, making inference relatively straightforward. See for example [@Petris2010, @Tusell2010, @KFAS, @seqHMM] for review of some of the `R` packages dealing with these type of models. The `R` package `bssm` is designed for Bayesian inference of general state space models with non-Gaussian and/or non-linear observational and state equations. The package aims to provide easy-to-use and efficient functions for fully Bayesian inference of common time series models such basic structural time series model (BSM) [@Harvey1989] with exogenous covariates, simple stochastic volatility models, and discretized diffusion models, making it straightforward and efficient to make predictions and other inference in a Bayesian setting. - -The motivation behind the `bssm` package is in [@vihola-helske-franks] which suggests a new computationally efficient, parallelisable approach for Bayesian inference of state space models. The core idea is to use fast approximate Markov chain Monte Carlo (MCMC) targeting the approximate marginal posterior of the hyperparameters, which is then used in importance sampling type weighting phase which provides asymptotically exact samples from the joint posterior of hyperparameters and the hidden states. In addition to this the two-stage procedure, standard pseudo-marginal MCMC and so called delayed acceptance pseudo-marginal MCMC are also supported. For more details, see [@helske-vihola2021]. There is also separate vignette for nonlinear models as well as for discretized diffusion models. - ```{r srr-tags, eval = FALSE, echo = FALSE} #' rOpenSci Statistical Software Standards addressed by the vignette #' -#' @srrstats {G1.1} The \code{bssm} package is -#' first to implement the IS-MCMC method as well as psi-APF particle filter -#' both introduced in Vihola, Helske, Franks (2020). +#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6} Here and in the R Journal paper. #' To our knowledge, the package is also the first R package to #' implement delayed acceptance pseudo-marginal MCMC for general state space #' models. #' Note that the IS-MCMC method is also available in \code{walker} package for #' limited class of models (subset of the models supported by \code{bssm}). -``` +``` + + +This is a short vignette illustrating the `bssm` package. For more detailed exposition, please see the corresponding R Journal paper: + +Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R". Accepted to R Journal. [ArXiv preprint]( +https://arxiv.org/abs/2101.08492). + + +# Introduction + +State space models (SSM) are latent variable models which are commonly applied in analysing time series data due to their flexible and general framework [cf. @DK2012]. For `R` [@r-core], there is large number of packages available for state space modelling, especially for the two special cases. First special case is linear-Gaussian SSM (LGSSM) where both the observation and state densities are Gaussian with linear relationships with the states. Another special case is SSM with discrete state space, which are sometimes called hidden Markov models (HMM). What is special about these two classes of models is that the marginal likelihood function, and the conditional state distributions (conditioned on the observations) of these models are analytically tractable, making inference relatively straightforward. See for example [@Petris2010, @Tusell2010, @KFAS, @seqHMM] for review of some of the `R` packages dealing with these type of models. The `R` package `bssm` is designed for Bayesian inference of general state space models with non-Gaussian and/or non-linear observational and state equations. The package aims to provide easy-to-use and efficient functions for fully Bayesian inference of common time series models such basic structural time series model (BSM) [@Harvey1989] with exogenous covariates, simple stochastic volatility models, and discretized diffusion models, making it straightforward and efficient to make predictions and other inference in a Bayesian setting. + +The motivation behind the `bssm` package is in [@vihola-helske-franks] which suggests a new computationally efficient, parallelisable approach for Bayesian inference of state space models. The core idea is to use fast approximate Markov chain Monte Carlo (MCMC) targeting the approximate marginal posterior of the hyperparameters (i.e. unknown variables excluding latent state variables), which is then used in importance sampling type weighting phase which provides asymptotically exact samples from the joint posterior of hyperparameters and the hidden states. In addition to this the two-stage procedure, standard pseudo-marginal MCMC and so called delayed acceptance pseudo-marginal MCMC are also supported. For more details, see [@helske-vihola2021]. There is also separate vignette for nonlinear models as well as for discretized diffusion models. ## State space models with linear-Gaussian dynamics From 1ba00864f0b0290e2f1206b90c7d92c0e998e22f Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 16 Nov 2021 10:13:25 +0200 Subject: [PATCH 104/180] use tidyr in as_draws --- R/as.data.frame.mcmc_output.R | 14 ++++++--- R/as_draws.R | 57 +++++++++++++++++++---------------- 2 files changed, 41 insertions(+), 30 deletions(-) diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index 9eb9bdcf..e1b3c26a 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -16,6 +16,9 @@ #' Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. #' For \code{expand = FALSE} and always for IS-MCMC, #' the resulting data.frame contains variable weight (= counts * IS-weights). +#' @param use_times If \code{TRUE} (default), transforms the values of the time +#' variable to match the ts attribute of the input to define. If \code{FALSE}, +#' time is based on the indexing starting from 1. #' @param ... Ignored. #' @export #' @examples @@ -40,7 +43,8 @@ as.data.frame.mcmc_output <- function(x, row.names, optional, variable = c("theta", "states"), times, states, - expand = !(x$mcmc_type %in% paste0("is", 1:3)), ...) { + expand = !(x$mcmc_type %in% paste0("is", 1:3)), + use_times = TRUE, ...) { variable <- match.arg(tolower(variable), c("theta", "states")) @@ -77,9 +81,11 @@ as.data.frame.mcmc_output <- function(x, weights <- x$counts * (if (x$mcmc_type %in% paste0("is", 1:3)) x$weights else 1) } - times <- time(ts(seq_len(nrow(x$alpha)), - start = attr(x, "ts")$start, - frequency = attr(x, "ts")$frequency))[times] + if (use_times) { + times <- time(ts(seq_len(nrow(x$alpha)), + start = attr(x, "ts")$start, + frequency = attr(x, "ts")$frequency))[times] + } d <- data.frame(iter = iters, value = as.numeric(values), variable = rep(colnames(x$alpha)[states], each = nrow(values)), diff --git a/R/as_draws.R b/R/as_draws.R index d764ade7..fd76fc3d 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -3,16 +3,20 @@ #' Converts MCMC output from \code{run_mcmc} call to a #' \code{draws_df} format of the \code{posterior} package. This enables the use #' of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} -#' packages. Note though that if \code{run_mcmc} used IS-MCMC -#' method, the resulting \code{weight} column of the output is -#' ignored by the aforementioned packages, i.e. the results correspond to -#' approximate MCMC. +#' packages. +#' +#' @note The jump chain representation is automatically expanded by +#' \code{as_draws}, but if \code{run_mcmc} used IS-MCMC method, the output +#' contains additional \code{weight} column corresponding to the IS-weights +#' (without counts), which is ignored by \code{posterior} and \code{bayesplot}, +#' i.e. those results correspond to approximate MCMC. #' #' @param x An object of class \code{mcmc_output} #' @return A \code{draws_df} object. -#' @exportS3Method posterior::as_draws_df mcmc_output -#' @export #' @rdname as_draws +#' @importFrom posterior as_draws as_draws_df +#' @importFrom tidyr pivot_wider +#' @exportS3Method posterior::as_draws_df mcmc_output #' @examples #' #' model <- bsm_lg(Nile, @@ -37,37 +41,38 @@ #' as_draws(fit2), as_draws(fit3), along = "chain") #' # it is actually enough to transform first mcmc_output to draws object, #' # rest are transformed automatically inside bind_draws -#' rhat(draws$sd_y) -#' ess_bulk(draws$sd_y) -#' ess_tail(draws$sd_y) -#' +#' posterior::rhat(draws$sd_y) +#' posterior::ess_bulk(draws$sd_y) +#' posterior::summarise_draws(draws) +#' as_draws_df.mcmc_output <- function(x) { + d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) - d_states <- as.data.frame(x, variable = "states", expand = TRUE) + d_states <- as.data.frame(x, variable = "states", expand = TRUE, + use_times = FALSE) - d <- data.frame(.iteration = (x$iter - x$burnin + 1):x$iter) + d <- merge( + tidyr::pivot_wider(d_theta, + values_from = value, + names_from = variable), + tidyr::pivot_wider(d_states, + values_from = value, + names_from = c(variable, time), + names_glue = "{variable}[{time}]")) + names(d)[1] <- ".iteration" if (x$mcmc_type %in% paste0("is", 1:3)) { - warning(paste("Input is based on a IS-MCMC, the output column 'weight'", + warning(paste("Input is based on a IS-MCMC, the output column '.weight'", "contains the IS-weights, but these are not used for example in the", "diagnostic methods by 'posterior' package, i.e. these are based", "on approximate MCMC chains.")) - d$weight <- d_theta$weight + names(d)[2] <- ".weight" + } else { + d$weight <- NULL } - for (variable in unique(d_theta$variable)) { - d[variable] <- d_theta$value[d_theta$variable == variable] - } - times <- unique(d_states$time) - for (variable in unique(d_states$variable)) { - for (i in times) - d[paste0(variable, "[", i, "]")] <- - d_states$value[d_states$variable == variable & d_states$time == i] - } - posterior::as_draws(d) + as_draws(d) } #' @exportS3Method posterior::as_draws mcmc_output -#' @export -#' @rdname as_draws as_draws.mcmc_output <- function(x) as_draws_df.mcmc_output(x) \ No newline at end of file From 5639570afe8b24f2973377ad3ad4225facdf7522 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 16 Nov 2021 15:23:08 +0200 Subject: [PATCH 105/180] draws, diagnostics, ess computation --- DESCRIPTION | 110 ++++++++++++++++--------------- NAMESPACE | 16 +++-- R/as_draws.R | 20 +++--- R/bssm-package.R | 1 + R/check_diagnostics.R | 85 ++++++++++++++++++++++++ R/expand_sample.R | 88 +++++++++++++++++++++++++ R/fitted.R | 28 ++++---- R/particle_smoother.R | 7 +- R/predict.R | 2 +- R/print_mcmc.R | 75 ++++----------------- R/run_mcmc.R | 6 +- R/srr-stats-standards.R | 15 +++-- inst/CITATION | 7 +- man/as.data.frame.mcmc_output.Rd | 5 ++ man/as_draws.Rd | 30 +++++---- man/check_diagnostics.Rd | 46 +++++++++++++ man/exchange.Rd | 1 + man/expand_sample.Rd | 28 +++++++- man/fitted.mcmc_output.Rd | 1 + man/particle_smoother.Rd | 6 +- man/predict.mcmc_output.Rd | 2 +- man/run_mcmc.Rd | 4 +- man/ssm_mlg.Rd | 3 +- man/ssm_mng.Rd | 3 +- man/ssm_ulg.Rd | 3 +- man/ssm_ung.Rd | 3 +- man/summary.mcmc_output.Rd | 2 +- 27 files changed, 421 insertions(+), 176 deletions(-) create mode 100644 R/check_diagnostics.R create mode 100644 R/expand_sample.R create mode 100644 man/check_diagnostics.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 7fa8a2c1..189d0652 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,52 +1,58 @@ -Package: bssm -Type: Package -Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space - Models -Version: 1.1.7 -Authors@R: - c(person(given = "Jouni", - family = "Helske", - role = c("aut", "cre"), - email = "jouni.helske@iki.fi", - comment = c(ORCID = "0000-0001-7130-793X")), - person(given = "Matti", - family = "Vihola", - role = "aut", - comment = c(ORCID = "0000-0002-8041-7222"))) -Description: Efficient methods for Bayesian inference of state space models - via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel - importance sampling type weighted estimators - (Vihola, Helske, and Franks, 2020, ). - Gaussian, Poisson, binomial, negative binomial, and Gamma - observation densities and basic stochastic volatility models - with linear-Gaussian state dynamics, - as well as general non-linear Gaussian models and discretised - diffusion models are supported. -License: GPL (>= 2) -Depends: R (>= 3.5.0) -Suggests: - covr, - dplyr, - ggplot2 (>= 2.0.0), - Hmisc, - KFAS (>= 1.2.1), - knitr (>= 1.11), - MASS, - posterior, - rmarkdown (>= 0.8.1), - ramcmc, - sde, - sitmo, - testthat -Imports: checkmate, coda (>= 0.18-1), diagis, Rcpp (>= 0.12.3) -LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo -SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -VignetteBuilder: knitr -BugReports: https://github.com/helske/bssm/issues -URL: https://github.com/helske/bssm -ByteCompile: true -Encoding: UTF-8 -NeedsCompilation: yes -RoxygenNote: 7.1.2 -Roxygen: list(markdown = TRUE, - roclets = c("namespace", "rd", "srr::srr_stats_roclet")) +Package: bssm +Type: Package +Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space + Models +Version: 1.1.7 +Authors@R: + c(person(given = "Jouni", + family = "Helske", + role = c("aut", "cre"), + email = "jouni.helske@iki.fi", + comment = c(ORCID = "0000-0001-7130-793X")), + person(given = "Matti", + family = "Vihola", + role = "aut", + comment = c(ORCID = "0000-0002-8041-7222"))) +Description: Efficient methods for Bayesian inference of state space models + via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel + importance sampling type weighted estimators + (Vihola, Helske, and Franks, 2020, ). + Gaussian, Poisson, binomial, negative binomial, and Gamma + observation densities and basic stochastic volatility models + with linear-Gaussian state dynamics, + as well as general non-linear Gaussian models and discretised + diffusion models are supported. +License: GPL (>= 2) +Depends: R (>= 3.5.0) +Suggests: + covr, + ggplot2 (>= 2.0.0), + Hmisc, + KFAS (>= 1.2.1), + knitr (>= 1.11), + MASS, + rmarkdown (>= 0.8.1), + ramcmc, + sde, + sitmo, + testthat +Imports: + magrittr, + checkmate, + coda (>= 0.18-1), + diagis, + dplyr, + posterior, + Rcpp (>= 0.12.3), + tidyr +LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo +SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) +VignetteBuilder: knitr +BugReports: https://github.com/helske/bssm/issues +URL: https://github.com/helske/bssm +ByteCompile: true +Encoding: UTF-8 +NeedsCompilation: yes +RoxygenNote: 7.1.2 +Roxygen: list(markdown = TRUE, + roclets = c("namespace", "rd", "srr::srr_stats_roclet")) diff --git a/NAMESPACE b/NAMESPACE index bd70f0a2..3ca973eb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -38,11 +38,10 @@ S3method(summary,mcmc_output) export(ar1_lg) export(ar1_ng) export(as_bssm) -export(as_draws.mcmc_output) -export(as_draws_df.mcmc_output) export(bootstrap_filter) export(bsm_lg) export(bsm_ng) +export(check_diagnostics) export(cpp_example_model) export(ekf) export(ekf_fast_smoother) @@ -84,14 +83,22 @@ importFrom(checkmate,test_flag) importFrom(checkmate,test_int) importFrom(checkmate,test_integerish) importFrom(coda,mcmc) -importFrom(coda,spectrum0.ar) importFrom(diagis,ess) importFrom(diagis,weighted_mean) importFrom(diagis,weighted_quantile) importFrom(diagis,weighted_se) importFrom(diagis,weighted_var) +importFrom(dplyr,across) +importFrom(dplyr,as_tibble) +importFrom(dplyr,group_by) +importFrom(dplyr,summarise) +importFrom(dplyr,ungroup) +importFrom(magrittr,"%>%") +importFrom(posterior,as_draws) +importFrom(posterior,as_draws_df) +importFrom(posterior,default_convergence_measures) +importFrom(posterior,summarise_draws) importFrom(stats,"tsp<-") -importFrom(stats,aggregate) importFrom(stats,as.ts) importFrom(stats,cov) importFrom(stats,dnorm) @@ -110,4 +117,5 @@ importFrom(stats,ts) importFrom(stats,ts.union) importFrom(stats,tsp) importFrom(stats,var) +importFrom(tidyr,pivot_wider) useDynLib(bssm) diff --git a/R/as_draws.R b/R/as_draws.R index fd76fc3d..b306c6b3 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -11,7 +11,10 @@ #' (without counts), which is ignored by \code{posterior} and \code{bayesplot}, #' i.e. those results correspond to approximate MCMC. #' -#' @param x An object of class \code{mcmc_output} +#' @param x An object of class \code{mcmc_output}. +#' @param times Vector of indices defining which time points to return? +#' Default is all. +#' @param ... Ignored. #' @return A \code{draws_df} object. #' @rdname as_draws #' @importFrom posterior as_draws as_draws_df @@ -45,29 +48,30 @@ #' posterior::ess_bulk(draws$sd_y) #' posterior::summarise_draws(draws) #' -as_draws_df.mcmc_output <- function(x) { +as_draws_df.mcmc_output <- function(x, times, ...) { d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) + if (missing(times)) times <- seq_len(ncol(x$alpha)) d_states <- as.data.frame(x, variable = "states", expand = TRUE, + times = times, use_times = FALSE) - d <- merge( + d <- cbind( tidyr::pivot_wider(d_theta, values_from = value, names_from = variable), tidyr::pivot_wider(d_states, values_from = value, names_from = c(variable, time), - names_glue = "{variable}[{time}]")) + names_glue = "{variable}[{time}]")[, -(1:2)]) names(d)[1] <- ".iteration" if (x$mcmc_type %in% paste0("is", 1:3)) { - warning(paste("Input is based on a IS-MCMC, the output column '.weight'", - "contains the IS-weights, but these are not used for example in the", + warning(paste("Input is based on a IS-MCMC and the output column 'weight'", + "contains the IS-weights. These are not used for example in the", "diagnostic methods by 'posterior' package, i.e. these are based", "on approximate MCMC chains.")) - names(d)[2] <- ".weight" } else { d$weight <- NULL } @@ -75,4 +79,4 @@ as_draws_df.mcmc_output <- function(x) { as_draws(d) } #' @exportS3Method posterior::as_draws mcmc_output -as_draws.mcmc_output <- function(x) as_draws_df.mcmc_output(x) \ No newline at end of file +as_draws.mcmc_output <- function(x, times, ...) as_draws_df.mcmc_output(x, times, ...) \ No newline at end of file diff --git a/R/bssm-package.R b/R/bssm-package.R index 05eae06e..83b3ffca 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -78,6 +78,7 @@ NULL #' @keywords datasets #' @references James Durbin, Siem Jan Koopman (2012). #' Time Series Analysis by State Space Methods. Oxford University Press. +#' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R new file mode 100644 index 00000000..69cdbc96 --- /dev/null +++ b/R/check_diagnostics.R @@ -0,0 +1,85 @@ +#' Quick Diagnostics Checks for \code{run_mcmc} Output +#' +#' Prints out the acceptance rate, smallest effective sample sizes (ESS) and +#' largest Rhat values for a quick first check that the sampling worked. +#' +#' For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior +#' package are based on the approximate posterior which should look reasonable, +#' otherwise the IS-correction does not make much sense. +#' +#' +#' @importFrom dplyr across +#' @importFrom posterior summarise_draws default_convergence_measures +#' @param x Results object of class \code{mcmc_output} from +#' \code{\link{run_mcmc}}. +#' @export +#' +#' @examples +#' set.seed(1) +#' n <- 30 +#' phi <- 2 +#' rho <- 0.9 +#' sigma <- 0.1 +#' beta <- 0.5 +#' u <- rexp(n, 0.1) +#' x <- rnorm(n) +#' z <- y <- numeric(n) +#' z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) +#' y[1] <- rnbinom(1, mu = u * exp(beta * x[1] + z[1]), size = phi) +#' for(i in 2:n) { +#' z[i] <- rnorm(1, rho * z[i - 1], sigma) +#' y[i] <- rnbinom(1, mu = u * exp(beta * x[i] + z[i]), size = phi) +#' } +#' +#' model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1), +#' sigma = gamma_prior(0.1, 2, 10), mu = 0., +#' phi = gamma_prior(2, 2, 1), distribution = "negative binomial", +#' xreg = x, beta = normal_prior(0.5, 0, 1), u = u) +#' +#' out <- run_mcmc(model, iter = 1e4, particles = 10) +#' +check_diagnostics <- function(x) { + + cat("\nAcceptance rate after the burn-in period: ", + paste(round(x$acceptance_rate, 3), "\n", sep = "")) + + cat("\nRun time (wall-clock):\n") + cat(paste(ifelse(x$time[3] < 10, round(x$time[3], 2), round(x$time[3])), + "seconds.\n")) + + + draws <- suppressWarnings(as_draws(x)) + + is_run <- x$mcmc_type %in% paste0("is", 1:3) + if (is_run) { + # removing hidden variables of draws object gives warning, we don't care + ess <- apply(suppressWarnings(draws[, 2:(ncol(draws) - 3)]), + 2, function(x) { + weighted_var(x, draws$weight) / asymptotic_var(x, draws$weight) + }) + min_ess <- which.min(ess) + cat("\nSmallest ESS based on weighted posterior: ", + round(ess[min_ess]), " (", names(ess)[min_ess], ")", sep = "") + + ess_is <- apply(suppressWarnings(draws[, 2:(ncol(draws) - 3)]), 2, + function(x) ess(draws$weight, identity, x)) + min_ess <- which.min(ess_is) + cat("\nSmallest ESS based on independent importance sampling: ", + round(ess[min_ess]), " (", names(ess_is)[min_ess], ")", sep = "") + + cat("\n\nNote: The input is based on a IS-weighted MCMC, so the ", + "approximate (non-weighted) posterior is used when computing the Rhat ", + "and ESS measures below.\n", sep="") + } + + sumr <- summarise_draws(draws, default_convergence_measures()) + min_ess <- which.min(sumr$ess_bulk) + cat("\nSmallest bulk-ESS: ", round(sumr$ess_bulk[min_ess]), " (", + sumr$variable[min_ess], ")", sep = "") + min_ess <- which.min(sumr$ess_tail) + cat("\nSmallest tail-ESS: ", round(sumr$ess_tail[min_ess]), " (", + sumr$variable[min_ess], ")", sep = "") + max_rhat <- which.max(sumr$rhat) + cat("\nLargest Rhat: ", round(sumr$rhat[max_rhat], 3), " (", + sumr$variable[max_rhat], ")", sep = "") +} diff --git a/R/expand_sample.R b/R/expand_sample.R new file mode 100644 index 00000000..53dbd549 --- /dev/null +++ b/R/expand_sample.R @@ -0,0 +1,88 @@ + +#' Expand the Jump Chain representation +#' +#' The MCMC algorithms of \code{bssm} use a jump chain representation where we +#' store the accepted values and the number of times we stayed in the current +#' value. Although this saves bit memory and is especially convenient for +#' IS-corrected MCMC, sometimes we want to have the usual sample paths +#' (for example for drawing traceplots). +#' Function \code{expand_sample} returns the expanded sample based on the +#' counts. Note that for IS-corrected output the expanded +#' sample corresponds to the approximate posterior i.e., +#' the weights are ignored. +#' +#' @seealso \code{as_draws}. +#' +#' @param x Output from \code{\link{run_mcmc}}. +#' @param variable Expand parameters \code{"theta"} or states \code{"states"}. +#' @param times Vector of indices. In case of states, +#' what time points to expand? Default is all. +#' @param states Vector of indices. In case of states, +#' what states to expand? Default is all. +#' @param by_states If \code{TRUE} (default), return list by states. +#' Otherwise by time. +#' @return An object of class \code{"mcmc"} of the \code{coda} package. +#' @export +#' @examples +#' +#' set.seed(1) +#' n <- 50 +#' x <- cumsum(rnorm(n)) +#' y <- rnorm(n, x) +#' model <- bsm_lg(y, sd_y = gamma_prior(1, 2, 2), +#' sd_level = gamma_prior(1, 2, 2)) +#' fit <- run_mcmc(model, iter = 1e4) +#' # Traceplots for theta +#' plot.ts(expand_sample(fit, variable = "theta")) +#' # Traceplot for x_5 +#' plot.ts(expand_sample(fit, variable = "states", times = 5, states = 1)) +expand_sample <- function(x, variable = "theta", times, states, + by_states = TRUE) { + + if (!test_flag(by_states)) + stop("Argument 'by_states' should be TRUE or FALSE. ") + + variable <- match.arg(tolower(variable), c("theta", "states")) + if (x$mcmc_type %in% paste0("is", 1:3)) + warning(paste("Input is based on a IS-weighted MCMC, the results", + "correspond to the approximate posteriors.", sep = " ")) + + if (variable == "theta") { + out <- apply(x$theta, 2, rep, times = x$counts) + } else { + if (x$output_type == 1) { + if (missing(times)) { + times <- seq_len(nrow(x$alpha)) + } else { + if (!check_integer(times) || any(times < 1) || any(times > nrow(x$alpha))) + stop(paste0("Argument 'times' should contain indices between 1 and ", + nrow(x$alpha),".")) + } + if (missing(states)) { + states <- seq_len(ncol(x$alpha)) + } else { + if (!check_integer(states) || any(states < 1) || + any(states > ncol(x$alpha))) + stop(paste0("Argument 'states' should contain indices between 1 and ", + ncol(x$alpha),".")) + } + + if (by_states) { + out <- lapply(states, function(i) { + z <- apply(x$alpha[times, i, , drop = FALSE], 1, rep, x$counts) + colnames(z) <- times + z + }) + names(out) <- colnames(x$alpha)[states] + } else { + out <- lapply(times, function(i) { + z <- apply(x$alpha[i, states, , drop = FALSE], 2, rep, x$counts) + colnames(z) <- colnames(x$alpha)[states] + z + }) + names(out) <- times + } + } else stop("MCMC output does not contain posterior samples of states.") + } + mcmc(out, start = x$burnin + 1, thin = x$thin) +} diff --git a/R/fitted.R b/R/fitted.R index 17d51fe5..698f4de3 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -4,7 +4,9 @@ #' distribution of the mean. #' #' @export -#' @importFrom stats fitted aggregate +#' @importFrom stats fitted +#' @importFrom magrittr %>% +#' @importFrom dplyr group_by ungroup summarise as_tibble #' @importFrom diagis weighted_quantile weighted_var weighted_mean weighted_se #' @name fitted.mcmc_output #' @param object Results object of class \code{mcmc_output} from @@ -22,7 +24,7 @@ fitted.mcmc_output <- function(object, model, probs = c(0.025, 0.975), ...) { - if (!inherits(model, "bbsm_model")) { + if (!inherits(model, "bssm_model")) { stop("Argument 'model' should be an object of class 'bssm_model'.") } if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { @@ -34,7 +36,7 @@ fitted.mcmc_output <- function(object, model, stop("Number of observations of the model and MCMC output do not match.") } } - + if (any(probs < 0 | probs > 1)) stop("'probs' outside [0, 1].") n <- nrow(object$alpha) - 1L @@ -103,16 +105,14 @@ fitted.mcmc_output <- function(object, model, d <- data.frame(value = as.numeric(pred), - variable = variables, - time = rep(time(model$y), each = nrow(pred))) - out <- do.call(data.frame, - aggregate(value ~ variable + time, data = d, - FUN = function(x) c( - weighted_mean(x, w), - sqrt(weighted_var(x, w)), - weighted_quantile(x, w, probs = probs), - sqrt(asymptotic_var(x, w))))) - names(out)[3:ncol(out)] <- c("Mean", "SD", paste0(probs * 100, "%"), "SE") - out + Variable = variables, + Time = rep(time(model$y), each = nrow(pred))) + + d %>% group_by(Variable, Time) %>% + summarise( + Mean = weighted_mean(value, w), + SD = sqrt(weighted_var(value, w)), + as_tibble(as.list(weighted_quantile(value, w, probs = probs))), + "SE(Mean)" = as.numeric(sqrt(asymptotic_var(value, w)))) %>% ungroup() } diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 24f75eaa..365e8501 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -36,14 +36,17 @@ #' [1] Gordon, NJ, Salmond, DJ, Smith, AFM (1993). #' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. #' IEE Proceedings-F, 140, 107-113. +#' https://doi.org/10.1049/ip-f-2.1993.0015 #' #' [2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +#' Scand J Statist. 2020; 1-38. +#' https://doi.org/10.1111/sjos.12492 #' #' [3] Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). #' The unscented particle filter. #' In Advances in neural information processing systems, p 584-590. +#' https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf #' #' [4] Jazwinski, A 1970. Stochastic Processes and Filtering Theory. #' Academic Press. @@ -51,6 +54,8 @@ #' [5] Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian #' nonlinear state space models. #' Journal of Computational and Graphical Statistics, 5, 1-25. +#' https://doi.org/10.2307/1390750 +#' #' @export #' @rdname particle_smoother particle_smoother <- function(model, particles, ...) { diff --git a/R/predict.R b/R/predict.R index 96ce1800..255acc28 100644 --- a/R/predict.R +++ b/R/predict.R @@ -7,7 +7,7 @@ #' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. #' #' @param object Results object of class \code{mcmc_output} from -#' \code{\link{run_mcmc}} +#' \code{\link{run_mcmc}}. #' @param model A \code{bssm_model} object. #' Should have same structure and class as the original model which was used in #' \code{run_mcmc}, in order to plug the posterior samples of the model diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 23e67a7f..7274ec3d 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -34,7 +34,7 @@ asymptotic_var <- function(x, w) { #' #' @method print mcmc_output #' @importFrom diagis weighted_mean weighted_var weighted_se ess -#' @importFrom coda mcmc spectrum0.ar +#' @importFrom coda mcmc #' @importFrom stats var #' @param x Output from \code{\link{run_mcmc}}. #' @param ... Ignored. @@ -75,7 +75,8 @@ print.mcmc_output <- function(x, ...) { } else { mean_theta <- colMeans(theta) sd_theta <- apply(theta, 2, sd) - se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) + se_theta <- sqrt(apply(theta, 2, function(x) iact(x) * var(x) / length(x))) + #se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) stats <- matrix(c(mean_theta, sd_theta, se_theta), ncol = 3, dimnames = list(colnames(x$theta), c("Mean", "SD", "SE"))) } @@ -103,7 +104,8 @@ print.mcmc_output <- function(x, ...) { } else { mean_alpha <- colMeans(alpha) sd_alpha <- apply(alpha, 2, sd) - se_alpha <- sqrt(spectrum0.ar(alpha)$spec / nrow(alpha)) + se_alpha <- sqrt(apply(alpha, 2, function(x) iact(x) * var(x) / length(x))) + #sqrt(spectrum0.ar(alpha)$spec / nrow(alpha)) stats <- matrix(c(mean_alpha, sd_alpha, se_alpha), ncol = 3, dimnames = list(colnames(x$alpha), c("Mean", "SD", "SE"))) } @@ -139,6 +141,7 @@ print.mcmc_output <- function(x, ...) { #' whereas SE corresponds to the square root of total asymptotic variance #' (see Remark 3 of Vihola et al. (2020)). #' +#' #' @param object Output from \code{run_mcmc} #' @param return_se if \code{FALSE} (default), computation of standard #' errors and effective sample sizes is omitted. @@ -194,7 +197,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sd_theta <- apply(theta, 2, sd) if (return_se) { - se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) + #sqrt(spectrum0.ar(theta)$spec / nrow(theta)) + se_theta <- sqrt(apply(theta, 2, function(x) iact(x) * var(x) / length(x))) ess_theta <- (sd_theta / se_theta)^2 summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), ncol = 4, @@ -251,10 +255,15 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", numeric(nrow(object$alpha))) if (return_se) { + # se_alpha <- vapply(alpha, function(x) + # apply(x, 2, function(z) + # sqrt(spectrum0.ar(z)$spec / length(z))), + # numeric(nrow(object$alpha))) se_alpha <- vapply(alpha, function(x) apply(x, 2, function(z) - sqrt(spectrum0.ar(z)$spec / length(z))), + sqrt(apply(theta, 2, function(x) iact(z) * var(z) / length(z)))), numeric(nrow(object$alpha))) + ess_alpha <- (sd_alpha / se_alpha)^2 summary_alpha <- list( "Mean" = mean_alpha, "SD" = sd_alpha, @@ -270,59 +279,3 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", "states" = return(summary_alpha) ) } - -#' Expand the Jump Chain representation -#' -#' The MCMC algorithms of \code{bssm} use a jump chain representation where we -#' store the accepted values and the number of times we stayed in the current -#' value. Although this saves bit memory and is especially convenient for -#' IS-corrected MCMC, sometimes we want to have the usual sample paths. -#' Function \code{expand_sample} returns the expanded sample based on the -#' counts. Note that for IS-corrected output the expanded -#' sample corresponds to the approximate posterior. -#' -#' @param x Output from \code{\link{run_mcmc}}. -#' @param variable Expand parameters \code{"theta"} or states \code{"states"}. -#' @param times Vector of indices. In case of states, -#' what time points to expand? Default is all. -#' @param states Vector of indices. In case of states, -#' what states to expand? Default is all. -#' @param by_states If \code{TRUE} (default), return list by states. -#' Otherwise by time. -#' @export -expand_sample <- function(x, variable = "theta", times, states, - by_states = TRUE) { - - if (!test_flag(by_states)) - stop("Argument 'by_states' should be TRUE or FALSE. ") - - variable <- match.arg(tolower(variable), c("theta", "states")) - if (x$mcmc_type %in% paste0("is", 1:3)) - warning(paste("Input is based on a IS-weighted MCMC, the results", - "correspond to the approximate posteriors.", sep = " ")) - if (variable == "theta") { - out <- apply(x$theta, 2, rep, times = x$counts) - } else { - if (x$output_type == 1) { - if (missing(times)) times <- seq_len(nrow(x$alpha)) - if (missing(states)) states <- seq_len(ncol(x$alpha)) - - if (by_states) { - out <- lapply(states, function(i) { - z <- apply(x$alpha[times, i, , drop = FALSE], 1, rep, x$counts) - colnames(z) <- times - z - }) - names(out) <- colnames(x$alpha)[states] - } else { - out <- lapply(times, function(i) { - z <- apply(x$alpha[i, states, , drop = FALSE], 2, rep, x$counts) - colnames(z) <- colnames(x$alpha)[states] - z - }) - names(out) <- times - } - } else stop("MCMC output does not contain posterior samples of states.") - } - mcmc(out, start = x$burnin + 1, thin = x$thin) -} diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 5df12c13..aff50e6e 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -99,13 +99,15 @@ #' @references #' [1] Vihola M (2012). Robust adaptive Metropolis algorithm with #' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. +#' https://doi.org/10.1007/s11222-011-9269-5 #' #' [2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' -#' [3] Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and +#' [3] Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and #' Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. +#' https://arxiv.org/abs/2101.08492 #' run_mcmc <- function(model, ...) { UseMethod("run_mcmc", model) @@ -371,7 +373,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", check_prop(target_acceptance) check_prop(gamma, "gamma") - output_type <- pmatch(tolwer(output_type), c("full", "summary", "theta")) + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3), "approx")) if (mcmc_type == "approx") particles <- 0 diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 17d150bb..dfc0076f 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -94,7 +94,7 @@ #' @srrstatsTODO {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* -#' # Standards not applicable +#' # General standards not applicable #' #' ## Factor types are not used nor supported: #' @srrstatsNA {G2.4d} *explicit conversion to factor via `as.factor()`* @@ -117,14 +117,17 @@ # Standards for Bayesian software -#' ## addressed by the paper vignette and the corresponding R Journal paper +#' ## addressed by the vignette, the corresponding R Journal paper, models.R and run_mcmc.R #' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* #' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* +#' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* +#' @srrstats {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* +#' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* -#' @srrstatsTODO {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* -#' @srrstatsTODO {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* -#' @srrstatsTODO {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* -#' @srrstatsTODO {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* +#' # Bayesian standards not applicable +#' +#' # the bssm package does not implement convergence checkers, but relie +#' @srrstats {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* #' @srrstatsTODO {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* #' @srrstatsTODO {BS2.1a} *The effects of such routines should be tested.* #' @srrstatsTODO {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* diff --git a/inst/CITATION b/inst/CITATION index 98a6b0ac..ebefa4d3 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,13 +1,12 @@ c( bibentry( - bibtype = "misc", + bibtype = "Article", key = "helske-vihola2021", title= "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", author="Jouni Helske and Matti Vihola", year="2021", - eprint="2101.08492", - archivePrefix="arXiv", - primaryClass="stat.CO", + journal = "R Journal", + note = "To appear" url = "https://arxiv.org/abs/2101.08492" ), bibentry( diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index 5683b7f9..daf16f9c 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -12,6 +12,7 @@ times, states, expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), + use_times = TRUE, ... ) } @@ -36,6 +37,10 @@ Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. For \code{expand = FALSE} and always for IS-MCMC, the resulting data.frame contains variable weight (= counts * IS-weights).} +\item{use_times}{If \code{TRUE} (default), transforms the values of the time +variable to match the ts attribute of the input to define. If \code{FALSE}, +time is based on the indexing starting from 1.} + \item{...}{Ignored.} } \description{ diff --git a/man/as_draws.Rd b/man/as_draws.Rd index 00a5eb2e..6184e745 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -2,15 +2,17 @@ % Please edit documentation in R/as_draws.R \name{as_draws_df.mcmc_output} \alias{as_draws_df.mcmc_output} -\alias{as_draws.mcmc_output} \title{Convert \code{run_mcmc} output to \code{draws_df} format} \usage{ -as_draws_df.mcmc_output(x) - -as_draws.mcmc_output(x) +\method{as_draws_df}{mcmc_output}(x, times, ...) } \arguments{ -\item{x}{An object of class \code{mcmc_output}} +\item{x}{An object of class \code{mcmc_output}.} + +\item{times}{Vector of indices defining which time points to return? +Default is all.} + +\item{...}{Ignored.} } \value{ A \code{draws_df} object. @@ -19,10 +21,14 @@ A \code{draws_df} object. Converts MCMC output from \code{run_mcmc} call to a \code{draws_df} format of the \code{posterior} package. This enables the use of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} -packages. Note though that if \code{run_mcmc} used IS-MCMC -method, the resulting \code{weight} column of the output is -ignored by the aforementioned packages, i.e. the results correspond to -approximate MCMC. +packages. +} +\note{ +The jump chain representation is automatically expanded by +\code{as_draws}, but if \code{run_mcmc} used IS-MCMC method, the output +contains additional \code{weight} column corresponding to the IS-weights +(without counts), which is ignored by \code{posterior} and \code{bayesplot}, +i.e. those results correspond to approximate MCMC. } \examples{ @@ -48,8 +54,8 @@ draws <- bind_draws(as_draws(fit1), as_draws(fit2), as_draws(fit3), along = "chain") # it is actually enough to transform first mcmc_output to draws object, # rest are transformed automatically inside bind_draws -rhat(draws$sd_y) -ess_bulk(draws$sd_y) -ess_tail(draws$sd_y) +posterior::rhat(draws$sd_y) +posterior::ess_bulk(draws$sd_y) +posterior::summarise_draws(draws) } diff --git a/man/check_diagnostics.Rd b/man/check_diagnostics.Rd new file mode 100644 index 00000000..d36d910a --- /dev/null +++ b/man/check_diagnostics.Rd @@ -0,0 +1,46 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_diagnostics.R +\name{check_diagnostics} +\alias{check_diagnostics} +\title{Quick Diagnostics Checks for \code{run_mcmc} Output} +\usage{ +check_diagnostics(x) +} +\arguments{ +\item{x}{Results object of class \code{mcmc_output} from +\code{\link{run_mcmc}}.} +} +\description{ +Prints out the acceptance rate, smallest effective sample sizes (ESS) and +largest Rhat values for a quick first check that the sampling worked. +} +\details{ +For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior +package are based on the approximate posterior which should look reasonable, +otherwise the IS-correction does not make much sense. +} +\examples{ +set.seed(1) +n <- 30 +phi <- 2 +rho <- 0.9 +sigma <- 0.1 +beta <- 0.5 +u <- rexp(n, 0.1) +x <- rnorm(n) +z <- y <- numeric(n) +z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2)) +y[1] <- rnbinom(1, mu = u * exp(beta * x[1] + z[1]), size = phi) +for(i in 2:n) { + z[i] <- rnorm(1, rho * z[i - 1], sigma) + y[i] <- rnbinom(1, mu = u * exp(beta * x[i] + z[i]), size = phi) +} + +model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1), + sigma = gamma_prior(0.1, 2, 10), mu = 0., + phi = gamma_prior(2, 2, 1), distribution = "negative binomial", + xreg = x, beta = normal_prior(0.5, 0, 1), u = u) + +out <- run_mcmc(model, iter = 1e4, particles = 10) + +} diff --git a/man/exchange.Rd b/man/exchange.Rd index 1104774d..10c2c3b0 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -24,5 +24,6 @@ plot.ts(cbind(model$y, exp(out$alphahat))) \references{ James Durbin, Siem Jan Koopman (2012). Time Series Analysis by State Space Methods. Oxford University Press. +https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 } \keyword{datasets} diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index c1238c88..bd455ee6 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R +% Please edit documentation in R/expand_sample.R \name{expand_sample} \alias{expand_sample} \title{Expand the Jump Chain representation} @@ -20,12 +20,34 @@ what states to expand? Default is all.} \item{by_states}{If \code{TRUE} (default), return list by states. Otherwise by time.} } +\value{ +An object of class \code{"mcmc"} of the \code{coda} package. +} \description{ The MCMC algorithms of \code{bssm} use a jump chain representation where we store the accepted values and the number of times we stayed in the current value. Although this saves bit memory and is especially convenient for -IS-corrected MCMC, sometimes we want to have the usual sample paths. +IS-corrected MCMC, sometimes we want to have the usual sample paths +(for example for drawing traceplots). Function \code{expand_sample} returns the expanded sample based on the counts. Note that for IS-corrected output the expanded -sample corresponds to the approximate posterior. +sample corresponds to the approximate posterior i.e., +the weights are ignored. +} +\examples{ + +set.seed(1) +n <- 50 +x <- cumsum(rnorm(n)) +y <- rnorm(n, x) +model <- bsm_lg(y, sd_y = gamma_prior(1, 2, 2), + sd_level = gamma_prior(1, 2, 2)) +fit <- run_mcmc(model, iter = 1e4) +# Traceplots for theta +plot.ts(expand_sample(fit, variable = "theta")) +# Traceplot for x_5 +plot.ts(expand_sample(fit, variable = "states", times = 5, states = 1)) +} +\seealso{ +\code{as_draws}. } diff --git a/man/fitted.mcmc_output.Rd b/man/fitted.mcmc_output.Rd index 4e9d6574..6302dcbb 100644 --- a/man/fitted.mcmc_output.Rd +++ b/man/fitted.mcmc_output.Rd @@ -24,5 +24,6 @@ model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior, period = 4) fit <- run_mcmc(model, iter = 2e4) res <- fitted(fit, model) +head(res) } diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index d9f7dc8c..15ab5919 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -111,14 +111,17 @@ ts.plot(out$alphahat, rowMeans(out2), col = 1:2) \link{1} Gordon, NJ, Salmond, DJ, Smith, AFM (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107-113. +https://doi.org/10.1049/ip-f-2.1993.0015 \link{2} Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. -Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +Scand J Statist. 2020; 1-38. +https://doi.org/10.1111/sjos.12492 \link{3} Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). The unscented particle filter. In Advances in neural information processing systems, p 584-590. +https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf \link{4} Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press. @@ -126,4 +129,5 @@ Academic Press. \link{5} Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25. +https://doi.org/10.2307/1390750 } diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 6ec5add3..54bbb6f5 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -17,7 +17,7 @@ } \arguments{ \item{object}{Results object of class \code{mcmc_output} from -\code{\link{run_mcmc}}} +\code{\link{run_mcmc}}.} \item{model}{A \code{bssm_model} object. Should have same structure and class as the original model which was used in diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index b88acd63..ba17ba38 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -334,13 +334,15 @@ theme_bw() \references{ \link{1} Vihola M (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. +https://doi.org/10.1007/s11222-011-9269-5 \link{2} Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -\link{3} Helske, J, Vihola, M (2019). bssm: Bayesian Inference of Non-linear and +\link{3} Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. +https://arxiv.org/abs/2101.08492 Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index 84af16c2..e7822cd1 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -40,7 +40,8 @@ or a m x k x n array.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} -\item{init_theta}{Initial values for the unknown hyperparameters theta.} +\item{init_theta}{Initial values for the unknown hyperparameters theta +(i.e. unknown variables excluding latent state variables).} \item{D}{Intercept terms for observation equation, given as a p x n matrix.} diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index b574543d..a125425d 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -55,7 +55,8 @@ standard deviation, and for other distributions this is ignored.} distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{init_theta}{Initial values for the unknown hyperparameters theta.} +\item{init_theta}{Initial values for the unknown hyperparameters theta +(i.e. unknown variables excluding latent state variables).} \item{D}{Intercept terms for observation equation, given as p x n matrix.} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index f5971b60..985275ec 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -40,7 +40,8 @@ a m x k matrix or a m x k x n array, or object which can be coerced to such.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} -\item{init_theta}{Initial values for the unknown hyperparameters theta.} +\item{init_theta}{Initial values for the unknown hyperparameters theta +(i.e. unknown variables excluding latent state variables).} \item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a scalar or vector of length n.} diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 72b25b7b..415a01a8 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -53,7 +53,8 @@ a positive scalar.} gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{init_theta}{Initial values for the unknown hyperparameters theta.} +\item{init_theta}{Initial values for the unknown hyperparameters theta +(i.e. unknown variables excluding latent state variables).} \item{D}{Intercept terms \eqn{D_t} for the observations equation, given as a scalar or vector of length n.} diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 83438330..02ce09c6 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -16,7 +16,7 @@ errors and effective sample sizes is omitted.} \code{"theta"} (default), \code{"states"}, or \code{"both"}?} \item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only -for hyperparameters theta.} +for hyperparameters theta, not latent states alpha.} \item{...}{Ignored.} } From d2bc3b17e55a8e726ac35eef2e8f73b6bdd7b200 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 16 Nov 2021 22:26:59 +0200 Subject: [PATCH 106/180] object to model --- R/check_arguments.R | 9 +++++++++ R/ekpf_filter.R | 34 ++++++++++++++++++---------------- 2 files changed, 27 insertions(+), 16 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 86f22bd7..7dba4f6c 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -381,3 +381,12 @@ check_positive_real <- function(x, name) { } x } + +check_missingness <- function(x) { + contains_na <- anyNA(model[-which(names(x) %in% c("y", "prior_parameters"))], + recursive = TRUE) + if (contains_na) stop(paste( + "Missing values not allowed in the model object", + "(except in components 'y' and 'prior_parameters').")) + +} \ No newline at end of file diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 62fc7315..8f1aa18e 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -3,7 +3,7 @@ #' Function \code{ekpf_filter} performs a extended Kalman particle filtering #' with stratification resampling, based on Van Der Merwe et al (2001). #' -#' @param object Model of class \code{ssm_nlg}. +#' @param model Model of class \code{ssm_nlg}. #' @param particles Number of particles as a positive integer. #' @param seed Seed for RNG (positive integer). #' @param ... Ignored. @@ -14,8 +14,8 @@ #' information processing systems (pp. 584-590). #' @export #' @rdname ekpf_filter -ekpf_filter <- function(object, particles, ...) { - UseMethod("ekpf_filter", object) +ekpf_filter <- function(model, particles, ...) { + UseMethod("ekpf_filter", model) } #' @method ekpf_filter ssm_nlg #' @export @@ -43,9 +43,11 @@ ekpf_filter <- function(object, particles, ...) { #' out <- ekpf_filter(model_nlg, particles = 100) #' ts.plot(cbind(x, out$at[1:n], out$att[1:n]), col = 1:3) #'} -ekpf_filter.ssm_nlg <- function(object, particles, +ekpf_filter.ssm_nlg <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -56,8 +58,8 @@ ekpf_filter.ssm_nlg <- function(object, particles, } particles <- check_integer(particles, "particles") - nsamples <- ifelse(!is.null(nrow(object$y)), nrow(object$y), - length(object$y)) * object$n_states * particles + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), + length(model$y)) * model$n_states * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) @@ -65,19 +67,19 @@ ekpf_filter.ssm_nlg <- function(object, particles, seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) - out <- ekpf(t(object$y), object$Z, object$H, object$T, - object$R, object$Z_gn, object$T_gn, object$a1, object$P1, - object$theta, object$log_prior_pdf, object$known_params, - object$known_tv_params, object$n_states, object$n_etas, - as.integer(object$time_varying), particles, + out <- ekpf(t(model$y), model$Z, model$H, model$T, + model$R, model$Z_gn, model$T_gn, model$a1, model$P1, + model$theta, model$log_prior_pdf, model$known_params, + model$known_tv_params, model$n_states, model$n_etas, + as.integer(model$time_varying), particles, seed) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- - rownames(out$alpha) <- object$state_names - out$at <- ts(out$at, start = start(object$y), - frequency = frequency(object$y)) - out$att <- ts(out$att, start = start(object$y), - frequency = frequency(object$y)) + rownames(out$alpha) <- model$state_names + out$at <- ts(out$at, start = start(model$y), + frequency = frequency(model$y)) + out$att <- ts(out$att, start = start(model$y), + frequency = frequency(model$y)) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out } From bc5e590650870f2aa09dcb4946a41f30cbd8eafd Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 16 Nov 2021 22:55:53 +0200 Subject: [PATCH 107/180] export asymptotic_var, ess alternatives, docs... --- R/asymptotic_var.R | 85 +++++++++++++++++++++++++++++++++++++++++ data/negbin_series.rda | Bin 0 -> 2342 bytes man/asymptotic_var.Rd | 54 ++++++++++++++++++++++++++ man/bssm.Rd | 6 +-- man/ekpf_filter.Rd | 6 +-- man/gaussian_approx.Rd | 4 +- man/get_map.Rd | 14 +++++++ man/iact.Rd | 29 ++++++++++++++ man/negbin_series.Rd | 33 ++++++++++++++++ man/poisson_series.Rd | 12 ++++-- man/run_mcmc.Rd | 6 +-- 11 files changed, 235 insertions(+), 14 deletions(-) create mode 100644 R/asymptotic_var.R create mode 100644 data/negbin_series.rda create mode 100644 man/asymptotic_var.Rd create mode 100644 man/get_map.Rd create mode 100644 man/iact.Rd create mode 100644 man/negbin_series.Rd diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R new file mode 100644 index 00000000..30eff8fd --- /dev/null +++ b/R/asymptotic_var.R @@ -0,0 +1,85 @@ +#' Integrated Autocorrelation Time +#' +#' Estimates the integrated autocorrelation time based on Sokal (1997). +#' +#' @param x A vector. +#' @references +#' Sokal A. (1997) Monte Carlo Methods in Statistical Mechanics: Foundations +#' and New Algorithms. +#' In: DeWitt-Morette C., Cartier P., Folacci A. (eds) Functional Integration. +#' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +#' https://doi.org/10.1007/978-1-4899-0319-8_6 +#' @export +#' @examples +#' set.seed(1) +#' x <- numeric(1e4) +#' phi <- 0.8 +#' for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) +#' # ESS: +#' length(x) / iact(x) +iact <- function(x) { + n <- length(x) + x_ <- (x - mean(x)) / sd(x) + C <- max(5.0, log10(n)) + tau <- 1 + for (k in 1:(n - 1)) { + tau <- tau + 2.0 * (x_[1:(n-k)] %*% x_[(1+k):n]) / (n - k) + if (k > C * tau) break + } + max(0.0, tau) +} +#' Asymptotic Variance of IS-type Estimators +#' +#' Estimates the asymptotic variance based on Corollary 1 +#' of Vihola et al. (2020) from weighted samples from IS-MCMC. The default +#' method is based on the integrated autocorrelation time (IACT) by Sokal (1997) +#' which seem to work well for reasonable problems, but it is also possible to +#' use the Geyer's method as implemented in \code{ess_basic} of the +#' \code{posterior} package, or the improved \code{ess_bulk} method of the same +#' package (these compute ESS instead of variances, but +#' MCSE^2 = var(x) / ESS = var(x) * IACT / length(x)). +#' +#' @param x Vector of samples. +#' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is +#' assumed). +#' @param method Method for computing the IACT. Default is \code{"sokal"} +#' @references +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +#' +#' Sokal A. (1997). Monte Carlo Methods in Statistical Mechanics: Foundations +#' and New Algorithms. +#' In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. +#' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +#' https://doi.org/10.1007/978-1-4899-0319-8_6 +#' +#' Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). +#' Rank-normalization, folding, and localization: An improved Rhat for +#' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. +#' https://doi.org/10.1214/20-BA1221 +#' @export +#' @examples +#' set.seed(1) +#' x <- numeric(1e4) +#' phi <- 0.7 +#' for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) +#' w <- rexp(1e4, 0.5 * exp(0.001 * x^2)) +#' # different methods: +#' asymptotic_var(x, w, method = "sokal") +#' asymptotic_var(x, w, method = "ess_basic") +#' asymptotic_var(x, w, method = "ess_bulk") +#' +asymptotic_var <- function(x, w, method = "sokal") { + method <- match.arg(method, c("sokal", "ess_basic", "ess_bulk")) + if (missing(w)) w <- rep(1, length(x)) + if(any(w < 0) | any(!is.finite(w))) + stop("Nonfinite or negative weights in 'w'.") + estimate_c <- mean(w) + estimate_mean <- weighted_mean(x, w) + z <- w * (x - estimate_mean) + switch(method, + sokal = var(z) * iact(z) / length(z) / estimate_c^2, + ess_basic = var(z) / ess_basic(z) / estimate_c^2, + ess_bulk = var(z) / ess_bulk(z) / estimate_c^2) +} diff --git a/data/negbin_series.rda b/data/negbin_series.rda new file mode 100644 index 0000000000000000000000000000000000000000..c999d646527edfe0e5ff3dc6435daa6308dc061c GIT binary patch literal 2342 zcmV+>3EB2ST4*^jL0KkKS%4LQEC2+bfB*mg|NsB@|NsC0|NsC0|NsC0|NsC0|NsC0 z|NsB@|Nqbhn~wl>>v}V{2yK;!#HXfH%6TJ0(t2c!o>NaK(={-eF_AW=k0^SWk5C#g zm`ya(X&QQJ9-}9y+9S{q15Z$49;QZ`W~P}urph*gdXGUKNXWpCMD&bIjW&^j6)BpY zr>T_FH8Ogd7?@2qqtx1%hL6=ArqW^tjTl2lK-wpyXnL9$Xql!*su-IjG~T9yU{6rf z(qc4ewG9K*dI&TC$%&I{84O06dW-_9W{Ete)Ovb}rkFB~w3Eoxc%x`X>Yk?5dY;mr znwhDi^&`~vFw{LD&^J6#tXwyc3^)v>WdXFUXLm+ydra%B_7>0n_hJ(~HGzNx% z(?L|xshKe*8kwM{ks25#Ai^482+crZ41mxL7z8mG004oejG6$zLqiZ6X`>S(Kxi~* z!fA*Mj1vGDq7nk0riYH*}KhpFm% zo}(H@ntBL&fb|Ebs$eFbiRBn=L8+#J42^ zY*ZW^v%r8zdA%D*X_aFNG)$wt@PtUEYv2flM07|32@fI!i_e-!Ae`eJdLafN*FX~B zgh1EILCE`f*R3rWVNn*BOd#M8N)B82d_#cDbPOOKLLd?ZAW`#8i*k85fsb@CAfw}$ z(hNd2ZO`Q@TaHkx`h3VlJ4M-HSzPNs$0X(LofgRV2n(%C58Qze>M;S!Mz{!Qw?jKE zrEk!(^^A~4l@5j;)i!Mtc~4OOwXVrRL5kx#Kic*QrTxEUjg<35McXIUbmJ}+o8}1b z0iLoBU|1$*D!ZpU8Oip@#C8oMVuaclBuEiK7NU9dGzgniC0ERdtE6$GV2SjcXJ&x{ zjS~{Sv>MvL!5t1@jYkr=5hH~Ko$~jt*7su%i=sZf82-}aR2~TM7iSEMHt~oQwmTi7m<;& z#+f-0Ku{oEQj!SLWbbz?pa}d=O&h&6$GuY@3^2zhn$g5Wh!BS;*ibX zSW3l6gaObXkVpUs$T1o{+{jV7_WIjqB2`KkWst|o*BG#-!8Y^ zlaaxQNo=w~W{jXO0RRSS)dFVYr>!#E6W$Y5KTF9Ajde?uygpS1$^HPa{d#JSsgfBE zcw&RP3ciSBa6hxJE#mRue(07Zt@dVm^NQ$&w_MPrPQ>95K>({H{!qFO@5AhBwB0R4 z4c!Mf>D<8hvoE+4wY|iB9S4e5LgfhDx;l8wL_^{ z*AGORBFf6FMzL%&INjt+f7y*#3B*zvrI${zi6bWX^GB72FMNMbGg?nWw zo@}Nt8q6u+x6<@zA77udB~VR>KaSLQ?jURj%5hs=mS1?Ggzhg**UFSK+sZS~M@a5- z7D^o8QCm@u3A0&PB~C_yy>*MPvbeM+-kG8|LbT&qeoJ^;dm0=4CLgl|;XfhZhmWjg zc&$Ix&|}`8%ht`nRIW0#$MRyBw{85=Vv{gzF8r?&wR6?ga!r+u*c!HP!Ah+vN3l-J zFWdvBMvQ+wqou1gfDGwYrmo3pJNzPLB3Q)}hVICejD}ppd%=5dUEMxW7A|Ah+v1Gf zlh4K!@&Wn<7o(4yHsT1F34tdL&?BdO+fKCTk>^a&9ad86D!S>NVhddVaP9b_$D4j zO~QlXg#T9(t~T@nJX2ZMnx81;8<@VuiJ6U^Bqm!vVBvrCZ#(j9T(!dI6?o^t8rUcT~!iqGD3d($kqO`c6aWr@hc*^=k7!N7#xfYn@E$$956~$hCKZ`y|minwVUcW%OA%BJ+r5w3e<$hj%Ii+P6hdDddIUywcX_VGx;&zqy94Z?)O62M499qZ^owO9-k(v4) zNXC7y9QcXmZOWzWR-%pK*?SE|rwKC)d2EcqYO`${=fYE~cXnee+?R(!O}Qnu&FJe# zlgdpO*+Fx5n;e;kX~eDC(zD*9=QCwk7O#$Qy^m^*vCz;r={Iv{S)j2rz5vCoxGbhW zs({xa>iu%j`w@~G@nis*lqfTdIgPGd2g?n?UnJ{5@vjXS-@*9N>SV-uoEkt_dQ(!! z7Jt#Xwv{%}Ue8dr!1YxZ&v6Sp{H8Uq#nIJEaqm`Ai2(!6y%AvELamLZv4-rrjX&{s MBvXY62mx3Ez{h_}82|tP literal 0 HcmV?d00001 diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd new file mode 100644 index 00000000..b23d9d25 --- /dev/null +++ b/man/asymptotic_var.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/asymptotic_var.R +\name{asymptotic_var} +\alias{asymptotic_var} +\title{Asymptotic Variance of IS-type Estimators} +\usage{ +asymptotic_var(x, w, method = "sokal") +} +\arguments{ +\item{x}{Vector of samples.} + +\item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is +assumed).} + +\item{method}{Method for computing the IACT. Default is \code{"sokal"}} +} +\description{ +Estimates the asymptotic variance based on Corollary 1 +of Vihola et al. (2020) from weighted samples from IS-MCMC. The default +method is based on the integrated autocorrelation time (IACT) by Sokal (1997) +which seem to work well for reasonable problems, but it is also possible to +use the Geyer's method as implemented in \code{ess_basic} of the +\code{posterior} package, or the improved \code{ess_bulk} method of the same +package (these compute ESS instead of variances, but +MCSE^2 = var(x) / ESS = var(x) * IACT / length(x)). +} +\examples{ +set.seed(1) +x <- numeric(1e4) +phi <- 0.7 +for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) +w <- rexp(1e4, 0.5 * exp(0.001 * x^2)) +# different methods: +asymptotic_var(x, w, method = "sokal") +asymptotic_var(x, w, method = "ess_basic") +asymptotic_var(x, w, method = "ess_bulk") + +} +\references{ +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 + +Sokal A. (1997). Monte Carlo Methods in Statistical Mechanics: Foundations +and New Algorithms. +In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. +NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +https://doi.org/10.1007/978-1-4899-0319-8_6 + +Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). +Rank-normalization, folding, and localization: An improved Rhat for +assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. +https://doi.org/10.1214/20-BA1221 +} diff --git a/man/bssm.Rd b/man/bssm.Rd index 1791a110..4eaba2ae 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -23,12 +23,12 @@ Carlo methods for models outside classic linear-Gaussian framework. For definitions of the currently supported models and methods, as well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and -the package vignettes. +the package vignettes and the R Journal paper. } \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. ArXiv 2101.08492, -. +Non-Gaussian State Space Models in R. R Journal (to appear). +https://arxiv.org/abs/2101.08492 Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 984b96f3..b121f14e 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -5,17 +5,17 @@ \alias{ekpf_filter.ssm_nlg} \title{Extended Kalman Particle Filtering} \usage{ -ekpf_filter(object, particles, ...) +ekpf_filter(model, particles, ...) \method{ekpf_filter}{ssm_nlg}( - object, + model, particles, seed = sample(.Machine$integer.max, size = 1), ... ) } \arguments{ -\item{object}{Model of class \code{ssm_nlg}.} +\item{model}{Model of class \code{ssm_nlg}.} \item{particles}{Number of particles as a positive integer.} diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 5189e3e7..88325b77 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -56,7 +56,7 @@ for(i in 1:7) Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space Methods. Second edition. Oxford: Oxford University Press. -Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/get_map.Rd b/man/get_map.Rd new file mode 100644 index 00000000..060444f7 --- /dev/null +++ b/man/get_map.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/post_correction.R +\name{get_map} +\alias{get_map} +\title{Get MAP estimate of theta} +\usage{ +get_map(x) +} +\arguments{ +\item{x}{Object of class \code{mcmc_output}} +} +\description{ +Get MAP estimate of theta +} diff --git a/man/iact.Rd b/man/iact.Rd new file mode 100644 index 00000000..f5e75bfb --- /dev/null +++ b/man/iact.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/asymptotic_var.R +\name{iact} +\alias{iact} +\title{Integrated Autocorrelation Time} +\usage{ +iact(x) +} +\arguments{ +\item{x}{A vector.} +} +\description{ +Estimates the integrated autocorrelation time based on Sokal (1997). +} +\examples{ +set.seed(1) +x <- numeric(1e4) +phi <- 0.8 +for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) +# ESS: +length(x) / iact(x) +} +\references{ +Sokal A. (1997) Monte Carlo Methods in Statistical Mechanics: Foundations +and New Algorithms. +In: DeWitt-Morette C., Cartier P., Folacci A. (eds) Functional Integration. +NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +https://doi.org/10.1007/978-1-4899-0319-8_6 +} diff --git a/man/negbin_series.Rd b/man/negbin_series.Rd new file mode 100644 index 00000000..854809ac --- /dev/null +++ b/man/negbin_series.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{negbin_series} +\alias{negbin_series} +\title{See example for code for reproducing the data. This was used in +Helske and Vihola (2021).} +\format{ +A time series \code{mts} object with 200 time points and two series. +} +\description{ +See example for code for reproducing the data. This was used in +Helske and Vihola (2021). +} +\examples{ +# The data was generated as follows: +set.seed(123) +n <- 200 +sd_level <- 0.1 +drift <- 0.01 +beta <- -0.9 +phi <- 5 + +level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) +x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) +y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) +} +\references{ +\link{3} Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. R Journal (to appear). +https://arxiv.org/abs/2101.08492 +} +\keyword{datasets} diff --git a/man/poisson_series.Rd b/man/poisson_series.Rd index 46f75a0a..99187048 100644 --- a/man/poisson_series.Rd +++ b/man/poisson_series.Rd @@ -3,12 +3,13 @@ \docType{data} \name{poisson_series} \alias{poisson_series} -\title{Simulated Poisson time series data} +\title{Simulated Poisson Time Series Data} \format{ -A vector of length 100 +A vector of length 100. } \description{ -See example for code for reproducing the data. +See example for code for reproducing the data. This was used in +Vihola, Helske, Franks (2020). } \examples{ # The data was generated as follows: @@ -16,4 +17,9 @@ set.seed(321) slope <- cumsum(c(0, rnorm(99, sd = 0.01))) y <- rpois(100, exp(cumsum(slope + c(0, rnorm(99, sd = 0.1))))) } +\references{ +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +estimators based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +} \keyword{datasets} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index ba17ba38..40988796 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -336,12 +336,12 @@ theme_bw() coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. https://doi.org/10.1007/s11222-011-9269-5 -\link{2} Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +\link{2} Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 \link{3} Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. +Non-Gaussian State Space Models in R. R Journal (to appear). https://arxiv.org/abs/2101.08492 Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based From 88ad29290f323152c7f5344892c2d1dd7c192f27 Mon Sep 17 00:00:00 2001 From: helske Date: Tue, 16 Nov 2021 23:44:36 +0200 Subject: [PATCH 108/180] example to readme --- NAMESPACE | 2 + R/approx.R | 8 +- R/as_bssm.R | 5 +- R/bootstrap_filter.R | 10 +- R/bssm-package.R | 53 ++- R/importance_sample.R | 4 +- R/kfilter.R | 6 + R/loglik.R | 9 + R/models.R | 8 + R/particle_smoother.R | 8 + R/post_correction.R | 7 +- R/predict.R | 2 + R/print_mcmc.R | 30 -- R/run_mcmc.R | 17 +- R/sim_smoother.R | 4 +- R/smoother.R | 7 + R/srr-stats-standards.R | 14 +- README.Rmd | 97 +++++- README.html | 347 -------------------- README.md | 532 +++++++++++++++++++++---------- man/figures/README-compare-1.png | Bin 0 -> 13077 bytes man/figures/README-example-1.png | Bin 0 -> 10931 bytes vignettes/bssm.Rmd | 3 +- vignettes/bssm.bib | 4 +- vignettes/psi_pf.Rmd | 4 + 25 files changed, 598 insertions(+), 583 deletions(-) delete mode 100644 README.html create mode 100644 man/figures/README-compare-1.png create mode 100644 man/figures/README-example-1.png diff --git a/NAMESPACE b/NAMESPACE index 3ca973eb..b130ec56 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -38,6 +38,7 @@ S3method(summary,mcmc_output) export(ar1_lg) export(ar1_ng) export(as_bssm) +export(asymptotic_var) export(bootstrap_filter) export(bsm_lg) export(bsm_ng) @@ -54,6 +55,7 @@ export(gamma_prior) export(gaussian_approx) export(halfnormal) export(halfnormal_prior) +export(iact) export(importance_sample) export(kfilter) export(normal) diff --git a/R/approx.R b/R/approx.R index e6cc5c6a..90799fbb 100644 --- a/R/approx.R +++ b/R/approx.R @@ -25,8 +25,8 @@ #' Koopman, SJ and Durbin J (2012). Time Series Analysis by State Space #' Methods. Second edition. Oxford: Oxford University Press. #' -#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @rdname gaussian_approx @@ -48,6 +48,8 @@ gaussian_approx <- function(model, max_iter, conv_tol, ...) { gaussian_approx.nongaussian <- function(model, max_iter = 100, conv_tol = 1e-8, ...) { + check_missingness(model) + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -82,6 +84,8 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, gaussian_approx.ssm_nlg <- function(model, max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { + check_missingness(model) + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") model$iekf_iter <- check_integer(iekf_iter, "iekf_iter") diff --git a/R/as_bssm.R b/R/as_bssm.R index 259830ac..e82fe1b7 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -2,7 +2,10 @@ #' #' Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} #' model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or -#' \code{ssm_mng}. +#' \code{ssm_mng}. As \code{KFAS} supports formula syntax for defining +#' e.g. regression and cyclic components it maybe sometimes easier to define +#' the model with \code{KFAS::SSModel} and then convert for the bssm style with +#' \code{as_bssm}. #' #' @param model Object of class \code{SSModel}. #' @param kappa For \code{SSModel} object, a prior variance for initial state diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index f676bc78..28e9dce4 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -35,6 +35,8 @@ bootstrap_filter <- function(model, particles, ...) { bootstrap_filter.gaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -78,6 +80,8 @@ bootstrap_filter.gaussian <- function(model, particles, bootstrap_filter.nongaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -114,7 +118,9 @@ bootstrap_filter.nongaussian <- function(model, particles, #' @export bootstrap_filter.ssm_nlg <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { - + + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -154,6 +160,8 @@ bootstrap_filter.ssm_nlg <- function(model, particles, bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (!test_count(L, positive=TRUE)) stop("Discretization level L must be a positive integer.") diff --git a/R/bssm-package.R b/R/bssm-package.R index 83b3ffca..7ef99d37 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -1,3 +1,4 @@ +#' #' Bayesian Inference of State Space Models #' #' This package contains functions for efficient Bayesian inference of state @@ -18,14 +19,12 @@ #' definitions of the currently supported models and methods, as #' well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF #' algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and -#' the package vignettes. -#' -#' @srrstatsTODO {G1.4,G1.4a} Package uses roxygen2 for documentation. +#' the package vignettes and the R Journal paper. #' #' @references #' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. ArXiv 2101.08492, -#' . +#' Non-Gaussian State Space Models in R. R Journal (to appear). +#' https://arxiv.org/abs/2101.08492 #' #' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. @@ -86,18 +85,54 @@ NULL #' #' out <- particle_smoother(model, particles = 500) #' plot.ts(cbind(model$y, exp(out$alphahat))) -NULL -#' Simulated Poisson time series data +NULL +#' Simulated Poisson Time Series Data #' -#' See example for code for reproducing the data. +#' See example for code for reproducing the data. This was used in +#' Vihola, Helske, Franks (2020). #' +#' @srrstats {G5.0, G5.1} used in Vihola, Helske, Franks (2020). #' @name poisson_series #' @docType data -#' @format A vector of length 100 +#' @format A vector of length 100. #' @keywords datasets +#' @references +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +#' estimators based on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +#' #' @examples #' # The data was generated as follows: #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) #' y <- rpois(100, exp(cumsum(slope + c(0, rnorm(99, sd = 0.1))))) NULL +#' +# Simulated Negative Binomial Time Series Data +#' +#' See example for code for reproducing the data. This was used in +#' Helske and Vihola (2021). +#' +#' @srrstats {G5.0, G5.1} used in Helske and Vihola (2021). +#' @name negbin_series +#' @docType data +#' @format A time series \code{mts} object with 200 time points and two series. +#' @keywords datasets +#' @references +#' [3] Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. R Journal (to appear). +#' https://arxiv.org/abs/2101.08492 +#' +#' @examples +#' # The data was generated as follows: +#' set.seed(123) +#' n <- 200 +#' sd_level <- 0.1 +#' drift <- 0.01 +#' beta <- -0.9 +#' phi <- 5 +#' +#' level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) +#' x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) +#' y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) +NULL \ No newline at end of file diff --git a/R/importance_sample.R b/R/importance_sample.R index 8f35e12b..71395969 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -42,7 +42,9 @@ importance_sample <- function(model, nsim, use_antithetic, importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1), ...) { - + + check_missingness(model) + model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") nsim <- check_integer(nsim, "nsim") diff --git a/R/kfilter.R b/R/kfilter.R index 42bd549e..58534a79 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -30,6 +30,8 @@ kfilter <- function(model, ...) { #' ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) kfilter.gaussian <- function(model, ...) { + check_missingness(model) + out <- gaussian_kfilter(model, model_type = model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- @@ -94,6 +96,8 @@ kfilter.nongaussian <- function(model, ...) { #' } ekf <- function(model, iekf_iter = 0) { + check_missingness(model) + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) out <- ekf_nlg(t(model$y), model$Z, model$H, model$T, @@ -161,6 +165,8 @@ ekf <- function(model, iekf_iter = 0) { #' } ukf <- function(model, alpha = 0.001, beta = 2, kappa = 0) { + check_missingness(model) + if (alpha <= 0) stop("Parameter 'alpha' should be positive. ") if (beta < 0) stop("Parameter 'beta' should be non-negative. ") if (kappa < 0) stop("Parameter 'kappa' should be non-negative. ") diff --git a/R/loglik.R b/R/loglik.R index bb03398a..83c6d6ea 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -48,6 +48,9 @@ #' model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) #' logLik(model) logLik.gaussian <- function(object, ...) { + + check_missingness(object) + gaussian_loglik(object, model_type(object)) } @@ -69,6 +72,8 @@ logLik.nongaussian <- function(object, particles, method = "psi", max_iter = 100, conv_tol = 1e-8, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(object) + object$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) object$conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -99,6 +104,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(object) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -132,6 +139,8 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", logLik.ssm_sde <- function(object, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(object) + if (L <= 0) stop("Discretization level L must be larger than 0.") if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) diff --git a/R/models.R b/R/models.R index a06fff5a..9a15af09 100644 --- a/R/models.R +++ b/R/models.R @@ -1,3 +1,11 @@ +#' @srrstats {G2.7, G2.8, G2.9} Only matrix/mts/arrays as tabular data are +#' supported, not data.frame or similar objects. +#' +#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c} Missing observations are handled +#' automatically as per SSM theory, whereas missing values are not allowed +#' elsewhere. +#' + ## placeholder functions for fixed models default_prior_fn <- function(theta) { 0 diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 365e8501..441c1187 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -79,6 +79,8 @@ particle_smoother <- function(model, particles, ...) { particle_smoother.gaussian <- function(model, particles, method = "psi", seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -134,6 +136,8 @@ particle_smoother.nongaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -183,6 +187,8 @@ particle_smoother.ssm_nlg <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { + check_missingness(model) + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -243,6 +249,8 @@ particle_smoother.ssm_nlg <- function(model, particles, particle_smoother.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (L < 1) stop("Discretization level L must be larger than 0.") if (missing(particles)) { diff --git a/R/post_correction.R b/R/post_correction.R index 1bb8fa86..0de4b305 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -1,4 +1,5 @@ -# Get MAP estimate of theta +#' Get MAP estimate of theta +#' @param x Object of class \code{mcmc_output} get_map <- function(x) { x$theta[which.max(x$posterior), ] } @@ -80,6 +81,8 @@ suggest_N <- function(model, theta, candidates = seq(10, 100, by = 10), replications = 100, seed = sample(.Machine$integer.max, size = 1)) { + check_missingness(model) + replications <- check_integer(replications, "replications") seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) @@ -234,6 +237,8 @@ suggest_N <- function(model, theta, post_correct <- function(model, mcmc_output, particles, threads = 1L, is_type = "is2", seed = sample(.Machine$integer.max, size = 1)) { + check_missingness(model) + particles <- check_integer(particles, "particles") threads <- check_integer(threads, "threads") diff --git a/R/predict.R b/R/predict.R index 255acc28..5ad1a2d0 100644 --- a/R/predict.R +++ b/R/predict.R @@ -118,6 +118,8 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", future = TRUE, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (!inherits(model, "bbsm_model")) { stop("Argument 'model' should be an object of class 'bssm_model'.") } diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 7274ec3d..5dc53b9c 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,33 +1,3 @@ -# Integrated Autocorrelation Time -# -# Here IACT is based on Sokal, -# Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms -# @param x A vector. -iact <- function(x) { - n <- length(x) - x_ <- (x - mean(x)) / sd(x) - C <- max(5.0, log10(n)) - tau <- 1 - for (k in 1:(n - 1)) { - tau <- tau + 2.0 * (x_[1:(n-k)] %*% x_[(1+k):n]) / (n - k) - if (k > C * tau) break - } - max(0.0, tau) -} -# Asymptotic Variance of IS-type Estimators -# -# Estimates the asymptotic variance based on Corollary 1 -# of Vihola et al. (2020) from weighted samples from IS-MCMC. -# -# @param x Vector of samples. -# @param w Vector of weights. -asymptotic_var <- function(x, w) { - estimate_c <- mean(w) - estimate_mean <- weighted_mean(x, w) - z <- w * (x - estimate_mean) - iact(z) * var(z) / length(z) / estimate_c^2 -} - #' Print Results from MCMC Run #' #' Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. diff --git a/R/run_mcmc.R b/R/run_mcmc.R index aff50e6e..d652d6ab 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -101,12 +101,12 @@ #' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. #' https://doi.org/10.1007/s11222-011-9269-5 #' -#' [2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. +#' [2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +#' estimators based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' [3] Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. Arxiv preprint 2101.08492. +#' Non-Gaussian State Space Models in R. R Journal (to appear). #' https://arxiv.org/abs/2101.08492 #' run_mcmc <- function(model, ...) { @@ -140,6 +140,9 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { + + check_missingness(model) + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) @@ -341,6 +344,9 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, ...) { + + check_missingness(model) + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") @@ -498,6 +504,9 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, ...) { + + check_missingness(model) + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") @@ -653,6 +662,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { + check_missingness(model) + if (any(c(model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf) %in% c("", ""))) { stop(paste("NULL pointer detected, please recompile the pointer file", diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 3b61aced..5b375fec 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -31,7 +31,9 @@ sim_smoother <- function(model, nsim, seed, use_antithetic = TRUE, ...) { #' @export sim_smoother.gaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { - + + check_missingness(model) + nsim <- check_integer(nsim, "nsim") seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) diff --git a/R/smoother.R b/R/smoother.R index 2952a16e..6895b8be 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -27,6 +27,8 @@ fast_smoother <- function(model, ...) { #' ts.plot(cbind(Nile, fast_smoother(model)), col = 1:2) fast_smoother.gaussian <- function(model, ...) { + check_missingness(model) + out <- gaussian_fast_smoother(model, model_type(model)) colnames(out) <- names(model$a1) ts(out[-nrow(out), , drop = FALSE], start = start(model$y), @@ -57,6 +59,8 @@ smoother <- function(model, ...) { #' ts.plot(sqrt(out$Vt[1, 1, ])) smoother.gaussian <- function(model, ...) { + check_missingness(model) + out <- gaussian_smoother(model, model_type(model)) colnames(out$alphahat) <- colnames(out$Vt) <- rownames(out$Vt) <- names(model$a1) @@ -118,6 +122,8 @@ smoother.nongaussian <- function(model, ...) { #' } ekf_smoother <- function(model, iekf_iter = 0) { + check_missingness(model) + iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) out <- ekf_smoother_nlg(t(model$y), model$Z, model$H, model$T, @@ -137,6 +143,7 @@ ekf_smoother <- function(model, iekf_iter = 0) { #' @export ekf_fast_smoother <- function(model, iekf_iter = 0) { + check_missingness(model) iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index dfc0076f..3747ecbc 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -9,7 +9,6 @@ #' #' @srrstatsVerbose TRUE #' -#' #' ### Standards for general statistical software ### #' #' # General documentation, addressed by the paper vignette and the corresponding R Journal paper @@ -25,7 +24,7 @@ #' #' # As tested by autotest #' @srrstats {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* -#' @srrstats {G2.0a} Provide explicit secondary documentation of any expectations on lengths of inputs +#' @srrstats {G2.0a} *Provide explicit secondary documentation of any expectations on lengths of inputs.* #' @srrstats {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* #' @srrstats {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* #' @srrstats {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* @@ -35,7 +34,6 @@ #' @srrstats {G2.4a} *explicit conversion to `integer` via `as.integer()`* #' @srrstats {G2.4b} *explicit conversion to continuous via `as.numeric()`* #' @srrstats {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* -#' #' @srrstats {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* #' #' ## match.arg and tolower are used where applicable. @@ -48,17 +46,15 @@ #' @srrstats {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* #' @srrstats {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* #' -#' ## Missing observations are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere +#' ## Missing observations (y) are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere. Inputing or ignoring them does not make sense in time series context. #' @srrstats {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* #' @srrstats {G2.14a} *error on missing data* #' @srrstats {G2.14b} *ignore missing data with default warnings or messages issued* #' @srrstats {G2.14c} *replace missing data with appropriately imputed values* +#' @srrstats {G2.15} *Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default `na.rm = FALSE`-type parameters (such as [`mean()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/mean.html), [`sd()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) or [`cor()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html)).* +#' @srrstats {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* #' -#' # This is currently checked when building the model, should there be another check in say run_mcmc in case user manually alters the input model? -#' @srrstatsTODO {G2.15} *Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default `na.rm = FALSE`-type parameters (such as [`mean()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/mean.html), [`sd()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) or [`cor()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html)).* -#' @srrstatsTODO {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* -#' -#' @srrstatsTODO {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* +#' @srrstats {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* #' #' # Simulated datasets are used in several occasions #' @srrstats {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* diff --git a/README.Rmd b/README.Rmd index 60d75445..c95f71f8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -14,8 +14,23 @@ knitr::opts_chunk$set( ``` ```{r srr-tags, eval = FALSE, echo = FALSE} -#' @srrstatsTODO {G1.2} Contains project status badge. +#' @srrstats {G1.2} Contains project status badge. +#' @srrstats {G1.4,G1.4a} Package uses roxygen2 for documentation. +#' @srrstats {G2.0, G2.0a, G2.1, G2.1a, G2.2, G2.4, G2.4a, G2.4b, G2.4c, G2.6} +#' Input types and shapes are tested and checked with autotest and converted +#' explicitly when necessary. +#' +#' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower are used where +#' applicable. +#' +#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, G2.15, G2.16} Missing observations +#' (y) are handled automatically as per SSM theory, whereas missing values are +#' not allowed elsewhere. Inputing or ignoring them does not make sense in time +#' series context. +#' +#' @srrstats {G3.0} No floating point equality comparisons are made. ``` + # bssm @@ -37,8 +52,8 @@ supported. For details, see - * [the bssm paper on ArXiv](https://arxiv.org/abs/2101.08492), - * [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) + * [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), + * [Package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) There are also couple posters and a talk related to IS-correction methodology and bssm package: @@ -48,37 +63,99 @@ There are also couple posters and a talk related to IS-correction methodology an * [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) The `bssm` package was originally developed with the support of Academy of -Finland grants 284513, 312605, and 311877. Current development is focused on usability and improvidevelopemnt is +Finland grants 284513, 312605, and 311877. Current development is focused on +increased usability and stability. ## Installation You can install the released version of bssm from [CRAN](https://CRAN.R-project.org) with: -```{r} +```{r, eval=FALSE} install.packages("bssm") ``` And the development version from [GitHub](https://github.com/) with: -```{r} +```{r, eval=FALSE} # install.packages("devtools") devtools::install_github("helske/bssm") ``` Or from R-universe with -```{r} +```{r, eval = FALSE} install.packages("bssm", repos = "https://helske.r-universe.dev") ``` ## Example -This is a basic example which shows you how to solve a common problem: +Consider the daily air quality measurements in New Your from May to September 1973, available in the `datasets` package. Let's try to predict the missing ozone levels by simple linear-Gaussian local linear trend model with temperature and wind as explanatory variables: ```{r example} -library(bssm) -## basic example code +library("bssm") +library("dplyr") +library("ggplot2") + +data("airquality", package = "datasets") + +# Covariates as matrix. For complex cases, check out as_bssm function +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +model <- bsm_lg(airquality$Ozone, + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + sd_y = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.01), + sd_slope = gamma_prior(1, 2, 0.01)) + +fit <- run_mcmc(model, iter = 20000, burnin = 5000) +fit + + +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred <- fitted(fit, model) +pred %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() + ``` +Same model but now assuming observations are from Gamma distribution: +```{r gamma-example} + +model2 <- bsm_ng(airquality$Ozone, + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + distribution = "gamma", + phi = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.1), + sd_slope = gamma_prior(1, 2, 0.1)) + +fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) +fit2 +``` + +Comparison: +```{r compare} +pred2 <- fitted(fit2, model2) + +bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), + alpha = 0.25) + + geom_line(aes(colour = Model)) + + geom_point(data = obs, + aes(x = Time, y = Ozone)) + + theme_bw() +``` + + ## Recent changes (For all changes, see NEWS file.) #### bssm 1.1.6 (Release date: 2021-09-06) diff --git a/README.html b/README.html deleted file mode 100644 index 53b8a442..00000000 --- a/README.html +++ /dev/null @@ -1,347 +0,0 @@ - - - - - - - - - - - - - -README.knit - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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bssm: an R package for Bayesian inference of state space models

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Project Status: Active - The project has reached a stable, usable state and is being actively developed R-CMD-check Codecov test coverage cran version downloads

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Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type weighted MCMC. Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported.

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For details, see

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There are also couple posters and a talk related to IS-correction methodology and bssm package:

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You can install the latest development version from R-universe with

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install.packages("bssm", repos = "https://helske.r-universe.dev")
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Or by using the devtools package:

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install.packages("devtools")
-devtools::install_github("helske/bssm")
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Recent changes (For all changes, see NEWS file.)

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bssm 1.1.6 (Release date: 2021-09-06)

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  • Cleaned codes and added more comprehensive tests in line with pkgcheck tests. This resulted in finding and fixing multiple bugs:
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  • Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions.
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  • Fixed a bug which caused an error in suggest_N for nlg_ssm.
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  • Fixed a bug which caused incorrect sampling of smoothing distribution for ar1_lg model when predicting past or when using simulation smoother.
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  • Fixed a bug which caused an error when predicting past values in multivariate time series case.
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  • Fixed sampling of negative binomial distribution in predict method, which used std::negative_binomial which converts non-integer phi to integer. Sampling now uses Gamma-Poisson mixture for simulation.
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bssm 1.1.4 (Release date: 2021-04-13)

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  • Better documentation for SV model, and changed ordering of arguments to emphasise the recommended parameterization.
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  • Fixed predict method for SV model.
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bssm 1.1.3-2 (Release date: 2021-02-24)

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  • Fixed missing parenthesis causing compilation fail in case of no OpenMP support.
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  • Added pandoc version >= 1.12.3 to system requirements.
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bssm 1.1.3-1 (Release date: 2021-02-22)

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  • Fixed PM-MCMC and DA-MCMC for SDE models and added an example to ssm_sde.
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  • Added vignette for SDE models.
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  • Updated citation information and streamlined the main vignette.
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bssm 1.1.2 (Release date: 2021-02-08)

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bssm 1.1.0 (Release date: 2021-01-19)

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  • Added function suggest_N which can be used to choose suitable number of particles for IS-MCMC.
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  • Added function post_correct which can be used to update previous approximate MCMC with IS-weights.
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  • Gamma priors are now supported in easy-to-use models such as bsm_lg.
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  • The adaptation of the proposal distribution now continues also after the burn-in by default.
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  • Changed default MCMC type to typically most efficient and robust IS2.
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  • Renamed nsim argument to particles in most of the R functions (nsim also works with a warning).
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  • Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. This resulted error within MCMC algorithms.
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  • Fixed a dimension drop bug in the predict method which caused error for univariate models.
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bssm 1.0.1-1 (Release date: 2020-11-12)

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  • Added an argument future for predict method which allows predictions for current time points by supplying the original model (e.g., for posterior predictive checks). At the same time the argument name future_model was changed to model.
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  • Fixed a bug in summary.mcmc_run which resulted error when trying to obtain summary for states only.
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  • Added a check for Kalman filter for a degenerate case where all observational level and state level variances are zero.
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  • Renamed argument n_threads to threads for consistency with iter and burnin arguments.
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bssm 1.0.0 (Release date: 2020-06-09)

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Major update

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  • Major changes for model definitions, now model updating and priors can be defined via R functions (non-linear and SDE models still rely on C++ snippets).
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  • Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size.
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  • Allow zero as initial value for positive-constrained parameters of bsm models.
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  • Small changes to summary method which can now return also only summaries of the states.
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  • Fixed a bug in initializing run_mcmc for negative binomial model.
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bssm 0.1.11 (Release date: 2020-02-25)

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bssm 0.1.10 (Release date: 2020-02-04)

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bssm 0.1.9 (Release date: 2020-01-27)

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For older versions, see NEWS file.

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- - - - - - - - - - - - - - - diff --git a/README.md b/README.md index 2c6685e0..8e1f8ae2 100644 --- a/README.md +++ b/README.md @@ -1,164 +1,368 @@ -[![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) -[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) -[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) - - -bssm: an R package for Bayesian inference of state space models -========================================================================== - -```{r srr-tags, eval = FALSE, echo = FALSE} -#' @srrstats {G1.0} Cites of the paper introducing to the package, as well as -#' published paper proposing the IS-MCMC method used in \code{bssm}. -#' @srrstats {G1.1} The \code{bssm} package is -#' first to implement the IS-MCMC method as well as psi-APF particle filter -#' both introduced in Vihola, Helske, Franks (2020). -#' To our knowledge, the package is also the first R package to -#' implement delayed acceptance pseudo-marginal MCMC for general state space -#' models. -#' Note that the IS-MCMC method is also available in \code{walker} package for -#' limited class of models (subset of the models supported by \code{bssm}). -#' @srrstatsTODO {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* -#' @srrstats {G1.3} Terminology is introduced in the vignettes as well as in the corresponding papers. - -``` - -Efficient methods for Bayesian inference of state space models via particle -Markov chain Monte Carlo and importance sampling type weighted MCMC. -Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation -densities with linear-Gaussian state dynamics, as well as general non-linear -Gaussian models and discretely observed latent diffusion processes are -supported. - -For details, see -* [the bssm paper on ArXiv](https://arxiv.org/abs/2101.08492), -* [package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) -* Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) - -There are also couple posters and a talk related to IS-correction methodology and bssm package: -* [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) -* [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) -* [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) - -You can install the latest development version from R-universe with - -```R -install.packages("bssm", repos = "https://helske.r-universe.dev") -``` - -Or by using the devtools package: - -```R -install.packages("devtools") -devtools::install_github("helske/bssm") -``` - -Recent changes (For all changes, see NEWS file.) -========================================================================== - -bssm 1.1.6 (Release date: ) -============== - * Cleaned codes and added more comprehensive tests in line with pkgcheck - tests. This resulted in finding and fixing multiple bugs: - * Fixed a bug in EKF-based particle filter which returned filtered estimates - also in place of one-step ahead predictions. - * Fixed a bug which caused an error in suggest_N for nlg_ssm. - * Fixed a bug which caused incorrect sampling of smoothing distribution for - ar1_lg model when predicting past or when using simulation smoother. - * Fixed a bug which caused an error when predicting past values in - multivariate time series case. - * Fixed sampling of negative binomial distribution in predict method, which - used std::negative_binomial which converts non-integer phi to integer. - Sampling now uses Gamma-Poisson mixture for simulation. - -bssm 1.1.4 (Release date: 2021-04-13) -============== - * Better documentation for SV model, and changed ordering of arguments to emphasise the - recommended parameterization. - * Fixed predict method for SV model. - -bssm 1.1.3-2 (Release date: 2021-02-24) -============== - * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. - * Added pandoc version >= 1.12.3 to system requirements. - -bssm 1.1.3-1 (Release date: 2021-02-22) -============== - * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. - * Added vignette for SDE models. - * Updated citation information and streamlined the main vignette. - -bssm 1.1.2 (Release date: 2021-02-08) -============== - * Some bug fixes, see NEWS for details. - -bssm 1.1.0 (Release date: 2021-01-19) -============== - - * Added function `suggest_N` which can be used to choose - suitable number of particles for IS-MCMC. - * Added function `post_correct` which can be used to update - previous approximate MCMC with IS-weights. - * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. - * The adaptation of the proposal distribution now continues also after the burn-in by default. - * Changed default MCMC type to typically most efficient and robust IS2. - * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). - * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. - This resulted error within MCMC algorithms. - * Fixed a dimension drop bug in the predict method which caused error for univariate models. - * Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. - -bssm 1.0.1-1 (Release date: 2020-11-12) -============== - - * Added an argument `future` for predict method which allows - predictions for current time points by supplying the original model - (e.g., for posterior predictive checks). - At the same time the argument name `future_model` was changed to `model`. - * Fixed a bug in summary.mcmc_run which resulted error when - trying to obtain summary for states only. - * Added a check for Kalman filter for a degenerate case where all - observational level and state level variances are zero. - * Renamed argument `n_threads` to `threads` for consistency - with `iter` and `burnin` arguments. - * Improved documentation, added examples. - * Added a vignette regarding psi-APF for non-linear models. - -bssm 1.0.0 (Release date: 2020-06-09) -============== -Major update - - * Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely on C++ snippets). - * Added support for multivariate non-Gaussian models. - * Added support for gamma distributions. - * Added the function as.data.frame for mcmc output which converts the MCMC samples - to data.frame format for easier post-processing. - * Added truncated normal prior. - * Many argument names and model building functions have been changed for clarity and consistency. - * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. - * Allow zero as initial value for positive-constrained parameters of bsm models. - * Small changes to summary method which can now return also only summaries of the states. - * Fixed a bug in initializing run_mcmc for negative binomial model. - * Fixed a bug in phi-APF for non-linear models. - * Reimplemented predict method which now always produces data frame of samples. - -bssm 0.1.11 (Release date: 2020-02-25) -============== - * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, - as it seems to work better with noisy likelihood estimates. - * Print and summary methods for MCMC output are now coherent in their output. - -bssm 0.1.10 (Release date: 2020-02-04) -============== - * Fixed missing weight update for IS-SPDK without OPENMP flag. - * Removed unused usage argument ... from expand_sample. - -bssm 0.1.9 (Release date: 2020-01-27) -============== - * Fixed state sampling for PM-MCMC with SPDK. - * Added ts attribute for svm model. - * Corrected asymptotic variance for summary methods. - -For older versions, see NEWS file. + + + +# bssm + + + +[![Project Status: Active - The project has reached a stable, usable +state and is being actively +developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Codecov test +coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) +[![cran +version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) +[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) + + + +The `bssm` R package provides efficient methods for Bayesian inference +of state space models via particle Markov chain Monte Carlo and +importance sampling type weighted MCMC. Currently Gaussian, Poisson, +binomial, negative binomial, and Gamma observation densities with +linear-Gaussian state dynamics, as well as general non-linear Gaussian +models and discretely observed latent diffusion processes are supported. + +For details, see + +- [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to + appear in R Journal), +- [Package vignettes at + CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +- Paper on [Importance sampling type estimators based on approximate + marginal Markov chain Monte + Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) + +There are also couple posters and a talk related to IS-correction +methodology and bssm package: + +- [UseR!2021 talk + slides](https://jounihelske.netlify.app/talk/user2021/) +- [SMC 2017 workshop: Accelerating MCMC with an + approximation](http://users.jyu.fi/~jovetale/posters/SMC2017) +- [UseR!2017: Bayesian non-Gaussian state space models in + R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) + +The `bssm` package was originally developed with the support of Academy +of Finland grants 284513, 312605, and 311877. Current development is +focused on increased usability and stability. + +## Installation + +You can install the released version of bssm from +[CRAN](https://CRAN.R-project.org) with: + +``` r +install.packages("bssm") +``` + +And the development version from [GitHub](https://github.com/) with: + +``` r +# install.packages("devtools") +devtools::install_github("helske/bssm") +``` + +Or from R-universe with + +``` r +install.packages("bssm", repos = "https://helske.r-universe.dev") +``` + +## Example + +Consider the daily air quality measurements in New Your from May to +September 1973, available in the `datasets` package. Let’s try to +predict the missing ozone levels by simple linear-Gaussian local linear +trend model with temperature and wind as explanatory variables: + +``` r +library("bssm") +#> +#> Attaching package: 'bssm' +#> The following object is masked from 'package:base': +#> +#> gamma +library("dplyr") +#> +#> Attaching package: 'dplyr' +#> The following objects are masked from 'package:stats': +#> +#> filter, lag +#> The following objects are masked from 'package:base': +#> +#> intersect, setdiff, setequal, union +library("ggplot2") + +data("airquality", package = "datasets") + +# Covariates as matrix. For complex cases, check out as_bssm function +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +model <- bsm_lg(airquality$Ozone, + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + sd_y = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.01), + sd_slope = gamma_prior(1, 2, 0.01)) + +fit <- run_mcmc(model, iter = 20000, burnin = 5000) +fit +#> +#> Call: +#> run_mcmc.gaussian(model = model, iter = 20000, burnin = 5000) +#> +#> Iterations = 5001:20000 +#> Thinning interval = 1 +#> Length of the final jump chain = 3601 +#> +#> Acceptance rate after the burn-in period: 0.24 +#> +#> Summary for theta: +#> +#> Mean SD SE +#> sd_y 20.9745103 1.9548950 0.065711076 +#> sd_level 6.1826612 2.6898276 0.110026275 +#> sd_slope 0.3227774 0.2496854 0.008370595 +#> Wind -2.5224090 0.5692881 0.017491284 +#> Temp 1.0289642 0.1991063 0.006160738 +#> +#> Effective sample sizes for theta: +#> +#> ESS +#> sd_y 885.0538 +#> sd_level 597.6626 +#> sd_slope 889.7614 +#> Wind 1059.3047 +#> Temp 1044.4906 +#> +#> Summary for alpha_154: +#> +#> Mean SD SE +#> level -28.3237394 19.921774 0.55159040 +#> slope -0.3782085 1.723463 0.04120739 +#> +#> Effective sample sizes for alpha_154: +#> +#> ESS +#> level 1304.436 +#> slope 1749.257 +#> +#> Run time: +#> user system elapsed +#> 0.92 0.00 0.90 +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred <- fitted(fit, model) +pred %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() +``` + + + +Same model but now assuming observations are from Gamma distribution: + +``` r +model2 <- bsm_ng(airquality$Ozone, + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + distribution = "gamma", + phi = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.1), + sd_slope = gamma_prior(1, 2, 0.1)) + +fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) +fit2 +#> +#> Call: +#> run_mcmc.nongaussian(model = model2, iter = 20000, particles = 10, +#> burnin = 5000) +#> +#> Iterations = 5001:20000 +#> Thinning interval = 1 +#> Length of the final jump chain = 3902 +#> +#> Acceptance rate after the burn-in period: 0.26 +#> +#> Summary for theta: +#> +#> Mean SD SE SE-IS +#> sd_level 0.060119156 0.036304969 0.0021676154 7.340294e-04 +#> sd_slope 0.004282931 0.003742675 0.0001661556 7.508864e-05 +#> phi 3.974702619 0.514745897 0.0174002858 1.019891e-02 +#> Wind -0.057474480 0.015071259 0.0004125317 2.982147e-04 +#> Temp 0.052108192 0.008977271 0.0002718235 1.784298e-04 +#> +#> Effective sample sizes for theta: +#> +#> ESS +#> sd_level 280.5225 +#> sd_slope 507.3815 +#> phi 875.1309 +#> Wind 1334.7023 +#> Temp 1090.7239 +#> +#> Summary for alpha_154: +#> +#> Mean SD SE SE-IS +#> level -0.154108984 0.74092804 0.0206049048 0.0148100409 +#> slope -0.003410796 0.02343375 0.0005549973 0.0004921424 +#> +#> Effective sample sizes for alpha_154: +#> +#> ESS +#> level 1293.037 +#> slope 1782.797 +#> +#> Run time: +#> user system elapsed +#> 10.74 0.07 10.75 +``` + +Comparison: + +``` r +pred2 <- fitted(fit2, model2) + +bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), + alpha = 0.25) + + geom_line(aes(colour = Model)) + + geom_point(data = obs, + aes(x = Time, y = Ozone)) + + theme_bw() +``` + + + +## Recent changes (For all changes, see NEWS file.) + +#### bssm 1.1.6 (Release date: 2021-09-06) + +- Cleaned codes and added more comprehensive tests in line with + pkgcheck tests. This resulted in finding and fixing multiple bugs: +- Fixed a bug in EKF-based particle filter which returned filtered + estimates also in place of one-step ahead predictions. +- Fixed a bug which caused an error in suggest\_N for nlg\_ssm. +- Fixed a bug which caused incorrect sampling of smoothing + distribution for ar1\_lg model when predicting past or when using + simulation smoother. +- Fixed a bug which caused an error when predicting past values in + multivariate time series case. +- Fixed sampling of negative binomial distribution in predict method, + which used std::negative\_binomial which converts non-integer phi to + integer. Sampling now uses Gamma-Poisson mixture for simulation. + +#### bssm 1.1.4 (Release date: 2021-04-13) + +- Better documentation for SV model, and changed ordering of arguments + to emphasise the recommended parameterization. +- Fixed predict method for SV model. + +#### bssm 1.1.3-2 (Release date: 2021-02-24) + +- Fixed missing parenthesis causing compilation fail in case of no + OpenMP support. +- Added pandoc version >= 1.12.3 to system requirements. + +#### bssm 1.1.3-1 (Release date: 2021-02-22) + +- Fixed PM-MCMC and DA-MCMC for SDE models and added an example to + `ssm_sde`. +- Added vignette for SDE models. +- Updated citation information and streamlined the main vignette. + +#### bssm 1.1.2 (Release date: 2021-02-08) + +- Some bug fixes, see NEWS for details. + +#### bssm 1.1.0 (Release date: 2021-01-19) + +- Added function `suggest_N` which can be used to choose suitable + number of particles for IS-MCMC. +- Added function `post_correct` which can be used to update previous + approximate MCMC with IS-weights. +- Gamma priors are now supported in easy-to-use models such as + `bsm_lg`. +- The adaptation of the proposal distribution now continues also after + the burn-in by default. +- Changed default MCMC type to typically most efficient and robust + IS2. +- Renamed `nsim` argument to `particles` in most of the R functions + (`nsim` also works with a warning). +- Fixed a bug with bsm models with covariates, where all standard + deviation parameters were fixed. This resulted error within MCMC + algorithms. +- Fixed a dimension drop bug in the predict method which caused error + for univariate models. +- Fixed few typos in vignette (thanks Kyle Hussman) and added more + examples. + +#### bssm 1.0.1-1 (Release date: 2020-11-12) + +- Added an argument `future` for predict method which allows + predictions for current time points by supplying the original model + (e.g., for posterior predictive checks). At the same time the + argument name `future_model` was changed to `model`. +- Fixed a bug in summary.mcmc\_run which resulted error when trying to + obtain summary for states only. +- Added a check for Kalman filter for a degenerate case where all + observational level and state level variances are zero. +- Renamed argument `n_threads` to `threads` for consistency with + `iter` and `burnin` arguments. +- Improved documentation, added examples. +- Added a vignette regarding psi-APF for non-linear models. + +#### bssm 1.0.0 (Release date: 2020-06-09) + +Major update + +- Major changes for model definitions, now model updating and priors + can be defined via R functions (non-linear and SDE models still rely + on C++ snippets). +- Added support for multivariate non-Gaussian models. +- Added support for gamma distributions. +- Added the function as.data.frame for mcmc output which converts the + MCMC samples to data.frame format for easier post-processing. +- Added truncated normal prior. +- Many argument names and model building functions have been changed + for clarity and consistency. +- Major overhaul of C++ internals which can bring minor efficiency + gains and smaller installation size. +- Allow zero as initial value for positive-constrained parameters of + bsm models. +- Small changes to summary method which can now return also only + summaries of the states. +- Fixed a bug in initializing run\_mcmc for negative binomial model. +- Fixed a bug in phi-APF for non-linear models. +- Reimplemented predict method which now always produces data frame of + samples. + +#### bssm 0.1.11 (Release date: 2020-02-25) + +- Switched (back) to approximate posterior in RAM for PM-SPDK and + PM-PSI, as it seems to work better with noisy likelihood estimates. +- Print and summary methods for MCMC output are now coherent in their + output. + +#### bssm 0.1.10 (Release date: 2020-02-04) + +- Fixed missing weight update for IS-SPDK without OPENMP flag. +- Removed unused usage argument … from expand\_sample. + +#### bssm 0.1.9 (Release date: 2020-01-27) + +- Fixed state sampling for PM-MCMC with SPDK. +- Added ts attribute for svm model. +- Corrected asymptotic variance for summary methods. + +For older versions, see NEWS file. diff --git a/man/figures/README-compare-1.png b/man/figures/README-compare-1.png new file mode 100644 index 0000000000000000000000000000000000000000..6c996d488ee4536d8736d7a3338dc391de001f61 GIT binary patch literal 13077 zcmbt*2Uru|vuHL6354E}s?uUWkuH5f5$UT0QBf2T2q0hqq_YuFK`9DYkg|$m2nZHB z0zXASQ6vgT2SI6(UIanlEx-TozVE(!-+lLf-^-VLo3lG-&di*dIdkUB#zA%#yd+5y z27}?XJh1N&27^T~7~mTx4!vW0NR5DAen2)z8kv_|D^>J{}&MK*=8Hr?k|gw8x`#!_U^w&oOOum(HuC*!^EW{g^WWv?|JZo;ZSY0Y z`%(`~sfT{)#zswhTkZ7ByF}Y}8yh{z`aPu{JsYJx8yi3J{eEn0{QUWI>eGkiAm6pV z?u}B9jdzJC5c+Im1MRxm0|ngJ*q~Eh-$1$KI(OjcMGS_oZu18;`ZLa8Fk3N}`%J09 z8Iyh7IXybJB~B?O+&uH7W{Yy)RB4ut?59!(PY>0{z}ZW}CsXXCdzLpgp5Q;dPQh>) zE0_)G#T5mZj|(W&a$nwhKt_SXy#9P=B}PPKuPL~L$%nN%7$^Y6Go#@{R2h8gL-t+b zfj~y1W(X 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z_1s)zIuduIW4nok^UcxIXw_ekuT1x|9^8Sw6JukyoVYC?HCAg!g+`UF`7{g`z7g*q za;RJy${Z+7aNxEenmpd`MaKRIqxl}}a+m63(gvSoCvyryV=76NLsQo7Db*fNNmHQG zoifKuRmOKC1~%;pyu@YAhDlHFxEr$ORT{g<6Y9&!$Xaz~Z{t?M3H4m%EB^{P{0|Hw zS8Q%LM0ZGJX<_11j_{>Y0{f9NUGF9cjwP_y@xU9+Lfn>W9&>5Y@`>%@{A8odMC(%E zQkB7G6CU>#e$uvjDdyK6o2B0)pQ^)__}tEhc1nM<1jbf?oqolCvA^-${Fwyfa|^$( zW Date: Wed, 17 Nov 2021 11:50:23 +0200 Subject: [PATCH 109/180] ncol to nrow --- R/as_draws.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/as_draws.R b/R/as_draws.R index b306c6b3..4f2c41a1 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -52,7 +52,7 @@ as_draws_df.mcmc_output <- function(x, times, ...) { d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) - if (missing(times)) times <- seq_len(ncol(x$alpha)) + if (missing(times)) times <- seq_len(nrow(x$alpha)) d_states <- as.data.frame(x, variable = "states", expand = TRUE, times = times, use_times = FALSE) From 9a3b4f2c2c66ed78196eaa1a0b13d0acd268ae64 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 11:53:37 +0200 Subject: [PATCH 110/180] replication --- .Rbuildignore | 1 + R/bssm-package.R | 19 ++ R/models.R | 15 + R/srr-stats-standards.R | 6 +- README.Rmd | 6 + README.md | 72 ++--- benchmarks/replications.Rmd | 94 ++++++ benchmarks/replications.html | 530 +++++++++++++++++++++++++++++++ man/as_bssm.Rd | 5 +- man/bsm_ng.Rd | 15 + man/figures/README-compare-1.png | Bin 13077 -> 13137 bytes man/figures/README-example-1.png | Bin 10931 -> 10976 bytes man/negbin_series.Rd | 19 ++ tests/testthat/test_approx.R | 17 +- 14 files changed, 759 insertions(+), 40 deletions(-) create mode 100644 benchmarks/replications.Rmd create mode 100644 benchmarks/replications.html diff --git a/.Rbuildignore b/.Rbuildignore index 5f0fa316..885af96d 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -16,3 +16,4 @@ ^codecov\.yml$ ^codemeta\.json$ ^README\.Rmd$ +^benchmarks$ diff --git a/R/bssm-package.R b/R/bssm-package.R index 7ef99d37..d9d73477 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -135,4 +135,23 @@ NULL #' level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) #' x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) #' y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) +#' +#' +#' # Construct model for bssm +#' bssm_model <- bsm_ng(y, +#' xreg = x, +#' beta = normal(0, 0, 10), +#' phi = halfnormal(1, 10), +#' sd_level = halfnormal(0.1, 1), +#' sd_slope = halfnormal(0.01, 0.1), +#' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), +#' distribution = "negative binomial") +#' +#' # run the MCMC, small number of iterations for CRAN +#' fit_bssm <- run_mcmc(bssm_model, iter = 6000, burnin = 1000, +#' particles = 10) +#' fit_bssm +#' +#' plot.ts(ts(cbind(sd_level=draws$sd_level, draws$sd_slope), start = 1000), +#' xlab = "Iteration", main = "Traceplots") NULL \ No newline at end of file diff --git a/R/models.R b/R/models.R index 9a15af09..50373065 100644 --- a/R/models.R +++ b/R/models.R @@ -833,6 +833,21 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' @return Object of class \code{bsm_ng}. #' @export #' @examples +#' # Same data as in Vihola, Helske, Franks (2020) +#' data(poisson_series) +#' s <- sd(log(pmax(0.1, poisson_series))) +#' model <- bsm_ng(poisson_series, sd_level = uniform(0.115, 0, 2 * s), +#' sd_slope = uniform(0.004, 0, 2 * s), P1 = diag(0.1, 2), +#' distribution = "poisson") +#' +#' \donttest{ +#' out <- run_mcmc(model, iter = 1e5, particles = 10) +#' summary(out, variable = "theta", return_se = TRUE) +#' # should be about 0.093 and 0.016 +#' summary(out, variable = "states", return_se = TRUE)$Mean[c(1,100),1] +#' # should be about -0.075, 2.618 +#' } +#' #' model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", #' sd_level = halfnormal(0.01, 1), #' sd_seasonal = halfnormal(0.01, 1), diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 3747ecbc..dc6e62e7 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -65,8 +65,8 @@ #' @srrstatsTODO {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* #' @srrstatsTODO {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* -#' ## Correctness is demonstrated in Helske & Vihola (2021) and Vihola & Helske & Franks (2020) -#' ## Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation +#' ## Correctness is demonstrated in Helske & Vihola (2021) (comparison with Stan) and Vihola & Helske & Franks (2020) (correcness of the IS-MCMC etc) +#' ## Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation. #' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* #' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* #' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* @@ -76,6 +76,8 @@ #' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* #' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* #' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* + + #' @srrstatsTODO {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* #' @srrstatsTODO {G5.8a} *Zero-length data* #' @srrstatsTODO {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* diff --git a/README.Rmd b/README.Rmd index c95f71f8..2304a116 100644 --- a/README.Rmd +++ b/README.Rmd @@ -29,6 +29,12 @@ knitr::opts_chunk$set( #' series context. #' #' @srrstats {G3.0} No floating point equality comparisons are made. +#' +#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The +#' algorithms work correctly as per Vihola, Helske, Franks (2020) +#' (all simulations were implemented with the bssm package) and Helske +#' and Vihola (2021). Full replication of the results would take days/weeks +#' (but see also bsm_ng and testthat tests). ``` # bssm diff --git a/README.md b/README.md index 8e1f8ae2..a8b4dbd5 100644 --- a/README.md +++ b/README.md @@ -115,43 +115,43 @@ fit #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3601 +#> Length of the final jump chain = 3479 #> -#> Acceptance rate after the burn-in period: 0.24 +#> Acceptance rate after the burn-in period: 0.232 #> #> Summary for theta: #> #> Mean SD SE -#> sd_y 20.9745103 1.9548950 0.065711076 -#> sd_level 6.1826612 2.6898276 0.110026275 -#> sd_slope 0.3227774 0.2496854 0.008370595 -#> Wind -2.5224090 0.5692881 0.017491284 -#> Temp 1.0289642 0.1991063 0.006160738 +#> sd_y 20.9429622 1.9383343 0.068905655 +#> sd_level 6.2794066 2.7536555 0.100973147 +#> sd_slope 0.3345056 0.2623785 0.008440415 +#> Wind -2.5564842 0.5471567 0.019052430 +#> Temp 1.0258170 0.1961969 0.007047035 #> #> Effective sample sizes for theta: #> -#> ESS -#> sd_y 885.0538 -#> sd_level 597.6626 -#> sd_slope 889.7614 -#> Wind 1059.3047 -#> Temp 1044.4906 +#> ESS +#> sd_y 791.3118 +#> sd_level 743.7165 +#> sd_slope 966.3380 +#> Wind 824.7507 +#> Temp 775.1242 #> #> Summary for alpha_154: #> -#> Mean SD SE -#> level -28.3237394 19.921774 0.55159040 -#> slope -0.3782085 1.723463 0.04120739 +#> Mean SD SE +#> level -28.039885 19.409460 0.56324484 +#> slope -0.366811 1.689306 0.03873495 #> #> Effective sample sizes for alpha_154: #> #> ESS -#> level 1304.436 -#> slope 1749.257 +#> level 1187.497 +#> slope 1902.002 #> #> Run time: #> user system elapsed -#> 0.92 0.00 0.90 +#> 0.89 0.02 0.91 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -188,43 +188,43 @@ fit2 #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3902 +#> Length of the final jump chain = 3834 #> -#> Acceptance rate after the burn-in period: 0.26 +#> Acceptance rate after the burn-in period: 0.256 #> #> Summary for theta: #> #> Mean SD SE SE-IS -#> sd_level 0.060119156 0.036304969 0.0021676154 7.340294e-04 -#> sd_slope 0.004282931 0.003742675 0.0001661556 7.508864e-05 -#> phi 3.974702619 0.514745897 0.0174002858 1.019891e-02 -#> Wind -0.057474480 0.015071259 0.0004125317 2.982147e-04 -#> Temp 0.052108192 0.008977271 0.0002718235 1.784298e-04 +#> sd_level 0.063067042 0.038284435 0.0016425712 8.029996e-04 +#> sd_slope 0.004092602 0.003530829 0.0001813586 7.127555e-05 +#> phi 3.986113336 0.531933924 0.0164752602 1.103394e-02 +#> Wind -0.056960598 0.015218618 0.0004459602 3.047884e-04 +#> Temp 0.052900914 0.008784547 0.0002490039 1.764499e-04 #> #> Effective sample sizes for theta: #> #> ESS -#> sd_level 280.5225 -#> sd_slope 507.3815 -#> phi 875.1309 -#> Wind 1334.7023 -#> Temp 1090.7239 +#> sd_level 543.2454 +#> sd_slope 379.0330 +#> phi 1042.4394 +#> Wind 1164.5504 +#> Temp 1244.5905 #> #> Summary for alpha_154: #> #> Mean SD SE SE-IS -#> level -0.154108984 0.74092804 0.0206049048 0.0148100409 -#> slope -0.003410796 0.02343375 0.0005549973 0.0004921424 +#> level -0.210918261 0.73523171 0.0208711444 0.0150506204 +#> slope -0.002735397 0.02261717 0.0004652766 0.0004946287 #> #> Effective sample sizes for alpha_154: #> #> ESS -#> level 1293.037 -#> slope 1782.797 +#> level 1240.955 +#> slope 2362.948 #> #> Run time: #> user system elapsed -#> 10.74 0.07 10.75 +#> 10.81 0.14 10.79 ``` Comparison: diff --git a/benchmarks/replications.Rmd b/benchmarks/replications.Rmd new file mode 100644 index 00000000..67e1380b --- /dev/null +++ b/benchmarks/replications.Rmd @@ -0,0 +1,94 @@ +--- +title: "Replications" +author: "Jouni Helske" +date: "11/17/2021" +output: html_document +--- + +```{r srr-tags, eval = FALSE, echo = FALSE} +#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The +#' algorithms work correctly as per Vihola, Helske, Franks (2020) +#' (all simulations were implemented with the bssm package) and Helske +#' and Vihola (2021). +#' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* +#' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* +#' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* +#' @srrstats {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* +#' @srrstats {G5.5} *Correctness tests should be run with a fixed random seed* +#' @srrstats {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* +#' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* +#' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* +#' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* +``` + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Reproducing some of the results of IS-MCMC paper + +Full simulation experiments of Vihola, Helske and Franks (2020) takes some time, +here we only run a single replication to test that the methods work as expected. +To generate Table 1 in the paper, the code below should be run say 1000 times, +from which IREs could be computed. + +```{r, cache = TRUE} +library("bssm") +data(poisson_series) +s <- sd(log(pmax(0.1, poisson_series))) +model <- bsm_ng(poisson_series, sd_level = uniform(0.115, 0, 2 * s), + sd_slope = uniform(0.004, 0, 2 * s), P1 = diag(0.1, 2), distribution = "poisson") + + +d <- data.frame(mean = NA, se = NA, + variable = c("sd_level", "sd_slope", "u_1", "u_100"), + mcmc_type = rep(c("approx", "da", "is1", "is2", "pm"), + times = 4*c(2, 6, 6, 6, 6)), + sampling_method = c(rep("psi", 8), + rep(rep(c("bsf", "spdk", "psi"), each = 2 * 4), 4)), + local_approx = rep(c(TRUE, FALSE), each = 4), + time = NA, + acceptance_rate = NA) + +iter <- 1e4 # Use less iterations than in the paper for faster experiment +for(i in seq(1, nrow(d), by = 4)) { + + cat("Testing method '", d$mcmc_type[i], "' with sampling by '", + d$sampling_method[i], "' and local_approx '", d$local_approx[i], "'\n", + sep = "") + + res <- run_mcmc(model, iter = iter, + sampling_method = d$sampling_method[i], + particles = switch(d$sampling_method[i], + bsf = 200, + spdk = 10, + psi = 10), + mcmc_type = d$mcmc_type[i], + local_approx = d$local_approx[i], + end_adaptive_phase = TRUE) + + w <- res$counts * + if (res$mcmc_type %in% paste0("is", 1:2)) res$weights else 1 + + d[((i - 1) + 1):((i - 1) + 4), "mean"] <- c( + diagis::weighted_mean(res$theta, w), + diagis::weighted_mean(res$alpha[1, 1, ], w), + diagis::weighted_mean(res$alpha[100, 1, ], w)) + + d[((i - 1) + 1):((i - 1) + 4), "se"] <- c( + sqrt(asymptotic_var(res$theta[, 1], w)), + sqrt(asymptotic_var(res$theta[, 2], w)), + sqrt(asymptotic_var(res$alpha[1, 1, ], w)), + sqrt(asymptotic_var(res$alpha[100, 1, ], w))) + + d$time[((i - 1) + 1):((i - 1) + 4)] <- res$time["elapsed"] + d$acceptance_rate[((i - 1) + 1):((i - 1) + 4)] <- res$acceptance_rate +} +``` +Results: +```{r} +library(dplyr) +d %>% + arrange(local_approx, variable, mcmc_type, sampling_method) +``` + diff --git a/benchmarks/replications.html b/benchmarks/replications.html new file mode 100644 index 00000000..abe8ed8f --- /dev/null +++ b/benchmarks/replications.html @@ -0,0 +1,530 @@ + + + + + + + + + + + + + + + +Replications + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Reproducing some of the results of IS-MCMC paper

+

Full simulation experiments of Vihola, Helske and Franks (2020) takes some time, here we only run a single replication to test that the methods work as expected. To generate Table 1 in the paper, the code below should be run say 1000 times, from which IREs could be computed.

+
library("bssm")
+
## 
+## Attaching package: 'bssm'
+
## The following object is masked from 'package:base':
+## 
+##     gamma
+
data(poisson_series)
+s <- sd(log(pmax(0.1, poisson_series)))
+model <- bsm_ng(poisson_series, sd_level = uniform(0.115, 0, 2 * s),
+  sd_slope = uniform(0.004, 0, 2 * s), P1 = diag(0.1, 2), distribution = "poisson")
+
+
+d <- data.frame(mean = NA, se = NA,
+  variable = c("sd_level", "sd_slope", "u_1", "u_100"),
+  mcmc_type = rep(c("approx", "da", "is1", "is2", "pm"),
+    times = 4*c(2, 6, 6, 6, 6)),
+  sampling_method = c(rep("psi", 8),
+    rep(rep(c("bsf", "spdk", "psi"), each = 2 * 4), 4)),
+  local_approx = rep(c(TRUE, FALSE), each = 4),
+  time = NA,
+  acceptance_rate = NA)
+
+iter <- 1e4 # Use less iterations than in the paper for faster experiment
+for(i in seq(1, nrow(d), by = 4)) {
+  
+  cat("Testing method '", d$mcmc_type[i], "' with sampling by '", 
+    d$sampling_method[i], "' and local_approx '", d$local_approx[i], "'\n", 
+    sep = "")
+  
+  res <- run_mcmc(model, iter = iter,
+    sampling_method = d$sampling_method[i],
+    particles = switch(d$sampling_method[i],
+      bsf = 200,
+      spdk = 10,
+      psi = 10),
+    mcmc_type = d$mcmc_type[i],
+    local_approx = d$local_approx[i],
+    end_adaptive_phase = TRUE)
+  
+  w <- res$counts * 
+    if (res$mcmc_type %in% paste0("is", 1:2)) res$weights else 1
+  
+  d[((i - 1) + 1):((i - 1) + 4), "mean"] <- c(
+    diagis::weighted_mean(res$theta, w),
+    diagis::weighted_mean(res$alpha[1, 1, ], w),
+    diagis::weighted_mean(res$alpha[100, 1, ], w))
+  
+  d[((i - 1) + 1):((i - 1) + 4), "se"] <- c(
+    sqrt(asymptotic_var(res$theta[, 1], w)),
+    sqrt(asymptotic_var(res$theta[, 2], w)),
+    sqrt(asymptotic_var(res$alpha[1, 1, ], w)),
+    sqrt(asymptotic_var(res$alpha[100, 1, ], w)))
+  
+  d$time[((i - 1) + 1):((i - 1) + 4)] <- res$time["elapsed"]
+  d$acceptance_rate[((i - 1) + 1):((i - 1) + 4)] <- res$acceptance_rate
+}
+
## Testing method 'approx' with sampling by 'psi' and local_approx 'TRUE'
+## Testing method 'approx' with sampling by 'psi' and local_approx 'FALSE'
+## Testing method 'da' with sampling by 'bsf' and local_approx 'TRUE'
+## Testing method 'da' with sampling by 'bsf' and local_approx 'FALSE'
+## Testing method 'da' with sampling by 'spdk' and local_approx 'TRUE'
+## Testing method 'da' with sampling by 'spdk' and local_approx 'FALSE'
+## Testing method 'da' with sampling by 'psi' and local_approx 'TRUE'
+## Testing method 'da' with sampling by 'psi' and local_approx 'FALSE'
+## Testing method 'is1' with sampling by 'bsf' and local_approx 'TRUE'
+## Testing method 'is1' with sampling by 'bsf' and local_approx 'FALSE'
+## Testing method 'is1' with sampling by 'spdk' and local_approx 'TRUE'
+## Testing method 'is1' with sampling by 'spdk' and local_approx 'FALSE'
+## Testing method 'is1' with sampling by 'psi' and local_approx 'TRUE'
+## Testing method 'is1' with sampling by 'psi' and local_approx 'FALSE'
+## Testing method 'is2' with sampling by 'bsf' and local_approx 'TRUE'
+## Testing method 'is2' with sampling by 'bsf' and local_approx 'FALSE'
+## Testing method 'is2' with sampling by 'spdk' and local_approx 'TRUE'
+## Testing method 'is2' with sampling by 'spdk' and local_approx 'FALSE'
+## Testing method 'is2' with sampling by 'psi' and local_approx 'TRUE'
+## Testing method 'is2' with sampling by 'psi' and local_approx 'FALSE'
+## Testing method 'pm' with sampling by 'bsf' and local_approx 'TRUE'
+## Testing method 'pm' with sampling by 'bsf' and local_approx 'FALSE'
+## Testing method 'pm' with sampling by 'spdk' and local_approx 'TRUE'
+## Testing method 'pm' with sampling by 'spdk' and local_approx 'FALSE'
+## Testing method 'pm' with sampling by 'psi' and local_approx 'TRUE'
+## Testing method 'pm' with sampling by 'psi' and local_approx 'FALSE'
+

Results:

+
library(dplyr)
+
## 
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+
## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+
d %>% 
+    arrange(local_approx, variable, mcmc_type, sampling_method)
+
##            mean           se variable mcmc_type sampling_method local_approx
+## 1    0.09578772 0.0022141806 sd_level    approx             psi        FALSE
+## 2    0.09183384 0.0025847529 sd_level        da             bsf        FALSE
+## 3    0.09056407 0.0034324313 sd_level        da             psi        FALSE
+## 4    0.09693717 0.0019153340 sd_level        da            spdk        FALSE
+## 5    0.09562614 0.0025997452 sd_level       is1             bsf        FALSE
+## 6    0.09303018 0.0027318562 sd_level       is1             psi        FALSE
+## 7    0.09758895 0.0025748789 sd_level       is1            spdk        FALSE
+## 8    0.10105081 0.0025722112 sd_level       is2             bsf        FALSE
+## 9    0.09536685 0.0027073170 sd_level       is2             psi        FALSE
+## 10   0.09590957 0.0023060056 sd_level       is2            spdk        FALSE
+## 11   0.09698759 0.0038890507 sd_level        pm             bsf        FALSE
+## 12   0.09430755 0.0024183804 sd_level        pm             psi        FALSE
+## 13   0.08704774 0.0035083078 sd_level        pm            spdk        FALSE
+## 14   0.01506997 0.0004887884 sd_slope    approx             psi        FALSE
+## 15   0.01622993 0.0007867216 sd_slope        da             bsf        FALSE
+## 16   0.01736272 0.0007476045 sd_slope        da             psi        FALSE
+## 17   0.01542487 0.0004711415 sd_slope        da            spdk        FALSE
+## 18   0.01533332 0.0005808372 sd_slope       is1             bsf        FALSE
+## 19   0.01653758 0.0006161702 sd_slope       is1             psi        FALSE
+## 20   0.01555065 0.0006237624 sd_slope       is1            spdk        FALSE
+## 21   0.01450235 0.0007518923 sd_slope       is2             bsf        FALSE
+## 22   0.01555210 0.0005458160 sd_slope       is2             psi        FALSE
+## 23   0.01522803 0.0005224722 sd_slope       is2            spdk        FALSE
+## 24   0.01526484 0.0008580442 sd_slope        pm             bsf        FALSE
+## 25   0.01726606 0.0005463481 sd_slope        pm             psi        FALSE
+## 26   0.01719711 0.0005954383 sd_slope        pm            spdk        FALSE
+## 27  -0.07869788 0.0105559757      u_1    approx             psi        FALSE
+## 28  -0.06847499 0.0092537409      u_1        da             bsf        FALSE
+## 29  -0.07676795 0.0124369314      u_1        da             psi        FALSE
+## 30  -0.08390600 0.0098000700      u_1        da            spdk        FALSE
+## 31  -0.08702685 0.0125916067      u_1       is1             bsf        FALSE
+## 32  -0.07410507 0.0095352137      u_1       is1             psi        FALSE
+## 33  -0.08393639 0.0114658112      u_1       is1            spdk        FALSE
+## 34  -0.08050907 0.0179799047      u_1       is2             bsf        FALSE
+## 35  -0.07701605 0.0105821573      u_1       is2             psi        FALSE
+## 36  -0.07846330 0.0121624205      u_1       is2            spdk        FALSE
+## 37  -0.09249907 0.0106879249      u_1        pm             bsf        FALSE
+## 38  -0.08991847 0.0111100364      u_1        pm             psi        FALSE
+## 39  -0.06743786 0.0106694815      u_1        pm            spdk        FALSE
+## 40   2.63760608 0.0079707360    u_100    approx             psi        FALSE
+## 41   2.63182502 0.0080169407    u_100        da             bsf        FALSE
+## 42   2.62174704 0.0073447869    u_100        da             psi        FALSE
+## 43   2.61369859 0.0072760694    u_100        da            spdk        FALSE
+## 44   2.62253096 0.0086131210    u_100       is1             bsf        FALSE
+## 45   2.62697081 0.0073913295    u_100       is1             psi        FALSE
+## 46   2.61395948 0.0065218848    u_100       is1            spdk        FALSE
+## 47   2.62980275 0.0117036903    u_100       is2             bsf        FALSE
+## 48   2.62820881 0.0061786231    u_100       is2             psi        FALSE
+## 49   2.61647003 0.0066978454    u_100       is2            spdk        FALSE
+## 50   2.62452635 0.0071852884    u_100        pm             bsf        FALSE
+## 51   2.62372637 0.0064406343    u_100        pm             psi        FALSE
+## 52   2.61946902 0.0053898263    u_100        pm            spdk        FALSE
+## 53   0.09332580 0.0026097129 sd_level    approx             psi         TRUE
+## 54   0.08850704 0.0034589396 sd_level        da             bsf         TRUE
+## 55   0.09495943 0.0026074340 sd_level        da             psi         TRUE
+## 56   0.09321799 0.0025949141 sd_level        da            spdk         TRUE
+## 57   0.08969266 0.0024885812 sd_level       is1             bsf         TRUE
+## 58   0.09524581 0.0026432277 sd_level       is1             psi         TRUE
+## 59   0.09200344 0.0025662615 sd_level       is1            spdk         TRUE
+## 60   0.09164685 0.0029147370 sd_level       is2             bsf         TRUE
+## 61   0.09085042 0.0026602853 sd_level       is2             psi         TRUE
+## 62   0.09401393 0.0023809428 sd_level       is2            spdk         TRUE
+## 63   0.09946007 0.0029061242 sd_level        pm             bsf         TRUE
+## 64   0.09425689 0.0026954951 sd_level        pm             psi         TRUE
+## 65   0.09196438 0.0034327278 sd_level        pm            spdk         TRUE
+## 66   0.01649875 0.0006084374 sd_slope    approx             psi         TRUE
+## 67   0.01629020 0.0007204475 sd_slope        da             bsf         TRUE
+## 68   0.01675024 0.0005625769 sd_slope        da             psi         TRUE
+## 69   0.01596388 0.0005902601 sd_slope        da            spdk         TRUE
+## 70   0.01684928 0.0005048439 sd_slope       is1             bsf         TRUE
+## 71   0.01619417 0.0006082412 sd_slope       is1             psi         TRUE
+## 72   0.01549983 0.0005977432 sd_slope       is1            spdk         TRUE
+## 73   0.01704707 0.0007564945 sd_slope       is2             bsf         TRUE
+## 74   0.01713414 0.0005378904 sd_slope       is2             psi         TRUE
+## 75   0.01581035 0.0006098587 sd_slope       is2            spdk         TRUE
+## 76   0.01493558 0.0007570220 sd_slope        pm             bsf         TRUE
+## 77   0.01681022 0.0005775678 sd_slope        pm             psi         TRUE
+## 78   0.01669934 0.0006237532 sd_slope        pm            spdk         TRUE
+## 79  -0.04763190 0.0098184938      u_1    approx             psi         TRUE
+## 80  -0.06303314 0.0108945672      u_1        da             bsf         TRUE
+## 81  -0.07412832 0.0118281401      u_1        da             psi         TRUE
+## 82  -0.06864390 0.0107736811      u_1        da            spdk         TRUE
+## 83  -0.06413780 0.0102697767      u_1       is1             bsf         TRUE
+## 84  -0.07305205 0.0087975786      u_1       is1             psi         TRUE
+## 85  -0.05281387 0.0087550795      u_1       is1            spdk         TRUE
+## 86  -0.06758727 0.0181045578      u_1       is2             bsf         TRUE
+## 87  -0.08541492 0.0092005051      u_1       is2             psi         TRUE
+## 88  -0.06533748 0.0107268023      u_1       is2            spdk         TRUE
+## 89  -0.07094869 0.0127049274      u_1        pm             bsf         TRUE
+## 90  -0.08475790 0.0099826212      u_1        pm             psi         TRUE
+## 91  -0.07116972 0.0117171069      u_1        pm            spdk         TRUE
+## 92   2.62666325 0.0064783980    u_100    approx             psi         TRUE
+## 93   2.63482193 0.0078166866    u_100        da             bsf         TRUE
+## 94   2.61414177 0.0064441268    u_100        da             psi         TRUE
+## 95   2.61282885 0.0065739149    u_100        da            spdk         TRUE
+## 96   2.61354285 0.0064132143    u_100       is1             bsf         TRUE
+## 97   2.61608214 0.0071682674    u_100       is1             psi         TRUE
+## 98   2.62069831 0.0067225577    u_100       is1            spdk         TRUE
+## 99   2.60570014 0.0103065461    u_100       is2             bsf         TRUE
+## 100  2.62148516 0.0074049226    u_100       is2             psi         TRUE
+## 101  2.61732719 0.0067673451    u_100       is2            spdk         TRUE
+## 102  2.61310072 0.0074142079    u_100        pm             bsf         TRUE
+## 103  2.60707729 0.0068890297    u_100        pm             psi         TRUE
+## 104  2.60921701 0.0070487136    u_100        pm            spdk         TRUE
+##       time acceptance_rate
+## 1     1.04          0.2336
+## 2    16.59          0.2466
+## 3     2.31          0.2068
+## 4     1.65          0.2202
+## 5   143.79          0.2348
+## 6     4.13          0.2238
+## 7     2.34          0.2262
+## 8     8.51          0.2300
+## 9     1.63          0.2470
+## 10    1.26          0.2396
+## 11   65.08          0.2388
+## 12    7.94          0.2354
+## 13    4.34          0.2410
+## 14    1.04          0.2336
+## 15   16.59          0.2466
+## 16    2.31          0.2068
+## 17    1.65          0.2202
+## 18  143.79          0.2348
+## 19    4.13          0.2238
+## 20    2.34          0.2262
+## 21    8.51          0.2300
+## 22    1.63          0.2470
+## 23    1.26          0.2396
+## 24   65.08          0.2388
+## 25    7.94          0.2354
+## 26    4.34          0.2410
+## 27    1.04          0.2336
+## 28   16.59          0.2466
+## 29    2.31          0.2068
+## 30    1.65          0.2202
+## 31  143.79          0.2348
+## 32    4.13          0.2238
+## 33    2.34          0.2262
+## 34    8.51          0.2300
+## 35    1.63          0.2470
+## 36    1.26          0.2396
+## 37   65.08          0.2388
+## 38    7.94          0.2354
+## 39    4.34          0.2410
+## 40    1.04          0.2336
+## 41   16.59          0.2466
+## 42    2.31          0.2068
+## 43    1.65          0.2202
+## 44  143.79          0.2348
+## 45    4.13          0.2238
+## 46    2.34          0.2262
+## 47    8.51          0.2300
+## 48    1.63          0.2470
+## 49    1.26          0.2396
+## 50   65.08          0.2388
+## 51    7.94          0.2354
+## 52    4.34          0.2410
+## 53    3.74          0.2404
+## 54   20.38          0.2408
+## 55    4.83          0.2250
+## 56    4.02          0.2448
+## 57  147.64          0.2434
+## 58    5.78          0.2432
+## 59    4.56          0.2322
+## 60   11.19          0.2510
+## 61    3.89          0.2384
+## 62    3.59          0.2342
+## 63   61.67          0.2428
+## 64   11.47          0.2358
+## 65    6.64          0.2358
+## 66    3.74          0.2404
+## 67   20.38          0.2408
+## 68    4.83          0.2250
+## 69    4.02          0.2448
+## 70  147.64          0.2434
+## 71    5.78          0.2432
+## 72    4.56          0.2322
+## 73   11.19          0.2510
+## 74    3.89          0.2384
+## 75    3.59          0.2342
+## 76   61.67          0.2428
+## 77   11.47          0.2358
+## 78    6.64          0.2358
+## 79    3.74          0.2404
+## 80   20.38          0.2408
+## 81    4.83          0.2250
+## 82    4.02          0.2448
+## 83  147.64          0.2434
+## 84    5.78          0.2432
+## 85    4.56          0.2322
+## 86   11.19          0.2510
+## 87    3.89          0.2384
+## 88    3.59          0.2342
+## 89   61.67          0.2428
+## 90   11.47          0.2358
+## 91    6.64          0.2358
+## 92    3.74          0.2404
+## 93   20.38          0.2408
+## 94    4.83          0.2250
+## 95    4.02          0.2448
+## 96  147.64          0.2434
+## 97    5.78          0.2432
+## 98    4.56          0.2322
+## 99   11.19          0.2510
+## 100   3.89          0.2384
+## 101   3.59          0.2342
+## 102  61.67          0.2428
+## 103  11.47          0.2358
+## 104   6.64          0.2358
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 9062382b..3224f084 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -22,7 +22,10 @@ Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \description{ Converts \code{SSModel} object of \code{KFAS} package to general \code{bssm} model of type \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or -\code{ssm_mng}. +\code{ssm_mng}. As \code{KFAS} supports formula syntax for defining +e.g. regression and cyclic components it maybe sometimes easier to define +the model with \code{KFAS::SSModel} and then convert for the bssm style with +\code{as_bssm}. } \examples{ library("KFAS") diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index c72688ee..a8f18d05 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -85,6 +85,21 @@ local trend component, a seasonal component, and regression component (or subset of these components). } \examples{ +# Same data as in Vihola, Helske, Franks (2020) +data(poisson_series) +s <- sd(log(pmax(0.1, poisson_series))) +model <- bsm_ng(poisson_series, sd_level = uniform(0.115, 0, 2 * s), + sd_slope = uniform(0.004, 0, 2 * s), P1 = diag(0.1, 2), + distribution = "poisson") + +\donttest{ +out <- run_mcmc(model, iter = 1e5, particles = 10) +summary(out, variable = "theta", return_se = TRUE) +# should be about 0.093 and 0.016 +summary(out, variable = "states", return_se = TRUE)$Mean[c(1,100),1] +# should be about -0.075, 2.618 +} + model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", sd_level = halfnormal(0.01, 1), sd_seasonal = halfnormal(0.01, 1), diff --git a/man/figures/README-compare-1.png b/man/figures/README-compare-1.png index 6c996d488ee4536d8736d7a3338dc391de001f61..5e47df3b2cd3ce79b54a0d3e64722ba64ac626cc 100644 GIT binary patch literal 13137 zcmcJ$c|26_`#*lpnbp1xvMa?jS<0R)hf=OSXWM}SwF z7XSbOi@m!J004vn0QjAZ!=6|l&>&)$pNJLR9J>O50RVUd2H5v9;9UY#ztPgt!X6m_ z1_pq)fx%^i%h#GplmBUuY@M9^ZuPPOaCwcK_hoP3@@4PKC6_OEuI2?;=dD#u&U=@a_jC2>_ul6X z?@PRa5^sZ&)zzvtZuQvsyJYKktE-)t4LVD_J6B6OS66@L1^irH{r&s*X!D1;Q2*ub zj@1(H)pyBQC9G_96>GZI0;{;Xy2_%}T*7+Eb8_$DAOH}oUHgIcC)h^;KoPLmWlBGN zYowd+UZ?Jjbw@TOT{-%&N=l`BwB(MJd~=DNkGJZ5@OaoY=WEGwopYl4nY;)4|T%55Z=*9Cql2Pl-Crts!mLCA7{h%l0ndzN{CE{9LOJA9Kg zWtUJ{_j)wJQt2{X**Qs?B!K#k?WLairvlexZaPmJ|M#3+3)$FQZj*g@XoQZxDDuapN`0EGNV6z z#+3>=hX3D+B6!=>(|pa8*Q!PEn!|i&n6BEbz2Bp)@I{K`!A)9nf>j9QD2$)Fnob`4 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zQkB7G6CU>#e$uvjDdyK6o2B0)pQ^)__}tEhc1nM<1jbf?oqolCvA^-${Fwyfa|^$( zW Date: Wed, 17 Nov 2021 14:11:42 +0200 Subject: [PATCH 111/180] test that negbin works as in the paper --- tests/testthat/test_mcmc.R | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index b655603a..45037bf4 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -1,6 +1,35 @@ context("Test MCMC") tol <- 1e-8 +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5} Replicate Helske & Vihola (2021) +test_that("MCMC results from bssm paper are still correct", { + skip_on_cran() + + data(negbin_series) + bssm_model <- bsm_ng(negbin_series, + xreg = x, + beta = normal(0, 0, 10), + phi = halfnormal(1, 10), + sd_level = halfnormal(0.1, 1), + sd_slope = halfnormal(0.01, 0.1), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + distribution = "negative binomial") + + # run the MCMC + fit_bssm <- run_mcmc(bssm_model, iter = 6e4, burnin = 1e4, + particles = 10, seed = 1) + expect_error(sumr_theta <- summary(fit_bssm)[, "Mean"], NA) + paper_theta <- c(0.092, 0.003, 5.392, -0.912) + expect_equivalent(sumr_theta, paper_theta, tol = 0.01) + + expect_error(sumr_alpha <- summary(fit_bssm, variable = "states")$Mean[200,], + NA) + paper_alpha <- c(6.962,0.006) + expect_equivalent(sumr_alpha, paper_alpha, tol = 0.01) + +}) + + test_that("MCMC results for Gaussian model are correct", { set.seed(123) model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, From 24dbc389edec2bd5a092044afdd789a7f146ff11 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 15:58:39 +0200 Subject: [PATCH 112/180] more standards --- R/asymptotic_var.R | 2 + R/check_diagnostics.R | 6 +- R/models.R | 1 + R/priors.R | 2 + R/run_mcmc.R | 23 +++++++ R/srr-stats-standards.R | 67 +++++++++++--------- README.Rmd | 17 ++++- README.md | 80 ++++++++++++------------ man/figures/README-compare-1.png | Bin 13137 -> 13123 bytes man/figures/README-example-1.png | Bin 10976 -> 10993 bytes tests/testthat/test_approx.R | 12 +++- tests/testthat/test_basics.R | 2 + tests/testthat/test_is.R | 39 ++++++++++++ tests/testthat/test_models.R | 5 ++ tests/testthat/test_particle_smoother.R | 22 +++++++ tests/testthat/test_sim_smoother.R | 3 +- 16 files changed, 206 insertions(+), 75 deletions(-) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 30eff8fd..ec49558f 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -1,3 +1,5 @@ +#' @srrstats {BS1.5} + #' Integrated Autocorrelation Time #' #' Estimates the integrated autocorrelation time based on Sokal (1997). diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 69cdbc96..04e62684 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -1,13 +1,15 @@ +#' @srrstats {BS1.5} Several options for ESS. See also asymptotic_var.R + #' Quick Diagnostics Checks for \code{run_mcmc} Output #' #' Prints out the acceptance rate, smallest effective sample sizes (ESS) and -#' largest Rhat values for a quick first check that the sampling worked. +#' largest Rhat values for a quick first check that the sampling worked. For +#' further checks, see e.g. \code{bayesplot} and \code{coda} packages. #' #' For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior #' package are based on the approximate posterior which should look reasonable, #' otherwise the IS-correction does not make much sense. #' -#' #' @importFrom dplyr across #' @importFrom posterior summarise_draws default_convergence_measures #' @param x Results object of class \code{mcmc_output} from diff --git a/R/models.R b/R/models.R index 50373065..246cc825 100644 --- a/R/models.R +++ b/R/models.R @@ -4,6 +4,7 @@ #' @srrstats {G2.14, G2.14a, G2.14b, G2.14c} Missing observations are handled #' automatically as per SSM theory, whereas missing values are not allowed #' elsewhere. +#' @srrstats {BS1.0, BS1.1, BS1.2} #' ## placeholder functions for fixed models diff --git a/R/priors.R b/R/priors.R index b7442dbc..57d73f20 100644 --- a/R/priors.R +++ b/R/priors.R @@ -1,3 +1,5 @@ +#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6} Correct prior definitions. + #' Prior objects for bssm models #' #' These simple objects of class \code{bssm_prior} are used to construct a diff --git a/R/run_mcmc.R b/R/run_mcmc.R index d652d6ab..51c5e5ab 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -1,3 +1,11 @@ +#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} +#' @srrstats {BS2.6} +#' @srrstats {BS2.7, BS1.3a, BS2.8} Explained in docs. +#' @srrrstats {BS2.9} The argument 'seed' is set to random value if not +#' specified by the user. +#' @srrstatsTODO {BS2.12} +#' +#' #' Bayesian Inference of State Space Models #' #' Adaptive Markov chain Monte Carlo simulation for SSMs using @@ -17,6 +25,16 @@ #' instead of subsampling one of these as in case of #' \code{output_type = "full"}. #' +#' Initial values for the sampling are taken from the model object +#' (\code{model$theta]). If you want to continue from previous run, you can +#' reconstruct your original model by plugging in the previously obtained +#' parameters to \code{model$theta}, providing the S matrix for the RAM +#' algorithm and setting \code{burnin = 0}. See example. Note however, that +#' this is not identical as running all the iterations once, due to the +#' RNG "discontinuity" and because even without burnin bssm does include +#' "theta_0" i.e. the initial theta in the final chain (even with +#' \code{burnin=0}). +#' #' @importFrom stats tsp #' @param model Model of class \code{bssm_model}. #' @param iter Positive integer defining the total number of MCMC iterations. @@ -135,6 +153,11 @@ run_mcmc <- function(model, ...) { #' geom_line() + theme_bw() + #' geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), #' col = 2) +#' +#' # Continue from the previous run +#' model$theta[] <- mcmc_results$theta[nrow(mcmc_results$theta), ] +#' run_more <- run_mcmc(model, S = mcmc_results$S, iter = 1000, burnin = 0) +#' run_mcmc.gaussian <- function(model, iter, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index dc6e62e7..d5b4665c 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -77,20 +77,17 @@ #' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* #' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* +#' ## Tested by autotest and testthat tests +#' @srrstats {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* +#' @srrstats {G5.8a} *Zero-length data* +#' @srrstats {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* +#' @srrstats {G5.8c} *Data with all-`NA` fields or columns or all identical fields or columns* +#' @srrstats {G5.8d} *Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)* -#' @srrstatsTODO {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* -#' @srrstatsTODO {G5.8a} *Zero-length data* -#' @srrstatsTODO {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* -#' @srrstatsTODO {G5.8c} *Data with all-`NA` fields or columns or all identical fields or columns* -#' @srrstatsTODO {G5.8d} *Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)* -#' @srrstatsTODO {G5.9} **Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:* -#' @srrstatsTODO {G5.9a} *Adding trivial noise (for example, at the scale of `.Machine$double.eps`) to data does not meaningfully change results* -#' @srrstatsTODO {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results* -#' @srrstatsTODO {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* -#' @srrstatsTODO {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* -#' @srrstatsTODO {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* -#' @srrstatsTODO {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* - +#' ## Tested by autotest and testthat tests +#' @srrstats {G5.9} **Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:* +#' @srrstats {G5.9a} *Adding trivial noise (for example, at the scale of `.Machine$double.eps`) to data does not meaningfully change results* +#' @srrstats {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results* #' # General standards not applicable #' @@ -112,6 +109,12 @@ #' # No output is written to local files #' @srrstatsNA {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* +#' # Package does not contain extended tests (although benchmarks folder contains template for running such very time-consuming tests) +#' @srrstatsNA {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* +#' @srrstatsNA {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* +#' @srrstatsNA {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* +#' @srrstatsNA {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* + # Standards for Bayesian software @@ -121,23 +124,31 @@ #' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* #' @srrstats {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* #' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* +#' @srrstats {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* +#' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* + +#' ## Tested by autotest and package documentation +#' @srrstats {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* +#' @srrstats {BS2.1a} *The effects of such routines should be tested.* + +#' ## Handled by the prior construction functions +#' @srrstats {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* +#' @srrstats {BS2.3} *Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).* +#' @srrstats {BS2.4} *Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)* +#' @srrstats {BS2.5} *Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.* + +#' ## Checked, explained and done in run_mcmc +#' @srrstats {BS2.6} *Check that values for computational parameters lie within plausible ranges.* +#' @srrstats {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* +#' @srrstats {BS2.9} *Ensure each chain is started with a different seed by default.* #' # Bayesian standards not applicable -#' -#' # the bssm package does not implement convergence checkers, but relie -#' @srrstats {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* -#' @srrstatsTODO {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* -#' @srrstatsTODO {BS2.1a} *The effects of such routines should be tested.* -#' @srrstatsTODO {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* -#' @srrstatsTODO {BS2.3} *Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).* -#' @srrstatsTODO {BS2.4} *Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)* -#' @srrstatsTODO {BS2.5} *Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.* -#' @srrstatsTODO {BS2.6} *Check that values for computational parameters lie within plausible ranges.* -#' @srrstatsTODO {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* -#' @srrstatsTODO {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* -#' @srrstatsTODO {BS2.9} *Ensure each chain is started with a different seed by default.* -#' @srrstatsTODO {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* -#' @srrstatsTODO {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* + +#' ## Not applicable as only single-chain runs are supported. +#' @srrstatsNA {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* +#' ## Starting values are not accepted in this form. +#' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* + #' @srrstatsTODO {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* #' @srrstatsTODO {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* #' @srrstatsTODO {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* diff --git a/README.Rmd b/README.Rmd index 2304a116..19fe0a5c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -34,7 +34,17 @@ knitr::opts_chunk$set( #' algorithms work correctly as per Vihola, Helske, Franks (2020) #' (all simulations were implemented with the bssm package) and Helske #' and Vihola (2021). Full replication of the results would take days/weeks -#' (but see also bsm_ng and testthat tests). +#' (but see also bsm_ng, negbin_series and several testthat tests). +#' +#' @srrstats {G5.8, G5.8a, G5.8b, G5.8c, G5.8d} Tested with autotest and the +#' testthat tests. +#' @srrstats {G5.9, G5.9a, G5.9b} Tested with autotest and the testthat tests. +#' +#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} Addressed in the models.R, +#' run_mcmc.R, in vignettes and in the R Journal paper. +#' +#' +#' @srrstats {BS2.1, BS2.1a} Tested by autotest and package examples/tests. ``` # bssm @@ -108,7 +118,9 @@ xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, - beta = normal(rep(0, ncol(xreg)), 0, 1), + # Define priors, see ?bssm_prior + # Initial value followed by parameters of the prior distribution + beta = normal_prior(rep(0, ncol(xreg)), 0, 1), sd_y = gamma_prior(1, 2, 0.01), sd_level = gamma_prior(1, 2, 0.01), sd_slope = gamma_prior(1, 2, 0.01)) @@ -116,7 +128,6 @@ model <- bsm_lg(airquality$Ozone, fit <- run_mcmc(model, iter = 20000, burnin = 5000) fit - obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) diff --git a/README.md b/README.md index a8b4dbd5..0c5bc0aa 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,9 @@ xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, - beta = normal(rep(0, ncol(xreg)), 0, 1), + # Define priors, see ?bssm_prior + # Initial value followed by parameters of the prior distribution + beta = normal_prior(rep(0, ncol(xreg)), 0, 1), sd_y = gamma_prior(1, 2, 0.01), sd_level = gamma_prior(1, 2, 0.01), sd_slope = gamma_prior(1, 2, 0.01)) @@ -115,43 +117,43 @@ fit #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3479 +#> Length of the final jump chain = 3622 #> -#> Acceptance rate after the burn-in period: 0.232 +#> Acceptance rate after the burn-in period: 0.241 #> #> Summary for theta: #> -#> Mean SD SE -#> sd_y 20.9429622 1.9383343 0.068905655 -#> sd_level 6.2794066 2.7536555 0.100973147 -#> sd_slope 0.3345056 0.2623785 0.008440415 -#> Wind -2.5564842 0.5471567 0.019052430 -#> Temp 1.0258170 0.1961969 0.007047035 +#> Mean SD SE +#> sd_y 21.033665 1.8865681 0.07191763 +#> sd_level 6.041851 2.7451950 0.12967048 +#> sd_slope 0.338372 0.2893476 0.01040584 +#> Wind -2.561184 0.5606024 0.02134808 +#> Temp 1.042712 0.1983040 0.00610235 #> #> Effective sample sizes for theta: #> -#> ESS -#> sd_y 791.3118 -#> sd_level 743.7165 -#> sd_slope 966.3380 -#> Wind 824.7507 -#> Temp 775.1242 +#> ESS +#> sd_y 688.1358 +#> sd_level 448.1921 +#> sd_slope 773.1890 +#> Wind 689.5922 +#> Temp 1056.0116 #> #> Summary for alpha_154: #> -#> Mean SD SE -#> level -28.039885 19.409460 0.56324484 -#> slope -0.366811 1.689306 0.03873495 +#> Mean SD SE +#> level -29.3403551 20.055312 0.5656459 +#> slope -0.3705886 1.685349 0.0396166 #> #> Effective sample sizes for alpha_154: #> #> ESS -#> level 1187.497 -#> slope 1902.002 +#> level 1257.099 +#> slope 1809.778 #> #> Run time: #> user system elapsed -#> 0.89 0.02 0.91 +#> 0.89 0.03 0.90 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -188,43 +190,43 @@ fit2 #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3834 +#> Length of the final jump chain = 3859 #> -#> Acceptance rate after the burn-in period: 0.256 +#> Acceptance rate after the burn-in period: 0.257 #> #> Summary for theta: #> #> Mean SD SE SE-IS -#> sd_level 0.063067042 0.038284435 0.0016425712 8.029996e-04 -#> sd_slope 0.004092602 0.003530829 0.0001813586 7.127555e-05 -#> phi 3.986113336 0.531933924 0.0164752602 1.103394e-02 -#> Wind -0.056960598 0.015218618 0.0004459602 3.047884e-04 -#> Temp 0.052900914 0.008784547 0.0002490039 1.764499e-04 +#> sd_level 0.057953011 0.039698181 0.0033318587 8.550051e-04 +#> sd_slope 0.003985939 0.003219326 0.0001736902 6.579475e-05 +#> phi 3.993065302 0.526393116 0.0161096524 1.067217e-02 +#> Wind -0.057504005 0.015354436 0.0004738455 3.067793e-04 +#> Temp 0.052668318 0.008672590 0.0002509057 1.765270e-04 #> #> Effective sample sizes for theta: #> #> ESS -#> sd_level 543.2454 -#> sd_slope 379.0330 -#> phi 1042.4394 -#> Wind 1164.5504 -#> Temp 1244.5905 +#> sd_level 141.9607 +#> sd_slope 343.5415 +#> phi 1067.6971 +#> Wind 1050.0118 +#> Temp 1194.7490 #> #> Summary for alpha_154: #> -#> Mean SD SE SE-IS -#> level -0.210918261 0.73523171 0.0208711444 0.0150506204 -#> slope -0.002735397 0.02261717 0.0004652766 0.0004946287 +#> Mean SD SE SE-IS +#> level -0.193163677 0.7331771 0.0213018796 0.0152269256 +#> slope -0.003622763 0.0218063 0.0004642879 0.0004669465 #> #> Effective sample sizes for alpha_154: #> #> ESS -#> level 1240.955 -#> slope 2362.948 +#> level 1184.628 +#> slope 2205.918 #> #> Run time: #> user system elapsed -#> 10.81 0.14 10.79 +#> 10.75 0.12 10.71 ``` 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{G.5.4, G5.4a, G5.4b, G5.4c} Tests that the approximation +#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.9b} Tests that the approximation #' coincides with KFAS and in GLM case results coincide with the glm. context("Test Gaussian approximation") - test_that("Gaussian approximation results of bssm and KFAS coincide", { library(KFAS) set.seed(123) @@ -29,6 +28,10 @@ test_that("Gaussian approximation results of bssm and KFAS coincide", { expect_equivalent(KFS(approx_KFAS)$alphahat, alphahat) expect_equivalent(logLik(approx_KFAS), logLik(approx_bssm)) + model_bssm$initial_mode[] <- model_bssm$initial_mode + rnorm(10, sd = 0.1) + expect_equivalent(logLik(gaussian_approx(model_bssm)), + logLik(approx_bssm), tol = 0.001) + }) @@ -48,6 +51,11 @@ test_that("Gaussian approximation works for SV model", { expect_equivalent(c(-0.0999179077423753, -0.101594935319188, -0.0985572218431492, -0.103275329248674, -0.103028083292436), fast_smoother(approx_bssm)[1:5]) + + model_bssm2 <- model_bssm + model_bssm2$initial_mode[] <- model_bssm$initial_mode + rnorm(5, sd = 0.1) + expect_equivalent(logLik(gaussian_approx(model_bssm)), + logLik(gaussian_approx(model_bssm2)), tol = 0.001) }) test_that("results for poisson GLM are equal to glm function", { diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index 52a572ee..4ad6f3da 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -1,5 +1,7 @@ context("Test basics") +#' @srrstats {G5.4, G5.4b, G5.6, G5.6a, G5.6b, G5.7} Compare with KFAS. +#' test_that("results for Gaussian models are comparable to KFAS", { library("KFAS") model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(0.01^2, 0)), H = 2) diff --git a/tests/testthat/test_is.R b/tests/testthat/test_is.R index dab171d7..a2226e50 100644 --- a/tests/testthat/test_is.R +++ b/tests/testthat/test_is.R @@ -1,5 +1,43 @@ context("Test importance_sample") +#' @srrstats {G5.6, G5.6a, G5.6b, G5.7, G5.9b} Replicate Durbin&Koopman (1997) +#' +test_that("Test that bssm recovers the parameters of the Seatbelts model", { + + model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", + sd_level = 1, sd_seasonal = 1, xreg = Seatbelts[, "law"], + beta = normal(0, 0, 1)) + + obj <- function(theta) { + model$beta[1] <- theta[1] + model$R[1, 1, 1] <- theta[2] + model$R[2, 2, 1] <- theta[3] + -logLik(model, particles = 0) + } + + fit <- optim(c(0, 0, 0), obj, method = "L-BFGS-B", + lower = c(-Inf, 0, 0), upper = c(10, 10, 10)) + + DK1997 <- c(-0.278, 0.0245, 0) # From Durbin and Koopman (1997) + expect_equal(fit$par, DK1997, tol = 0.01) + + # fixed seed for smooth likelihood optimization (enough only for "spdk") + fixed_seed <- sample(1:1e6, size = 1) + # Same but with importance sampling + obj <- function(theta) { + model$beta[1] <- theta[1] + model$R[1, 1, 1] <- theta[2] + model$R[2, 2, 1] <- theta[3] + -logLik(model, particles = 10, method = "spdk", seed = fixed_seed) + } + + fit_is <- optim(c(0, 0, 0), obj, method = "L-BFGS-B", + lower = c(-Inf, 0, 0), upper = c(10, 10, 10)) + + # essentially identical results in this case + expect_equal(fit_is$par, DK1997, tol = 0.01) +}) + test_that("Test that poisson bsm_ng give identical results with ssm_ung", { expect_error(model_ssm_ung <- ssm_ung(y = 1:10, Z = matrix(c(1, 0), 2, 1), @@ -25,3 +63,4 @@ test_that("Test that svm still works", { expect_true(is.finite(sum(sim$states))) expect_true(is.finite(sum(sim$weights))) }) + diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index bc16b8da..a76eda5e 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -1,6 +1,9 @@ +#' @srrstats {G5.8, G5.8a, G5.8b, G5.8c, G5.8d, BS2.1, BS2.1a} + context("Test models") test_that("bad argument values for bsm throws an error", { + expect_error(bsm_lg(numeric(0), 1, 1)) expect_error(bsm_lg("character vector")) expect_error(bsm_lg(matrix(0, 2, 2))) expect_error(bsm_lg(1)) @@ -26,6 +29,7 @@ test_that("proper arguments for bsm don't throw an error", { test_that("bad argument values for bsm_ng throws an error", { + expect_error(bsm_ng(numeric(0), 1, 1, distribution = "poisson")) expect_error(bsm_ng("character vector", distribution = "poisson")) expect_error(bsm_ng(1:10, distribution = "poisson")) expect_error(bsm_ng(diag(2), distribution = "poisson", @@ -51,6 +55,7 @@ test_that("bad argument values for bsm_ng throws an error", { test_that("proper arguments for ng_bsm don't throw an error", { expect_error(bsm_ng(1:10, 1, 1, distribution = "poisson"), NA) + expect_error(bsm_ng(1:10, 1, 1, distribution = "POISSon"), NA) expect_error(bsm_ng(1:10, uniform(0, 0, 1), 1, distribution = "poisson"), NA) expect_error(bsm_ng(1:10, 1, uniform(0, 0, 1), distribution = "poisson"), NA) expect_error(bsm_ng(1:10, 1, 1, 1, period = 3, distribution = "poisson"), NA) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index cb986629..a7a18a9d 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -2,6 +2,28 @@ context("Test that particle smoothers work") +#' @srrstats {G5.9, G5.9a, G5.9b} + + +test_that("Test that trivial noise does not affect particle_smoother", { + + expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, + sd_y = 0.01, a1 = c(1, 0), P1 = diag(0.01, 2)), NA) + model_bsm2 <- model_bsm + model_bsm2$y <- model_bsm2$y + .Machine$double.eps + expect_equal( + particle_smoother(model_bsm, 1e5, seed = 1)$alphahat, + particle_smoother(model_bsm2, 1e5, seed = 1)$alphahat) +}) +test_that("Test that different seeds give comparable results", { + + expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, + sd_y = 0.01, a1 = c(1, 0), P1 = diag(0.01, 2)), NA) + expect_equal( + particle_smoother(model_bsm, 1e5)$alphahat, + particle_smoother(model_bsm, 1e5)$alphahat, tolerance = 0.001) +}) + test_that("Test that particle_smoother for LGSSM works as Kalman smoother", { expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, diff --git a/tests/testthat/test_sim_smoother.R b/tests/testthat/test_sim_smoother.R index 2e5767ed..43733cbd 100644 --- a/tests/testthat/test_sim_smoother.R +++ b/tests/testthat/test_sim_smoother.R @@ -1,4 +1,3 @@ - context("Test that simulation smoother work") @@ -24,3 +23,5 @@ test_that("sim_smoother for non-gaussian model works as Kalman smoother", { expect_equal(smoother(model)$alphahat, as.ts(apply(sims, 1:2, mean))) }) + + From 82a6ab63f4a80bcaf2ee507eb8e52a776e4c4fe0 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 16:56:53 +0200 Subject: [PATCH 113/180] docs --- R/asymptotic_var.R | 2 +- R/check_diagnostics.R | 2 +- R/priors.R | 2 +- R/run_mcmc.R | 2 +- README.Rmd | 1 + man/check_diagnostics.Rd | 3 ++- man/run_mcmc.Rd | 14 ++++++-------- 7 files changed, 13 insertions(+), 13 deletions(-) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index ec49558f..0363c0f0 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -1,5 +1,5 @@ #' @srrstats {BS1.5} - +NULL #' Integrated Autocorrelation Time #' #' Estimates the integrated autocorrelation time based on Sokal (1997). diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 04e62684..92c96a6b 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -1,5 +1,5 @@ #' @srrstats {BS1.5} Several options for ESS. See also asymptotic_var.R - +NULL #' Quick Diagnostics Checks for \code{run_mcmc} Output #' #' Prints out the acceptance rate, smallest effective sample sizes (ESS) and diff --git a/R/priors.R b/R/priors.R index 57d73f20..9a5b4c56 100644 --- a/R/priors.R +++ b/R/priors.R @@ -1,5 +1,5 @@ #' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6} Correct prior definitions. - +NULL #' Prior objects for bssm models #' #' These simple objects of class \code{bssm_prior} are used to construct a diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 51c5e5ab..98c21399 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -4,7 +4,7 @@ #' @srrrstats {BS2.9} The argument 'seed' is set to random value if not #' specified by the user. #' @srrstatsTODO {BS2.12} -#' +NULL #' #' Bayesian Inference of State Space Models #' diff --git a/README.Rmd b/README.Rmd index 19fe0a5c..c5a03f62 100644 --- a/README.Rmd +++ b/README.Rmd @@ -110,6 +110,7 @@ Consider the daily air quality measurements in New Your from May to September 19 library("bssm") library("dplyr") library("ggplot2") +set.seed(1) data("airquality", package = "datasets") diff --git a/man/check_diagnostics.Rd b/man/check_diagnostics.Rd index d36d910a..94d59e31 100644 --- a/man/check_diagnostics.Rd +++ b/man/check_diagnostics.Rd @@ -12,7 +12,8 @@ check_diagnostics(x) } \description{ Prints out the acceptance rate, smallest effective sample sizes (ESS) and -largest Rhat values for a quick first check that the sampling worked. +largest Rhat values for a quick first check that the sampling worked. For +further checks, see e.g. \code{bayesplot} and \code{coda} packages. } \details{ For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 40988796..6e54543c 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -191,14 +191,7 @@ arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and Vihola (2021) for details. } \details{ -For linear-Gaussian models, option \code{"summary"} does not simulate -states directly but computes the posterior means and variances of states -using fast Kalman smoothing. This is slightly faster, -more memory efficient and more accurate than calculations based on -simulation smoother. In other cases, the means and -covariances are computed using the full output of particle filter -instead of subsampling one of these as in case of -\code{output_type = "full"}. + } \examples{ model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), @@ -220,6 +213,11 @@ sumr \%>\% ggplot(aes(time, mean)) + geom_line() + theme_bw() + geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), col = 2) + +# Continue from the previous run +model$theta[] <- mcmc_results$theta[nrow(mcmc_results$theta), ] +run_more <- run_mcmc(model, S = mcmc_results$S, iter = 1000, burnin = 0) + set.seed(1) n <- 50 slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) From 6009425d3eb9cd2617539c2121b84b5259044f9f Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 17:47:23 +0200 Subject: [PATCH 114/180] fix missigness check --- R/check_arguments.R | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index 7dba4f6c..bd3bc57a 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -383,10 +383,17 @@ check_positive_real <- function(x, name) { } check_missingness <- function(x) { - contains_na <- anyNA(model[-which(names(x) %in% c("y", "prior_parameters"))], - recursive = TRUE) - if (contains_na) stop(paste( - "Missing values not allowed in the model object", - "(except in components 'y' and 'prior_parameters').")) - + if (is.null(x$prior_parameters)) { + contains_na <- anyNA(x[-which(names(x) == "y")], + recursive = TRUE) + if (contains_na) stop(paste( + "Missing values not allowed in the model object", + "(except in component 'y').")) + } else { + contains_na <- anyNA(x[-which(names(x) %in% c("y", "prior_parameters"))], + recursive = TRUE) + if (contains_na) stop(paste( + "Missing values not allowed in the model object", + "(except in components 'y' and 'prior_parameters').")) + } } \ No newline at end of file From 01db7b315cc2a2a10aa88044e26a74e0b83bd49d Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 20:29:36 +0200 Subject: [PATCH 115/180] don't overwrite D --- R/as_bssm.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/as_bssm.R b/R/as_bssm.R index e82fe1b7..55d7949a 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -11,7 +11,7 @@ #' @param kappa For \code{SSModel} object, a prior variance for initial state #' used to replace exact diffuse elements of the original model. #' @param ... Additional arguments to model building functions of \code{bssm} -#' (such as prior and updating functions). +#' (such as prior and updating functions, C, and D). #' @return Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or #' \code{ssm_mng}. #' @export @@ -41,9 +41,9 @@ as_bssm <- function(model, kappa = 100, ...) { if (dim(model$R)[2] > 1) { for (i in 1:dim(R)[3]) { L <- KFAS::ldl(model$Q[, , (i - 1) * tvq + 1]) - D <- sqrt(diag(diag(L))) + d <- sqrt(diag(diag(L))) diag(L) <- 1 - R[, , i] <- model$R[, , (i - 1) * tvr + 1] %*% L %*% D + R[, , i] <- model$R[, , (i - 1) * tvr + 1] %*% L %*% d } } else { R <- model$R * sqrt(c(model$Q)) @@ -140,9 +140,9 @@ as_bssm <- function(model, kappa = 100, ...) { H <- model$H for (i in 1:dim(H)[3]) { L <- KFAS::ldl(model$H[, , i]) - D <- sqrt(diag(diag(L))) + d <- sqrt(diag(diag(L))) diag(L) <- 1 - H[, , i] <- L %*% D + H[, , i] <- L %*% d } out <- ssm_mlg(y = model$y, Z = Z, H = H, T = model$T, R = R, From 0c7834e18505814a257cd3f70672d98ed690e599 Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 20:42:19 +0200 Subject: [PATCH 116/180] remove tolower --- R/cpp_example_models.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 7a6e5c09..294c8206 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -13,7 +13,7 @@ #' cpp_example_model <- function(example, return_code = FALSE) { - example <- match.arg(tolower(example), c("nlg_linear_gaussian", "nlg_sin_exp", + example <- match.arg(example, c("nlg_linear_gaussian", "nlg_sin_exp", "nlg_growth", "nlg_ar_exp", "sde_poisson_OU")) code <- switch(example, From a4d440ca867db3827ee2a33fb9e1276d1b5c74af Mon Sep 17 00:00:00 2001 From: helske Date: Wed, 17 Nov 2021 20:44:21 +0200 Subject: [PATCH 117/180] missingness, standards, docs --- R/bssm-package.R | 14 +- R/check_arguments.R | 27 +-- R/models.R | 24 +-- R/srr-stats-standards.R | 15 +- README.Rmd | 318 ++++++++++++++++++++----------- README.md | 250 +++++++++++++++++++----- man/figures/README-compare-1.png | Bin 13123 -> 13174 bytes man/figures/README-example-1.png | Bin 10993 -> 10984 bytes 8 files changed, 454 insertions(+), 194 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index d9d73477..b18a5f5e 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -2,7 +2,7 @@ #' Bayesian Inference of State Space Models #' #' This package contains functions for efficient Bayesian inference of state -#' space models, where model is assumed to be either +#' space models (SSMs), where model is assumed to be either #' #' * Exponential family state space models, where the state equation is linear #' Gaussian, and the conditional observation density is either Gaussian, @@ -14,12 +14,16 @@ #' #' * Model with continuous SDE dynamics. #' +#' Missing values in response series are allowed as per SSM theory and can be +#' automatically predicted, but there can be no missing values in the system +#' matrices of the model. +#' #' The \code{bssm} package includes several MCMC sampling and sequential Monte #' Carlo methods for models outside classic linear-Gaussian framework. For -#' definitions of the currently supported models and methods, as -#' well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF -#' algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and -#' the package vignettes and the R Journal paper. +#' definitions of the currently supported models and methods, usage of the +#' package as well as some theory behind the novel IS-MCMC and +#' \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, +#' Helske, Franks (2020), and the package vignettes. #' #' @references #' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and diff --git a/R/check_arguments.R b/R/check_arguments.R index bd3bc57a..b0bf6bf1 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -383,17 +383,20 @@ check_positive_real <- function(x, name) { } check_missingness <- function(x) { - if (is.null(x$prior_parameters)) { - contains_na <- anyNA(x[-which(names(x) == "y")], - recursive = TRUE) - if (contains_na) stop(paste( - "Missing values not allowed in the model object", - "(except in component 'y').")) - } else { - contains_na <- anyNA(x[-which(names(x) %in% c("y", "prior_parameters"))], - recursive = TRUE) - if (contains_na) stop(paste( - "Missing values not allowed in the model object", - "(except in components 'y' and 'prior_parameters').")) + if (!inherits(x, c("ssm_nlg", "ssm_sde"))) { + if (is.null(x$prior_parameters)) { + contains_na <- + anyNA(x[-which(names(x) %in% c("y", "update_fn", "prior_fn"))], + recursive = TRUE) + if (contains_na) stop(paste( + "Missing values not allowed in the model object", + "(except in component 'y').")) + } else { + contains_na <- anyNA(x[-which(names(x) %in% c("y", "prior_parameters"))], + recursive = TRUE) + if (contains_na) stop(paste( + "Missing values not allowed in the model object", + "(except in components 'y' and 'prior_parameters').")) + } } } \ No newline at end of file diff --git a/R/models.R b/R/models.R index 246cc825..5666d86c 100644 --- a/R/models.R +++ b/R/models.R @@ -1,7 +1,7 @@ #' @srrstats {G2.7, G2.8, G2.9} Only matrix/mts/arrays as tabular data are #' supported, not data.frame or similar objects. #' -#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c} Missing observations are handled +#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, BS3.0} Missing observations are handled #' automatically as per SSM theory, whereas missing values are not allowed #' elsewhere. #' @srrstats {BS1.0, BS1.1, BS1.2} @@ -125,25 +125,27 @@ default_update_fn <- function(theta) { #' # (for large model it is more efficient to do this #' # "manually" by constructing only necessary matrices, #' # i.e., in this case a list with H and Q) -#' -#' updatefn <- function(theta) { +#' +#' prior_fn <- function(theta) { +#' if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) +#' } +#' +#' update_fn <- function(theta) { #' #' model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ #' SSMseasonal(period = 12, #' sea.type = "trigonometric", Q = theta[2]^2) + #' log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) #' -#' as_bssm(model_kfas, kappa = 100) +#' # the bssm_model object is essentially list so this is fine +#' as_bssm(model_kfas, kappa = 100, init_theta = init_theta, +#' update_fn = update_fn, prior_fn = prior_fn) #' } #' -#' prior <- function(theta) { -#' if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) -#' } #' init_theta <- rep(1e-2, 3) -#' c("sd_level", "sd_seasonal", "sd_y") -#' model_bssm <- as_bssm(model_kfas, kappa = 100, -#' init_theta = init_theta, -#' prior_fn = prior, update_fn = updatefn) +#' names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y") +#' +#' model_bssm <- update_fn(init_theta) #' #' \donttest{ #' out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index d5b4665c..d184b1fd 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -142,6 +142,10 @@ #' @srrstats {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* #' @srrstats {BS2.9} *Ensure each chain is started with a different seed by default.* +#' As with the the general standards. +#' @srrstats {BS3.0} *Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (`NA`, `Inf`) values, and that such values, or entire rows including any such values, will be automatically removed from input data.* + + #' # Bayesian standards not applicable #' ## Not applicable as only single-chain runs are supported. @@ -149,11 +153,14 @@ #' ## Starting values are not accepted in this form. #' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* -#' @srrstatsTODO {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* -#' @srrstatsTODO {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* -#' @srrstatsTODO {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* +#' ## Of course applicable, but at the moment this is not done +#' @srrstatsNA {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* +#' @srrstatsNA {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* +#' @srrstatsNA {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* +#' +#' ## ? #' @srrstatsTODO {BS2.15} *Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.* -#' @srrstatsTODO {BS3.0} *Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (`NA`, `Inf`) values, and that such values, or entire rows including any such values, will be automatically removed from input data.* +#' #' @srrstatsTODO {BS3.1} *Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings* #' @srrstatsTODO {BS3.2} *Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms* #' @srrstatsTODO {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* diff --git a/README.Rmd b/README.Rmd index c5a03f62..f1f86e53 100644 --- a/README.Rmd +++ b/README.Rmd @@ -55,7 +55,7 @@ knitr::opts_chunk$set( [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) [![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) [![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) - + The `bssm` R package provides efficient methods for Bayesian inference of state @@ -65,18 +65,18 @@ Currently Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretely observed latent diffusion processes are supported. - + For details, see - - * [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), - * [Package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) - * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) - + +* [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), +* [Package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +* Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) + There are also couple posters and a talk related to IS-correction methodology and bssm package: - * [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) - * [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) - * [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) +* [UseR!2021 talk slides](https://jounihelske.netlify.app/talk/user2021/) +* [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) +* [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, and 311877. Current development is focused on @@ -97,14 +97,14 @@ And the development version from [GitHub](https://github.com/) with: devtools::install_github("helske/bssm") ``` Or from R-universe with - + ```{r, eval = FALSE} install.packages("bssm", repos = "https://helske.r-universe.dev") ``` ## Example -Consider the daily air quality measurements in New Your from May to September 1973, available in the `datasets` package. Let's try to predict the missing ozone levels by simple linear-Gaussian local linear trend model with temperature and wind as explanatory variables: +Consider the daily air quality measurements in New Your from May to September 1973, available in the `datasets` package. Let's try to predict the missing ozone levels by simple linear-Gaussian local linear trend model with temperature and wind as explanatory variables (missing response variables are handled naturally in the state space modelling framework, however no missing values in covariates are normally allowed); ```{r example} library("bssm") @@ -125,12 +125,12 @@ model <- bsm_lg(airquality$Ozone, sd_y = gamma_prior(1, 2, 0.01), sd_level = gamma_prior(1, 2, 0.01), sd_slope = gamma_prior(1, 2, 0.01)) - + fit <- run_mcmc(model, iter = 20000, burnin = 5000) fit obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) pred <- fitted(fit, model) pred %>% @@ -148,12 +148,12 @@ Same model but now assuming observations are from Gamma distribution: ```{r gamma-example} model2 <- bsm_ng(airquality$Ozone, - xreg = xreg, - beta = normal(rep(0, ncol(xreg)), 0, 1), - distribution = "gamma", - phi = gamma_prior(1, 2, 0.01), - sd_level = gamma_prior(1, 2, 0.1), - sd_slope = gamma_prior(1, 2, 0.1)) + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + distribution = "gamma", + phi = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.1), + sd_slope = gamma_prior(1, 2, 0.1)) fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) fit2 @@ -174,111 +174,199 @@ bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% ``` +Now let's assume that we also want to use the solar radiation variable as predictor for ozone. As it contains few missing values, we cannot use it directly. As the number of missing time points is very small, simple imputation would likely be acceptable, but let's consider more another approach. For simplicity, the slope terms of the previous models are now omitted, and we focus on the Gaussian case. Let $\mu_t$ be the true solar radiation at time $t$. Now for ozone $O_t$ we assume following model: +$$ +\begin{aligned} +O_t &= D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t,\\ +\alpha_{t+1} &= \alpha_t + \sigma_\eta\eta_t,\\ +\alpha_1 &\sim N(0, 100^2\textrm{I}), +\end{aligned} +$$ +where $D_t = \beta X_t$ contains regression terms related to wind and temperature, $\alpha_t$ is the time varying intercept term, and $\beta_S$ is the effect of solar radiation $\mu_t$. + +Now for the observed solar radiation $S_t$ we assume + +$$ +\begin{aligned} +S_t &= \mu_t\\ +\mu_{t+1} &= \mu_t + \sigma_\xi\xi_t,\\ +\mu_1 &\sim N(0, 100^2), +\end{aligned} +$$ +i.e. we assume as simple random walk for the $\mu$ which we observe without error or not at all (there is no error term in the observation equation $S_t=\mu_t$). + +We combine these two models as a bivariate Gaussian model with `ssm_mlg`: + +```{r missing-values} +# predictors (not including solar radiation) for ozone +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +# Function which outputs new model components given the parameter vector theta +update_fn <- function(theta) { + D <- rbind(t(xreg %*% theta[1:2]), 1) + Z <- matrix(c(1, 0, theta[3], 1), 2, 2) + R <- diag(exp(theta[4:5])) + H <- diag(c(exp(theta[6]), 0)) + # add third dimension so we have p x n x 1, p x m x 1, p x p x 1 arrays + dim(Z)[3] <- dim(R)[3] <- dim(H)[3] <- 1 + list(D = D, Z = Z, R = R, H = H) +} + +# Function for log-prior density +prior_fn <- function(theta) { + sum(dnorm(theta[1:3], 0, 10, log = TRUE)) + + sum(dgamma(exp(theta[4:6]), 2, 0.01, log = TRUE)) + + sum(theta[4:6]) # log-jacobian +} + +init_theta <- c(0, 0, 0, log(50), log(5), log(20)) +comps <- update_fn(init_theta) + +model <- ssm_mlg(y = cbind(Ozone = airquality$Ozone, Solar = airquality$Solar.R), + Z = comps$Z, D = comps$D, H = comps$H, T = diag(2), R = comps$R, + a1 = rep(0, 2), P1 = diag(100, 2), init_theta = init_theta, + state_names = c("alpha", "mu"), update_fn = update_fn, + prior_fn = prior_fn) + +fit <- run_mcmc(model, iter = 60000, burnin = 10000) +fit +``` + +Draw predictions: +```{r bivariate-fig} +pred <- fitted(fit, model) + +obs <- data.frame(Time = 1:nrow(airquality), + Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) + +pred %>% filter(Variable == "Solar") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Solar), colour = "Tomato") + + theme_bw() + + +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred %>% filter(Variable == "Ozone") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() +``` + ## Recent changes (For all changes, see NEWS file.) - + #### bssm 1.1.6 (Release date: 2021-09-06) - - * Cleaned codes and added more comprehensive tests in line with pkgcheck - tests. This resulted in finding and fixing multiple bugs: - * Fixed a bug in EKF-based particle filter which returned filtered estimates - also in place of one-step ahead predictions. - * Fixed a bug which caused an error in suggest_N for nlg_ssm. - * Fixed a bug which caused incorrect sampling of smoothing distribution for - ar1_lg model when predicting past or when using simulation smoother. - * Fixed a bug which caused an error when predicting past values in - multivariate time series case. - * Fixed sampling of negative binomial distribution in predict method, which - used std::negative_binomial which converts non-integer phi to integer. - Sampling now uses Gamma-Poisson mixture for simulation. - + +* Cleaned codes and added more comprehensive tests in line with pkgcheck +tests. This resulted in finding and fixing multiple bugs: +* Fixed a bug in EKF-based particle filter which returned filtered estimates +also in place of one-step ahead predictions. +* Fixed a bug which caused an error in suggest_N for nlg_ssm. +* Fixed a bug which caused incorrect sampling of smoothing distribution for +ar1_lg model when predicting past or when using simulation smoother. +* Fixed a bug which caused an error when predicting past values in +multivariate time series case. +* Fixed sampling of negative binomial distribution in predict method, which +used std::negative_binomial which converts non-integer phi to integer. +Sampling now uses Gamma-Poisson mixture for simulation. + #### bssm 1.1.4 (Release date: 2021-04-13) - - * Better documentation for SV model, and changed ordering of arguments to emphasise the - recommended parameterization. - * Fixed predict method for SV model. - + +* Better documentation for SV model, and changed ordering of arguments to emphasise the +recommended parameterization. +* Fixed predict method for SV model. + #### bssm 1.1.3-2 (Release date: 2021-02-24) - - * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. - * Added pandoc version >= 1.12.3 to system requirements. - + +* Fixed missing parenthesis causing compilation fail in case of no OpenMP support. +* Added pandoc version >= 1.12.3 to system requirements. + #### bssm 1.1.3-1 (Release date: 2021-02-22) - - * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. - * Added vignette for SDE models. - * Updated citation information and streamlined the main vignette. - + +* Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. +* Added vignette for SDE models. +* Updated citation information and streamlined the main vignette. + #### bssm 1.1.2 (Release date: 2021-02-08) - - * Some bug fixes, see NEWS for details. - + +* Some bug fixes, see NEWS for details. + #### bssm 1.1.0 (Release date: 2021-01-19) - - - * Added function `suggest_N` which can be used to choose - suitable number of particles for IS-MCMC. - * Added function `post_correct` which can be used to update - previous approximate MCMC with IS-weights. - * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. - * The adaptation of the proposal distribution now continues also after the burn-in by default. - * Changed default MCMC type to typically most efficient and robust IS2. - * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). - * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. - This resulted error within MCMC algorithms. - * Fixed a dimension drop bug in the predict method which caused error for univariate models. - * Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. + + +* Added function `suggest_N` which can be used to choose +suitable number of particles for IS-MCMC. +* Added function `post_correct` which can be used to update +previous approximate MCMC with IS-weights. +* Gamma priors are now supported in easy-to-use models such as `bsm_lg`. +* The adaptation of the proposal distribution now continues also after the burn-in by default. +* Changed default MCMC type to typically most efficient and robust IS2. +* Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). +* Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. +This resulted error within MCMC algorithms. +* Fixed a dimension drop bug in the predict method which caused error for univariate models. +* Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. #### bssm 1.0.1-1 (Release date: 2020-11-12) - - - * Added an argument `future` for predict method which allows - predictions for current time points by supplying the original model - (e.g., for posterior predictive checks). - At the same time the argument name `future_model` was changed to `model`. - * Fixed a bug in summary.mcmc_run which resulted error when - trying to obtain summary for states only. - * Added a check for Kalman filter for a degenerate case where all - observational level and state level variances are zero. - * Renamed argument `n_threads` to `threads` for consistency - with `iter` and `burnin` arguments. - * Improved documentation, added examples. - * Added a vignette regarding psi-APF for non-linear models. - + + +* Added an argument `future` for predict method which allows +predictions for current time points by supplying the original model +(e.g., for posterior predictive checks). +At the same time the argument name `future_model` was changed to `model`. +* Fixed a bug in summary.mcmc_run which resulted error when +trying to obtain summary for states only. +* Added a check for Kalman filter for a degenerate case where all +observational level and state level variances are zero. +* Renamed argument `n_threads` to `threads` for consistency +with `iter` and `burnin` arguments. +* Improved documentation, added examples. +* Added a vignette regarding psi-APF for non-linear models. + #### bssm 1.0.0 (Release date: 2020-06-09) - - Major update - - * Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely on C++ snippets). - * Added support for multivariate non-Gaussian models. - * Added support for gamma distributions. - * Added the function as.data.frame for mcmc output which converts the MCMC samples - to data.frame format for easier post-processing. - * Added truncated normal prior. - * Many argument names and model building functions have been changed for clarity and consistency. - * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. - * Allow zero as initial value for positive-constrained parameters of bsm models. - * Small changes to summary method which can now return also only summaries of the states. - * Fixed a bug in initializing run_mcmc for negative binomial model. - * Fixed a bug in phi-APF for non-linear models. - * Reimplemented predict method which now always produces data frame of samples. - + +Major update + +* Major changes for model definitions, now model updating and priors +can be defined via R functions (non-linear and SDE models still rely on C++ snippets). +* Added support for multivariate non-Gaussian models. +* Added support for gamma distributions. +* Added the function as.data.frame for mcmc output which converts the MCMC samples +to data.frame format for easier post-processing. +* Added truncated normal prior. +* Many argument names and model building functions have been changed for clarity and consistency. +* Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. +* Allow zero as initial value for positive-constrained parameters of bsm models. +* Small changes to summary method which can now return also only summaries of the states. +* Fixed a bug in initializing run_mcmc for negative binomial model. +* Fixed a bug in phi-APF for non-linear models. +* Reimplemented predict method which now always produces data frame of samples. + #### bssm 0.1.11 (Release date: 2020-02-25) - - * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, - as it seems to work better with noisy likelihood estimates. - * Print and summary methods for MCMC output are now coherent in their output. - + +* Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, +as it seems to work better with noisy likelihood estimates. +* Print and summary methods for MCMC output are now coherent in their output. + #### bssm 0.1.10 (Release date: 2020-02-04) - - * Fixed missing weight update for IS-SPDK without OPENMP flag. - * Removed unused usage argument ... from expand_sample. - + +* Fixed missing weight update for IS-SPDK without OPENMP flag. +* Removed unused usage argument ... from expand_sample. + #### bssm 0.1.9 (Release date: 2020-01-27) - - * Fixed state sampling for PM-MCMC with SPDK. - * Added ts attribute for svm model. - * Corrected asymptotic variance for summary methods. - - For older versions, see NEWS file. - + +* Fixed state sampling for PM-MCMC with SPDK. +* Added ts attribute for svm model. +* Corrected asymptotic variance for summary methods. + +For older versions, see NEWS file. + diff --git a/README.md b/README.md index 0c5bc0aa..18819082 100644 --- a/README.md +++ b/README.md @@ -75,7 +75,10 @@ install.packages("bssm", repos = "https://helske.r-universe.dev") Consider the daily air quality measurements in New Your from May to September 1973, available in the `datasets` package. Let’s try to predict the missing ozone levels by simple linear-Gaussian local linear -trend model with temperature and wind as explanatory variables: +trend model with temperature and wind as explanatory variables (missing +response variables are handled naturally in the state space modelling +framework, however no missing values in covariates are normally +allowed); ``` r library("bssm") @@ -94,6 +97,7 @@ library("dplyr") #> #> intersect, setdiff, setequal, union library("ggplot2") +set.seed(1) data("airquality", package = "datasets") @@ -108,7 +112,7 @@ model <- bsm_lg(airquality$Ozone, sd_y = gamma_prior(1, 2, 0.01), sd_level = gamma_prior(1, 2, 0.01), sd_slope = gamma_prior(1, 2, 0.01)) - + fit <- run_mcmc(model, iter = 20000, burnin = 5000) fit #> @@ -117,45 +121,45 @@ fit #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3622 +#> Length of the final jump chain = 3593 #> -#> Acceptance rate after the burn-in period: 0.241 +#> Acceptance rate after the burn-in period: 0.239 #> #> Summary for theta: #> -#> Mean SD SE -#> sd_y 21.033665 1.8865681 0.07191763 -#> sd_level 6.041851 2.7451950 0.12967048 -#> sd_slope 0.338372 0.2893476 0.01040584 -#> Wind -2.561184 0.5606024 0.02134808 -#> Temp 1.042712 0.1983040 0.00610235 +#> Mean SD SE +#> sd_y 20.8618647 1.9369381 0.068145131 +#> sd_level 6.3731836 2.8013937 0.113153715 +#> sd_slope 0.3388712 0.2833955 0.010355574 +#> Wind -2.5183269 0.5764833 0.020978488 +#> Temp 1.0265846 0.2064343 0.007497538 #> #> Effective sample sizes for theta: #> -#> ESS -#> sd_y 688.1358 -#> sd_level 448.1921 -#> sd_slope 773.1890 -#> Wind 689.5922 -#> Temp 1056.0116 +#> ESS +#> sd_y 807.9079 +#> sd_level 612.9297 +#> sd_slope 748.9238 +#> Wind 755.1359 +#> Temp 758.1001 #> #> Summary for alpha_154: #> -#> Mean SD SE -#> level -29.3403551 20.055312 0.5656459 -#> slope -0.3705886 1.685349 0.0396166 +#> Mean SD SE +#> level -28.3163054 20.132341 0.69650977 +#> slope -0.3740463 1.685733 0.03683278 #> #> Effective sample sizes for alpha_154: #> -#> ESS -#> level 1257.099 -#> slope 1809.778 +#> ESS +#> level 835.4763 +#> slope 2094.6363 #> #> Run time: #> user system elapsed -#> 0.89 0.03 0.90 +#> 0.89 0.01 0.90 obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) pred <- fitted(fit, model) pred %>% @@ -174,12 +178,12 @@ Same model but now assuming observations are from Gamma distribution: ``` r model2 <- bsm_ng(airquality$Ozone, - xreg = xreg, - beta = normal(rep(0, ncol(xreg)), 0, 1), - distribution = "gamma", - phi = gamma_prior(1, 2, 0.01), - sd_level = gamma_prior(1, 2, 0.1), - sd_slope = gamma_prior(1, 2, 0.1)) + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + distribution = "gamma", + phi = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.1), + sd_slope = gamma_prior(1, 2, 0.1)) fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) fit2 @@ -190,43 +194,43 @@ fit2 #> #> Iterations = 5001:20000 #> Thinning interval = 1 -#> Length of the final jump chain = 3859 +#> Length of the final jump chain = 3858 #> #> Acceptance rate after the burn-in period: 0.257 #> #> Summary for theta: #> #> Mean SD SE SE-IS -#> sd_level 0.057953011 0.039698181 0.0033318587 8.550051e-04 -#> sd_slope 0.003985939 0.003219326 0.0001736902 6.579475e-05 -#> phi 3.993065302 0.526393116 0.0161096524 1.067217e-02 -#> Wind -0.057504005 0.015354436 0.0004738455 3.067793e-04 -#> Temp 0.052668318 0.008672590 0.0002509057 1.765270e-04 +#> sd_level 0.057158663 0.035366227 0.0019652211 7.416778e-04 +#> sd_slope 0.003894013 0.003654978 0.0001818441 7.567405e-05 +#> phi 4.006977632 0.536273508 0.0157319790 1.071056e-02 +#> Wind -0.057351094 0.015411504 0.0004389087 3.051982e-04 +#> Temp 0.052808820 0.008701489 0.0002478631 1.716846e-04 #> #> Effective sample sizes for theta: #> #> ESS -#> sd_level 141.9607 -#> sd_slope 343.5415 -#> phi 1067.6971 -#> Wind 1050.0118 -#> Temp 1194.7490 +#> sd_level 323.8580 +#> sd_slope 403.9906 +#> phi 1161.9996 +#> Wind 1232.9392 +#> Temp 1232.4328 #> #> Summary for alpha_154: #> -#> Mean SD SE SE-IS -#> level -0.193163677 0.7331771 0.0213018796 0.0152269256 -#> slope -0.003622763 0.0218063 0.0004642879 0.0004669465 +#> Mean SD SE SE-IS +#> level -0.200656509 0.73134471 0.0205560413 0.0145184378 +#> slope -0.002689176 0.02289051 0.0005131012 0.0005050788 #> #> Effective sample sizes for alpha_154: #> #> ESS -#> level 1184.628 -#> slope 2205.918 +#> level 1265.801 +#> slope 1990.238 #> #> Run time: #> user system elapsed -#> 10.75 0.12 10.71 +#> 10.75 0.04 10.77 ``` Comparison: @@ -246,6 +250,158 @@ bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% +Now let’s assume that we also want to use the solar radiation variable +as predictor for ozone. As it contains few missing values, we cannot use +it directly. As the number of missing time points is very small, simple +imputation would likely be acceptable, but let’s consider more another +approach. For simplicity, the slope terms of the previous models are now +omitted, and we focus on the Gaussian case. Let *μ**t* be the +true solar radiation at time *t*. Now for ozone *O**t* we +assume following model: +$$ +\\begin{aligned} +O\_t &= D\_t + \\alpha\_t + \\beta\_S \\mu\_t + \\sigma\_\\epsilon \\epsilon\_t,\\\\ +\\alpha\_{t+1} &= \\alpha\_t + \\sigma\_\\eta\\eta\_t,\\\\ +\\alpha\_1 &\\sim N(0, 100^2\\textrm{I}), +\\end{aligned} +$$ +where *D**t* = *β**X**t* contains regression terms +related to wind and temperature, *α**t* is the time varying +intercept term, and *β**S* is the effect of solar radiation +*μ**t*. + +Now for the observed solar radiation *S**t* we assume + +$$ +\\begin{aligned} +S\_t &= \\mu\_t\\\\ +\\mu\_{t+1} &= \\mu\_t + \\sigma\_\\xi\\xi\_t,\\\\ +\\mu\_1 &\\sim N(0, 100^2), +\\end{aligned} +$$ +i.e. we assume as simple random walk for the *μ* which we observe +without error or not at all (there is no error term in the observation +equation *S**t* = *μ**t*). + +We combine these two models as a bivariate Gaussian model with +`ssm_mlg`: + +``` r +# predictors (not including solar radiation) for ozone +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +# Function which outputs new model components given the parameter vector theta +update_fn <- function(theta) { + D <- rbind(t(xreg %*% theta[1:2]), 1) + Z <- matrix(c(1, 0, theta[3], 1), 2, 2) + R <- diag(exp(theta[4:5])) + H <- diag(c(exp(theta[6]), 0)) + # add third dimension so we have p x n x 1, p x m x 1, p x p x 1 arrays + dim(Z)[3] <- dim(R)[3] <- dim(H)[3] <- 1 + list(D = D, Z = Z, R = R, H = H) +} + +# Function for log-prior density +prior_fn <- function(theta) { + sum(dnorm(theta[1:3], 0, 10, log = TRUE)) + + sum(dgamma(exp(theta[4:6]), 2, 0.01, log = TRUE)) + + sum(theta[4:6]) # log-jacobian +} + +init_theta <- c(0, 0, 0, log(50), log(5), log(20)) +comps <- update_fn(init_theta) + +model <- ssm_mlg(y = cbind(Ozone = airquality$Ozone, Solar = airquality$Solar.R), + Z = comps$Z, D = comps$D, H = comps$H, T = diag(2), R = comps$R, + a1 = rep(0, 2), P1 = diag(100, 2), init_theta = init_theta, + state_names = c("alpha", "mu"), update_fn = update_fn, + prior_fn = prior_fn) + +fit <- run_mcmc(model, iter = 60000, burnin = 10000) +fit +#> +#> Call: +#> run_mcmc.gaussian(model = model, iter = 60000, burnin = 10000) +#> +#> Iterations = 10001:60000 +#> Thinning interval = 1 +#> Length of the final jump chain = 12234 +#> +#> Acceptance rate after the burn-in period: 0.245 +#> +#> Summary for theta: +#> +#> Mean SD SE +#> theta_1 -3.89121114 0.58715113 0.0233827004 +#> theta_2 0.98712126 0.18819758 0.0051506907 +#> theta_3 0.06324657 0.02417334 0.0004672314 +#> theta_4 0.82577262 0.67134723 0.0165661049 +#> theta_5 4.75567622 0.05858454 0.0010887250 +#> theta_6 3.05462451 0.07640392 0.0014803971 +#> +#> Effective sample sizes for theta: +#> +#> ESS +#> theta_1 630.5368 +#> theta_2 1335.0487 +#> theta_3 2676.7595 +#> theta_4 1642.3041 +#> theta_5 2895.5411 +#> theta_6 2663.6361 +#> +#> Summary for alpha_154: +#> +#> Mean SD SE +#> alpha -16.44435 14.99708 0.3659912 +#> mu 223.60490 116.49063 1.3409568 +#> +#> Effective sample sizes for alpha_154: +#> +#> ESS +#> alpha 1679.082 +#> mu 7546.619 +#> +#> Run time: +#> user system elapsed +#> 12.02 0.13 12.07 +``` + +Draw predictions: + +``` r +pred <- fitted(fit, model) + +obs <- data.frame(Time = 1:nrow(airquality), + Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) + +pred %>% filter(Variable == "Solar") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Solar), colour = "Tomato") + + theme_bw() +``` + + + +``` r +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred %>% filter(Variable == "Ozone") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() +``` + + + ## Recent changes (For all changes, see NEWS file.) #### bssm 1.1.6 (Release date: 2021-09-06) diff --git a/man/figures/README-compare-1.png b/man/figures/README-compare-1.png index 590b618ddd36caaa495c8078c602d810a62550be..cb8797e8fb757538d19194b4d946470b9c06d44f 100644 GIT binary patch literal 13174 zcmb_@2|QHa|M#6cV`eO4-?A@Z63H5q<&QlT;_D&2~Vk+h&9 zDbX^LDT5*^vW<|eS)beY_kW((e|w(S^Lk#-yvFO8bI<2<&gXMJ=W{;ibKW-%cHcx1 zQxO9Ifa1J)y%zw0C;;HTk?~lKo0ka@D}KPP^bJ@E05kvy0chCw91v0g)YO@qn`2cp zfJOsCXtZ;*bJ(}T?e8bT%`L+%qS!6tO~}8|goONUbS?z@R8)jibc9qaMYu&oxMf5{ z6h{>Q$oPjK!!0A@&C)p7(whvd5%z3p2_yYWfi+xOT0#t8oW-ORJ+RsD5CBNN{QHM{9m&}X09t_adPn+^ ztHYh*w>qq{m3Qf-pWj>fY?(plNJXBj#`_BI&=8{n-2TIIJJ-3Zb<8X+6%r2oS`LBB z^_+g#q?JZbT7z##j{#&a5`ugD!APYnrZWoLFiDh6Dop0Z4JjEaS*wCNfc2efB`0Q|1IJBJeZIx`hR9v98A;w%eWY0 ztcO5bF~(ak#@V7sHpbW(V~jEI$3`SejxNVWv|M_CiH*ovi{FBc=)%7vjGju>`77mr zuK!;QT^0(0dt+5a${4arFB)K`7Iv7UxMz)msV~29@HT+ zJ7vE<3S-<9-C^EbIm1E67?69?D`+7`r`F|+NsL})eIq#8wsnqvB)k`xxwfNs%xp);5&FJf~f0tT$aR4GQN^u4XYALkc0;1S>TXL9^k$p5SMKd^JjdEXCF 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zu3J$fF2prxbBD!6{m_d@H-RS#at-5cFX5r-1H9`Vi0Yyy^#rgNNdA6_wn}_N+8MKQ z2g9GbZd9DlNd!x2M$#@~fiKJe46h)`4?y&`xVnnsqaALo$WG?xn|Rm9?5mIppnuU- zj(^yhb-)0hY^{2<97ip`ymjtVrN~E8vqP>f93A$I;w3^CkJR*2Lel3i%L3EX-h;pw z`eml2!^!?JTi}wp&nXo<8(oLnj@EM%Aw09B*ON|9hbl{scGXVPS`|m)ws>rWJ zw^v=e?uDQQgeH4R!(NU_P^G3jmE9k;4i9TyiCt+k!owDRItKw)BM;ir8oKiV4_aLx z%L6CL9|831;%w6C4{oy1jQa=$J+pegERcw>j>B4w^S2M^ z3c+S&F~Btt*L-Q<6d&OCZ0AK4{RfW~LgWH$Bw6CHi`bYJH@BY%u<77|d{Sny(4+vi z+V)HCMIi8LsT3cMaRaC3QO3=P-32jvAz!S@8O%~AJ`gFCJ}eA4owcN+`#akenmIGE zZ3G9`Pa&ay<(zF&T(#lehAeLManJ-8ZLiNxH0^kW^2AHDH2XZ@SFq<|tc97@qBz#g zQ621?-u#8hS=+-Y!|UPtOUToc6O2X>tvQ2eN#HsE2+&kMa6J_GzG-lo7f6_?=qtkj>@BzFN%=;FS5h8Y`Ck_n`rll zZ#&}D6F@XQKvUw(wq}bpS1JvD6Qi_deTS~>ss{FDG6)r#v75+%Qs*=CK3PW)k*lIp z1o{mlQUJ5L<6y2R_|bW5NVnXwPYW-X!C*q0^yCIlcyHwzC!Gh2KC z$I&zH!sUL~tRGl3(*@WDJYTteRMHYnQM|E-@a84a?X7o-W^10Bgs5Xt*c zCa)L-=KTFSt%H0D>l7c#t++-|j*-G8_=&(~v)wf;UbwdmbYSkjR#XTUpAvxs;WZCBXszuBM`O%+X+#7A$S?;6v_qn2#Qq^o@2mV3_|n(u zq;?p5^i&7>Qh$-p3sSa`3B#3?-$AWtz<}Y|^*{``O>XDMbubhJl%I9NJ&y|3-A} zLB;<O_6bgu}#u-_-|)RIsRO5N`;zQ|?EMOj>%u`}t609&e1-SZ*rH^FsVjncE^kjUgaK*7}p$6&bdp#Nk9 zUJhL{uV1VRTm8+cVneDNHc@0@oq~p|9EFgrDf3{ vDm!2z-=T`GvqD Date: Thu, 18 Nov 2021 20:29:09 +0200 Subject: [PATCH 118/180] added examples and improved docs, fixed standards --- NAMESPACE | 3 + R/asymptotic_var.R | 10 +- R/bssm-package.R | 1 - R/check_arguments.R | 85 +++++++-- R/check_diagnostics.R | 5 +- R/cpp_example_models.R | 82 ++++++++- R/kfilter.R | 3 +- R/models.R | 21 ++- R/predict.R | 9 +- R/print_mcmc.R | 2 + R/priors.R | 3 +- R/run_mcmc.R | 23 +-- R/srr-stats-standards.R | 218 +++++++++++++----------- README.Rmd | 3 +- README.md | 6 +- benchmarks/replications.Rmd | 12 +- man/as_bssm.Rd | 2 +- man/bssm.Rd | 14 +- man/check.Rd | 112 ++++++++++++ man/figures/README-bivariate-fig-1.png | Bin 0 -> 8839 bytes man/figures/README-bivariate-fig-2.png | Bin 0 -> 10819 bytes man/kfilter.Rd | 3 +- man/predict.mcmc_output.Rd | 9 +- man/run_mcmc.Rd | 22 ++- man/ssm_nlg.Rd | 2 +- man/ssm_sde.Rd | 3 +- man/ssm_ulg.Rd | 20 ++- tests/testthat/test_approx.R | 2 +- tests/testthat/test_as_bssm.R | 25 +++ tests/testthat/test_basics.R | 10 +- tests/testthat/test_is.R | 4 +- tests/testthat/test_mcmc.R | 75 +++++++- tests/testthat/test_particle_smoother.R | 2 - tests/testthat/test_sde.R | 95 ++--------- vignettes/bssm.Rmd | 4 +- vignettes/bssm.bib | 7 + vignettes/growth_model.Rmd | 22 +-- vignettes/sde_model.Rmd | 7 +- 38 files changed, 631 insertions(+), 295 deletions(-) create mode 100644 man/check.Rd create mode 100644 man/figures/README-bivariate-fig-1.png create mode 100644 man/figures/README-bivariate-fig-2.png diff --git a/NAMESPACE b/NAMESPACE index b130ec56..b302d676 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -79,6 +79,7 @@ export(ukf) export(uniform) export(uniform_prior) importFrom(Rcpp,evalCpp) +importFrom(checkmate,test_atomic_vector) importFrom(checkmate,test_count) importFrom(checkmate,test_double) importFrom(checkmate,test_flag) @@ -99,6 +100,8 @@ importFrom(magrittr,"%>%") importFrom(posterior,as_draws) importFrom(posterior,as_draws_df) importFrom(posterior,default_convergence_measures) +importFrom(posterior,ess_basic) +importFrom(posterior,ess_bulk) importFrom(posterior,summarise_draws) importFrom(stats,"tsp<-") importFrom(stats,as.ts) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 0363c0f0..3a7ca7b9 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -1,5 +1,3 @@ -#' @srrstats {BS1.5} -NULL #' Integrated Autocorrelation Time #' #' Estimates the integrated autocorrelation time based on Sokal (1997). @@ -12,6 +10,7 @@ NULL #' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. #' https://doi.org/10.1007/978-1-4899-0319-8_6 #' @export +#' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) #' x <- numeric(1e4) @@ -40,7 +39,8 @@ iact <- function(x) { #' \code{posterior} package, or the improved \code{ess_bulk} method of the same #' package (these compute ESS instead of variances, but #' MCSE^2 = var(x) / ESS = var(x) * IACT / length(x)). -#' +#' +#' @importFrom posterior ess_basic ess_bulk #' @param x Vector of samples. #' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is #' assumed). @@ -61,6 +61,7 @@ iact <- function(x) { #' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. #' https://doi.org/10.1214/20-BA1221 #' @export +#' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) #' x <- numeric(1e4) @@ -77,6 +78,9 @@ asymptotic_var <- function(x, w, method = "sokal") { if (missing(w)) w <- rep(1, length(x)) if(any(w < 0) | any(!is.finite(w))) stop("Nonfinite or negative weights in 'w'.") + if (!any(w > 0)) { + stop("No positive weights in 'w'.") + } estimate_c <- mean(w) estimate_mean <- weighted_mean(x, w) z <- w * (x - estimate_mean) diff --git a/R/bssm-package.R b/R/bssm-package.R index b18a5f5e..d27b578f 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -41,7 +41,6 @@ #' @importFrom coda mcmc #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit -#' @importFrom checkmate test_count test_double test_flag test_integerish test_int #' @useDynLib bssm NULL #' Deaths by drowning in Finland in 1969-2019 diff --git a/R/check_arguments.R b/R/check_arguments.R index b0bf6bf1..e5779d0f 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -1,4 +1,23 @@ - +#' Check Arguments +#' +#' @importFrom checkmate test_atomic_vector test_count test_double test_flag +#' test_integerish test_int +#' +#' @param name Name of the argument used in printing error messages. +#' @param positive Logical, check for positiveness of \code{x}. +#' @param max Maximum value of \code{x}. +#' @param p Integer, number of time series. +#' @param n Integer, number of time points. +#' @param m Integer, dimensionality of the state vector. +#' @param k Integer, number of predictors. +#' @param multivariate Logical, should \code{p} be larger than 1? +#' @param beta Vector of regression coefficients. +#' @param xreg Matrix or vector of predictors. +#' @param distribution Distribution(s) of the responses. +#' @param y The response time series. +#' @param type Name to be added to the sd parameter name. +#' @param add_prefix Logical, add \code{type} to parameter name. +#' @rdname check check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(!is.na(x))) { if (multivariate) { @@ -43,6 +62,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } x } +#' @rdname check check_period <- function(x, n) { if (!test_int(x)) { stop("Argument 'period' should be a single integer. ") @@ -56,7 +76,7 @@ check_period <- function(x, n) { } x } - +#' @rdname check check_distribution <- function(x, distribution) { for (i in seq_len(ncol(x))) { if (distribution[i] != "gaussian" && any(na.omit(x[, i]) < 0)) { @@ -72,6 +92,9 @@ check_distribution <- function(x, distribution) { } } } + + +#' @rdname check check_sd <- function(x, type, add_prefix = TRUE) { if (add_prefix) { @@ -93,7 +116,7 @@ check_sd <- function(x, type, add_prefix = TRUE) { } } - +#' @rdname check check_xreg <- function(x, n) { if (!(nrow(x) %in% c(0, n))) { @@ -105,6 +128,7 @@ check_xreg <- function(x, n) { } +#' @rdname check check_beta <- function(x, k) { if(!is.numeric(x)) stop("'beta' must be numeric. ") if (length(x) != k) { @@ -116,7 +140,7 @@ check_beta <- function(x, k) { } } - +#' @rdname check check_mu <- function(x) { if (length(x) != 1) { @@ -127,6 +151,7 @@ check_mu <- function(x) { } } +#' @rdname check check_rho <- function(x) { if (length(x) != 1) { @@ -137,11 +162,14 @@ check_rho <- function(x) { } } -check_phi <- function(x, distribution) { + +#' @rdname check +check_phi <- function(x) { if (x < 0) { stop("Parameter 'phi' must be non-negative.") } } +#' @rdname check check_u <- function(x, y, multivariate = FALSE) { if (any(x < 0)) { stop("All values of 'u' must be non-negative.") @@ -168,19 +196,19 @@ check_u <- function(x, y, multivariate = FALSE) { } x } - +#' @rdname check check_prior <- function(x, name) { if (!is_prior(x) && !is_prior_list(x)) { stop(paste(name, "must be of class 'bssm_prior' or 'bssm_prior_list'.")) } } - +#' @rdname check check_prop <- function(x, name = "target") { if (length(x) > 1 || x >= 1 || x <= 0) { stop(paste0("Argument '", name, "' must be on interval (0, 1).")) } } - +#' @rdname check check_D <- function(x, p, n) { if (missing(x) || is.null(x)) { x <- if (p == 1) 0 else matrix(0, p, 1) @@ -201,7 +229,7 @@ check_D <- function(x, p, n) { } x } - +#' @rdname check check_C <- function(x, m, n) { if (missing(x) || is.null(x)) { x <- matrix(0, m, 1) @@ -215,6 +243,10 @@ check_C <- function(x, m, n) { x } + + + +#' @rdname check create_regression <- function(beta, xreg, n) { if (missing(xreg) || is.null(xreg)) { list(xreg = matrix(0, 0, 0), coefs = numeric(0), beta = NULL) @@ -226,8 +258,12 @@ create_regression <- function(beta, xreg, n) { stop(paste("Prior for beta must be of class 'bssm_prior' or", "'bssm_prior_list.", sep = " " )) } else { - if (is.null(dim(xreg)) && length(xreg) == n) { - dim(xreg) <- c(n, 1) + if (is.null(dim(xreg))) { + if (length(xreg) == n) { + dim(xreg) <- c(n, 1) + } else { + stop("Length of xreg is not equal to the length of the series y.") + } } check_xreg(xreg, n) nx <- ncol(xreg) @@ -247,7 +283,7 @@ create_regression <- function(beta, xreg, n) { list(xreg = xreg, coefs = coefs, beta = beta) } } - +#' @rdname check check_Z <- function(x, p, n, multivariate = FALSE) { if(!is.numeric(x)) stop("'Z' must be numeric. ") if (!multivariate) { @@ -278,7 +314,7 @@ check_Z <- function(x, p, n, multivariate = FALSE) { } x } - +#' @rdname check check_T <- function(x, m, n) { if(!is.numeric(x)) stop("'T' must be numeric. ") if (length(x) == 1 && m == 1) { @@ -293,7 +329,7 @@ check_T <- function(x, m, n) { } x } - +#' @rdname check check_R <- function(x, m, n) { if (length(x) == m) { dim(x) <- c(m, 1, 1) @@ -310,7 +346,7 @@ check_R <- function(x, m, n) { } x } - +#' @rdname check check_a1 <- function(x, m) { if (missing(x) || is.null(x)) { x <- numeric(m) @@ -326,6 +362,7 @@ check_a1 <- function(x, m) { x } +#' @rdname check check_P1 <- function(x, m) { if (missing(x) || is.null(x)) { x <- matrix(0, m, m) @@ -342,6 +379,7 @@ check_P1 <- function(x, m) { x } +#' @rdname check check_H <- function(x, p, n, multivariate = FALSE) { if(!is.numeric(x)) stop("'H' must be numeric. ") @@ -362,7 +400,7 @@ check_H <- function(x, p, n, multivariate = FALSE) { x } - +#' @rdname check check_integer <- function(x, name = "particles", positive = TRUE, max = 1e7) { if (!test_count(x, positive)) { stop(paste0("Argument '", name, "' should be a ", @@ -374,14 +412,25 @@ check_integer <- function(x, name = "particles", positive = TRUE, max = 1e7) { } as.integer(x) } - +#' @rdname check check_positive_real <- function(x, name) { if (!test_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { stop(paste0("Argument '", name, "' should be positive real value.")) } x } - +#' @rdname check +check_theta <- function(x) { + + if (!is.numeric(x) || !test_atomic_vector(x, strict = TRUE)) { + stop("Argument 'theta' should be a numeric vector.") + } + if (is.null(names(x))) { + names(x) <- paste("theta_", 1:length(x)) + } + x +} +#' @rdname check check_missingness <- function(x) { if (!inherits(x, c("ssm_nlg", "ssm_sde"))) { if (is.null(x$prior_parameters)) { diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 92c96a6b..40f9f033 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -1,5 +1,3 @@ -#' @srrstats {BS1.5} Several options for ESS. See also asymptotic_var.R -NULL #' Quick Diagnostics Checks for \code{run_mcmc} Output #' #' Prints out the acceptance rate, smallest effective sample sizes (ESS) and @@ -15,7 +13,8 @@ NULL #' @param x Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}}. #' @export -#' +#' @srrstats {BS5.3, BS5.5} Several options for ESS. See also asymptotic_var.R +#' and summary functions #' @examples #' set.seed(1) #' n <- 30 diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 294c8206..174fa83b 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -4,7 +4,6 @@ #' Run \code{cpp_example_model("abc")} to get the names of possible models. #' @param return_code If TRUE, will not compile the model but only returns the #' corresponding code. -#' #' @return Returns pointers to the C++ snippets defining the model, or in case #' of \code{return_code = TRUE}, returns the example code without compiling. #' @export @@ -14,7 +13,7 @@ cpp_example_model <- function(example, return_code = FALSE) { example <- match.arg(example, c("nlg_linear_gaussian", "nlg_sin_exp", - "nlg_growth", "nlg_ar_exp", "sde_poisson_OU")) + "nlg_growth", "nlg_ar_exp", "sde_poisson_OU", "sde_gbm")) code <- switch(example, "sde_poisson_OU" = { @@ -98,6 +97,85 @@ cpp_example_model <- function(example, return_code = FALSE) { Rcpp::Named("obs_density") = Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); } ' + }, + "sde_gbm" = { + ' + // A latent Geometric Brownian motion with Gaussian observations + // dx_t = mu * x_t * dt + sigma_x * x_t * dB_t, t>=0, + // y_k ~ N(log(x_k), sigma_y^2), k = 1,...,n + // See Vihola, Helske, and Franks (2020) + + // x: state + // theta: vector of parameters + + // theta(0) = mu + // theta(1) = log(sigma_x) + // theta(2) = log(sigma_y) + + #include + // [[Rcpp::depends(RcppArmadillo)]] + // [[Rcpp::interfaces(r, cpp)]] + + // Drift function + // [[Rcpp::export]] + double drift(const double x, const arma::vec& theta) { + return theta(0) * x; + } + // diffusion function + // [[Rcpp::export]] + double diffusion(const double x, const arma::vec& theta) { + return std::max(0.0, exp(theta(1)) * x); + } + // Derivative of the diffusion function + // [[Rcpp::export]] + double ddiffusion(const double x, const arma::vec& theta) { + return exp(theta(1)) * (x > 0.0); + } + + // log-density of the prior + // Note that these differ from the ones in the paper + // [[Rcpp::export]] + double log_prior_pdf(const arma::vec& theta) { + // remember, dgamma is shape and scale in C side + double log_pdf = R::dnorm(theta(0), 0, 0.5, 1) + + R::dgamma(exp(theta(1)), 2, 1, 1) + theta(1); + R::dgamma(exp(theta(2)), 2, 1, 1) + theta(2); + return log_pdf; + } + + // log-density of observations + // given vector of sampled states alpha + // [[Rcpp::export]] + arma::vec log_obs_density(const double y, + const arma::vec& alpha, const arma::vec& theta) { + + arma::vec log_pdf(alpha.n_elem); + for (unsigned int i = 0; i < alpha.n_elem; i++) { + log_pdf(i) = R::dnorm(y, log(alpha(i)), exp(theta(2)), 1); + } + return log_pdf; + } + + // [[Rcpp::export]] + Rcpp::List create_xptrs() { + // typedef for a pointer of drift/volatility function + typedef double (*fnPtr)(const double x, const arma::vec& theta); + // typedef for log_prior_pdf + typedef double (*prior_fnPtr)(const arma::vec& theta); + // typedef for log_obs_density + typedef arma::vec (*obs_fnPtr)(const double y, + const arma::vec& alpha, const arma::vec& theta); + + return Rcpp::List::create( + Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), + Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), + Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), + Rcpp::Named("prior") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), + Rcpp::Named("obs_density") = + Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + } + ' }, "nlg_ar_exp" = { ' diff --git a/R/kfilter.R b/R/kfilter.R index 58534a79..a83cf065 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -13,7 +13,8 @@ #' @return List containing the log-likelihood #' (approximate in non-Gaussian case), one-step-ahead predictions \code{at} #' and filtered estimates \code{att} of states, and the corresponding -#' variances \code{Pt} and \code{Ptt}. +#' variances \code{Pt} and \code{Ptt} up to the time point n+1 where n is the +#' length of the input time series. #' @seealso \code{\link{bootstrap_filter}} #' @export #' @rdname kfilter diff --git a/R/models.R b/R/models.R index 5666d86c..2f8733e7 100644 --- a/R/models.R +++ b/R/models.R @@ -1,11 +1,10 @@ #' @srrstats {G2.7, G2.8, G2.9} Only matrix/mts/arrays as tabular data are #' supported, not data.frame or similar objects. -#' #' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, BS3.0} Missing observations are handled #' automatically as per SSM theory, whereas missing values are not allowed #' elsewhere. #' @srrstats {BS1.0, BS1.1, BS1.2} -#' +NULL ## placeholder functions for fixed models default_prior_fn <- function(theta) { @@ -1040,10 +1039,10 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, use_phi <- distribution %in% c("negative binomial", "gamma") if (use_phi) { if (is_prior(phi)) { - check_phi(phi$init, distribution) + check_phi(phi$init) phi_est <- TRUE } else { - check_phi(phi, distribution) + check_phi(phi) } } else { phi <- 1 @@ -1297,10 +1296,10 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u, beta, phi_est <- FALSE if (use_phi) { if (is_prior(phi)) { - check_phi(phi$init, distribution) + check_phi(phi$init) phi_est <- TRUE } else { - check_phi(phi, distribution) + check_phi(phi) } } else { phi <- 1 @@ -1468,7 +1467,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' #' Compared to other models, these general models need a bit more effort from #' the user, as you must provide the several small C++ snippets which define the -#' model structure. See examples in the vignette. +#' model structure. See examples in the vignette and \code{cpp_example_model}. #' #' @param y Observations as multivariate time series (or matrix) of length #' \eqn{n}. @@ -1536,6 +1535,9 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, } n_states <- as.integer(n_states) n_etas <- as.integer(n_etas) + + theta <- check_theta(theta) + structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, Z_gn = Z_gn, T_gn = T_gn, a1 = a1, P1 = P1, theta = theta, log_prior_pdf = log_prior_pdf, known_params = known_params, @@ -1557,7 +1559,8 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' #' As in case of \code{ssm_nlg} models, these general models need a bit more #' effort from the user, as you must provide the several small C++ snippets -#' which define the model structure. See vignettes for an example. +#' which define the model structure. See vignettes for an example and +#' \code{cpp_example_model}. #' #' @param y Observations as univariate time series (or vector) of length #' \eqn{n}. @@ -1614,7 +1617,7 @@ ssm_sde <- function(y, drift, diffusion, ddiffusion, obs_pdf, prior_pdf, theta, x0, positive) { y <- check_y(y) - + theta <- check_theta(theta) structure(list(y = as.ts(y), drift = drift, diffusion = diffusion, ddiffusion = ddiffusion, obs_pdf = obs_pdf, diff --git a/R/predict.R b/R/predict.R index 5ad1a2d0..d69abb04 100644 --- a/R/predict.R +++ b/R/predict.R @@ -2,10 +2,11 @@ #' #' Draw samples from the posterior predictive distribution for future #' time points given the posterior draws of hyperparameters \eqn{\theta} and -#' latent state \eqn{alpha_{n+1}}. Function can also be used to draw samples -#' from the posterior predictive distribution -#' \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. -#' +#' latent state \eqn{alpha_{n+1}} returned by \code{run_mcmc}. +#' Function can also be used to draw samples from the posterior predictive +#' distribution \eqn{p(\tilde y_1, \ldots, \tilde y_n | y_1,\ldots, y_n)}. +#' +#' @seealso \code{fitted} for in-sample predictions. #' @param object Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}}. #' @param model A \code{bssm_model} object. diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 5dc53b9c..607d3fbc 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -8,6 +8,7 @@ #' @importFrom stats var #' @param x Output from \code{\link{run_mcmc}}. #' @param ... Ignored. +#' @srrstats {BS6.0} #' @export print.mcmc_output <- function(x, ...) { @@ -125,6 +126,7 @@ print.mcmc_output <- function(x, ...) { #' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @export +#' @srrstats {BS6.4} summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) { diff --git a/R/priors.R b/R/priors.R index 9a5b4c56..3e93ba87 100644 --- a/R/priors.R +++ b/R/priors.R @@ -1,5 +1,3 @@ -#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6} Correct prior definitions. -NULL #' Prior objects for bssm models #' #' These simple objects of class \code{bssm_prior} are used to construct a @@ -30,6 +28,7 @@ NULL #' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case #' of multiple priors (i.e. multiple regression coefficients). #' @export +#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6} Correct prior definitions. #' @examples #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: #' uniform(init = 0.2, min = -1.0, max = 1.0) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 98c21399..a71dc17d 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -1,11 +1,3 @@ -#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} -#' @srrstats {BS2.6} -#' @srrstats {BS2.7, BS1.3a, BS2.8} Explained in docs. -#' @srrrstats {BS2.9} The argument 'seed' is set to random value if not -#' specified by the user. -#' @srrstatsTODO {BS2.12} -NULL -#' #' Bayesian Inference of State Space Models #' #' Adaptive Markov chain Monte Carlo simulation for SSMs using @@ -23,10 +15,13 @@ NULL #' simulation smoother. In other cases, the means and #' covariances are computed using the full output of particle filter #' instead of subsampling one of these as in case of -#' \code{output_type = "full"}. +#' \code{output_type = "full"}. The states are sampled up to the time point n+1 +#' where n is the length of the input time series i.e. the last values are +#' one-step-ahead predictions. (for predicting further, see +#' \code{?predict.mcmc_output}). #' #' Initial values for the sampling are taken from the model object -#' (\code{model$theta]). If you want to continue from previous run, you can +#' (\code{model$theta}). If you want to continue from previous run, you can #' reconstruct your original model by plugging in the previously obtained #' parameters to \code{model$theta}, providing the S matrix for the RAM #' algorithm and setting \code{burnin = 0}. See example. Note however, that @@ -113,6 +108,14 @@ NULL #' For pseudo-marginal methods (\code{"pm"}), maximum of these is used. #' @param ... Ignored. #' @export +#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} +#' @srrstats {BS2.6} +#' @srrstats {BS2.7, BS1.3a, BS2.8} Explained in docs. +#' @srrstats {BS2.9} The argument 'seed' is set to random value if not +#' specified by the user. +#' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the +#' input model, whereas some metadata (like the class of input model and seed) +#' is returned by run_mcmc. #' @rdname run_mcmc #' @references #' [1] Vihola M (2012). Robust adaptive Metropolis algorithm with diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index d184b1fd..d60f7370 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -9,10 +9,8 @@ #' #' @srrstatsVerbose TRUE #' -#' ### Standards for general statistical software ### -#' -#' # General documentation, addressed by the paper vignette and the corresponding R Journal paper -#' +# Standards for general statistical software +# General documentation, addressed by the paper vignette and the corresponding R Journal paper#' #' @srrstats {G1.0} *Statistical Software should list at least one primary reference from published academic literature.* #' @srrstats {G1.1} *Statistical Software should document whether the algorithm(s) it implements are:* - *The first implementation of a novel algorithm*; or - *The first implementation within **R** of an algorithm which has previously been implemented in other languages or contexts*; or - *An improvement on other implementations of similar algorithms in **R***. #' @srrstats {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* @@ -22,31 +20,31 @@ #' @srrstats {G1.5} *Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.* #' @srrstats {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* #' -#' # As tested by autotest +#' As tested by autotest #' @srrstats {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* #' @srrstats {G2.0a} *Provide explicit secondary documentation of any expectations on lengths of inputs.* #' @srrstats {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* #' @srrstats {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* #' @srrstats {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* #' -#' ## Explicit conversions are used where necessary +#' Explicit conversions are used where necessary #' @srrstats {G2.4} *Provide appropriate mechanisms to convert between different data types, potentially including:* #' @srrstats {G2.4a} *explicit conversion to `integer` via `as.integer()`* #' @srrstats {G2.4b} *explicit conversion to continuous via `as.numeric()`* #' @srrstats {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* #' @srrstats {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* #' -#' ## match.arg and tolower are used where applicable. +#' match.arg and tolower are used where applicable. #' @srrstats {G2.3} *For univariate character input:* #' @srrstats {G2.3a} *Use `match.arg()` or equivalent where applicable to only permit expected values.* #' @srrstats {G2.3b} *Either: use `tolower()` or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.* - -#' ## Only matrix/mts/arrays are supported, not data.frame style objects. +#' +#' Only matrix/mts/arrays are supported, not data.frame style objects. #' @srrstats {G2.7} *Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.* #' @srrstats {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* #' @srrstats {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* #' -#' ## Missing observations (y) are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere. Inputing or ignoring them does not make sense in time series context. +#' Missing observations (y) are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere. Inputing or ignoring them does not make sense in time series context. #' @srrstats {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* #' @srrstats {G2.14a} *error on missing data* #' @srrstats {G2.14b} *ignore missing data with default warnings or messages issued* @@ -55,18 +53,18 @@ #' @srrstats {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* #' #' @srrstats {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* -#' -#' # Simulated datasets are used in several occasions +#' +#' Simulated datasets are used in several occasions #' @srrstats {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* #' @srrstats {G5.1} *Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.* - +#' #' @srrstats {G5.2} *Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,* #' @srrstats {G5.2a} *Every message produced within R code by `stop()`, `warning()`, `message()`, or equivalent should be unique* -#' @srrstatsTODO {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* -#' @srrstatsTODO {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* - -#' ## Correctness is demonstrated in Helske & Vihola (2021) (comparison with Stan) and Vihola & Helske & Franks (2020) (correcness of the IS-MCMC etc) -#' ## Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation. +#' @srrstats {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* +#' @srrstats {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* +#' +#' Correctness is demonstrated in Helske & Vihola (2021) (comparison with Stan) and Vihola & Helske & Franks (2020) (correcness of the IS-MCMC etc) +#' Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation. #' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* #' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* #' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* @@ -76,126 +74,140 @@ #' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* #' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* #' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* - -#' ## Tested by autotest and testthat tests +#' +#' Tested by autotest and testthat tests #' @srrstats {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* #' @srrstats {G5.8a} *Zero-length data* #' @srrstats {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* #' @srrstats {G5.8c} *Data with all-`NA` fields or columns or all identical fields or columns* #' @srrstats {G5.8d} *Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)* - -#' ## Tested by autotest and testthat tests +#' +#' Tested by autotest and testthat tests #' @srrstats {G5.9} **Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:* #' @srrstats {G5.9a} *Adding trivial noise (for example, at the scale of `.Machine$double.eps`) to data does not meaningfully change results* #' @srrstats {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results* - -#' # General standards not applicable #' -#' ## Factor types are not used nor supported: -#' @srrstatsNA {G2.4d} *explicit conversion to factor via `as.factor()`* -#' @srrstatsNA {G2.4e} *explicit conversion from factor via `as...()` functions* -#' @srrstatsNA {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* +#' Standards for Bayesian software #' -#' # No data.frame style tabular data is used/supported as input -#' @srrstatsNA {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* -#' @srrstatsNA {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* -#' @srrstatsNA {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* -#' @srrstatsNA {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* - - -#' @srrstatsNA {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* -#' @srrstatsNA {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* - -#' # No output is written to local files -#' @srrstatsNA {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* - -#' # Package does not contain extended tests (although benchmarks folder contains template for running such very time-consuming tests) -#' @srrstatsNA {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* -#' @srrstatsNA {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* -#' @srrstatsNA {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* -#' @srrstatsNA {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* - - -# Standards for Bayesian software - -#' ## addressed by the vignette, the corresponding R Journal paper, models.R and run_mcmc.R +#' Addressed by the vignette, the corresponding R Journal paper, models.R and run_mcmc.R #' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* #' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* #' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* #' @srrstats {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* #' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* -#' @srrstats {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* #' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* - -#' ## Tested by autotest and package documentation +#' @srrstats {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* +#' @srrstats {BS4.1} *Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).* +#' @srrstats {BS5.3} *Bayesian Software should return convergence statistics or equivalent* +#' @srrstats {BS5.5} *Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.* +#' +#' Tested by autotest and package documentation #' @srrstats {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* #' @srrstats {BS2.1a} *The effects of such routines should be tested.* - -#' ## Handled by the prior construction functions +#' +#' Handled by the prior construction functions #' @srrstats {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* #' @srrstats {BS2.3} *Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).* #' @srrstats {BS2.4} *Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)* #' @srrstats {BS2.5} *Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.* - -#' ## Checked, explained and done in run_mcmc +#' +#' Checked, explained and done in run_mcmc #' @srrstats {BS2.6} *Check that values for computational parameters lie within plausible ranges.* #' @srrstats {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* #' @srrstats {BS2.9} *Ensure each chain is started with a different seed by default.* - +#' #' As with the the general standards. #' @srrstats {BS3.0} *Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (`NA`, `Inf`) values, and that such values, or entire rows including any such values, will be automatically removed from input data.* - - -#' # Bayesian standards not applicable - -#' ## Not applicable as only single-chain runs are supported. -#' @srrstatsNA {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* -#' ## Starting values are not accepted in this form. -#' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* - -#' ## Of course applicable, but at the moment this is not done -#' @srrstatsNA {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* -#' @srrstatsNA {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* -#' @srrstatsNA {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* #' -#' ## ? -#' @srrstatsTODO {BS2.15} *Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.* -#' -#' @srrstatsTODO {BS3.1} *Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings* -#' @srrstatsTODO {BS3.2} *Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms* -#' @srrstatsTODO {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* -#' @srrstatsTODO {BS4.1} *Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).* -#' @srrstatsTODO {BS4.3} *Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.* -#' @srrstatsTODO {BS4.4} *Enable computations to be stopped on convergence (although not necessarily by default).* -#' @srrstatsTODO {BS4.5} *Ensure that appropriate mechanisms are provided for models which do not converge.* -#' @srrstatsTODO {BS4.6} *Implement tests to confirm that results with convergence checker are statistically equivalent to results from equivalent fixed number of samples without convergence checking.* -#' @srrstatsTODO {BS4.7} *Where convergence checkers are themselves parametrised, the effects of such parameters should also be tested. For threshold parameters, for example, lower values should result in longer sequence lengths.* -#' @srrstatsTODO {BS5.0} *Return values should include starting value(s) or seed(s), including values for each sequence where multiple sequences are included* -#' @srrstatsTODO {BS5.1} *Return values should include appropriate metadata on types (or classes) and dimensions of input data* -#' @srrstatsTODO {BS5.2} *Bayesian Software should either return the input function or prior distributional specification in the return object; or enable direct access to such via additional functions which accept the return object as single argument.* -#' @srrstatsTODO {BS5.3} *Bayesian Software should return convergence statistics or equivalent* -#' @srrstatsTODO {BS5.4} *Where multiple checkers are enabled, Bayesian Software should return details of convergence checker used* -#' @srrstatsTODO {BS5.5} *Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.* -#' @srrstatsTODO {BS6.0} *Software should implement a default `print` method for return objects* -#' @srrstatsTODO {BS6.1} *Software should implement a default `plot` method for return objects* -#' @srrstatsTODO {BS6.2} *Software should provide and document straightforward abilities to plot sequences of posterior samples, with burn-in periods clearly distinguished* -#' @srrstatsTODO {BS6.3} *Software should provide and document straightforward abilities to plot posterior distributional estimates* -#' @srrstatsTODO {BS6.4} *Software may provide `summary` methods for return objects* -#' @srrstatsTODO {BS6.5} *Software may provide abilities to plot both sequences of posterior samples and distributional estimates together in single graphic* -#' @srrstatsTODO {BS7.0} *Software should demonstrate and confirm recovery of parametric estimates of a prior distribution* -#' @srrstatsTODO {BS7.1} *Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information* -#' @srrstatsTODO {BS7.2} *Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data* -#' @srrstatsTODO {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* -#' @srrstatsTODO {BS7.4} *Bayesian software should implement tests which confirm that predicted or fitted values are on (approximately) the same scale as input values.* -#' @srrstatsTODO {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* +#' run_mcmc returns some of these, other are embedded in the input model itself (starting values) +#' @srrstats {BS5.0} *Return values should include starting value(s) or seed(s), including values for each sequence where multiple sequences are included* +#' @srrstats {BS5.1} *Return values should include appropriate metadata on types (or classes) and dimensions of input data* +#' @srrstats {BS5.2} *Bayesian Software should either return the input function or prior distributional specification in the return object; or enable direct access to such via additional functions which accept the return object as single argument.* +#' +#' Implemented. +#' @srrstats {BS6.0} *Software should implement a default `print` method for return objects* +#' @srrstats {BS6.4} *Software may provide `summary` methods for return objects* +#' +#' Demonstrated in the vignettes, examples, papers and tests +#' @srrstats {BS7.0} *Software should demonstrate and confirm recovery of parametric estimates of a prior distribution* +#' @srrstats {BS7.1} *Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information* +#' @srrstats {BS7.2} *Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data* +#' @srrstats {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* +#' @srrstats {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* +#' +#' Demonstrated in test_mcmc.R. +#' The scales do not matter (in terms of runtime) in random walk Metropolis nor in particle filters, as long as numerical issues are not encountered +#' @srrstats {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* +#' @srrstats {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* +#' #' @noRd NULL - #' NA_standards #' #' Any non-applicable standards can have their tags changed from `@srrstatsTODO` #' to `@srrstatsNA`, and placed together in this block, along with explanations #' for why each of these standards have been deemed not applicable. #' (These comments may also be deleted at any time.) +#' +#' General standards not applicable +#' +#' Factor types are not used nor supported: +#' @srrstatsNA {G2.4d} *explicit conversion to factor via `as.factor()`* +#' @srrstatsNA {G2.4e} *explicit conversion from factor via `as...()` functions* +#' @srrstatsNA {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* +#' +#' No data.frame style tabular data is used/supported as input +#' @srrstatsNA {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* +#' @srrstatsNA {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* +#' @srrstatsNA {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* +#' @srrstatsNA {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* +#' +#' @srrstatsNA {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* +#' @srrstatsNA {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* +#' +#' No output is written to local files +#' @srrstatsNA {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* +#' +#' Package does not contain extended tests (although benchmarks folder contains template for running such very time-consuming tests) +#' @srrstatsNA {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* +#' @srrstatsNA {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* +#' @srrstatsNA {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* +#' @srrstatsNA {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* +#' +#' Bayesian standards not applicable +#' +#' Not applicable as only single-chain runs are supported. +#' @srrstatsNA {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* +#' Starting values are not accepted in this form. +#' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* +#' +#' No automatic stopping at converge (converge checkers) is supported +#' @srrstatsNA {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* +#' @srrstatsNA {BS4.3} *Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.* +#' @srrstatsNA {BS4.4} *Enable computations to be stopped on convergence (although not necessarily by default).* +#' @srrstatsNA {BS4.5} *Ensure that appropriate mechanisms are provided for models which do not converge.* +#' @srrstatsNA {BS4.6} *Implement tests to confirm that results with convergence checker are statistically equivalent to results from equivalent fixed number of samples without convergence checking.* +#' @srrstatsNA {BS4.7} *Where convergence checkers are themselves parametrised, the effects of such parameters should also be tested. For threshold parameters, for example, lower values should result in longer sequence lengths.* +#' @srrstatsNA {BS5.4} *Where multiple checkers are enabled, Bayesian Software should return details of convergence checker used* +#' +#' Of course applicable, but at the moment this is not done (issue has been open several years in github...). +#' @srrstatsNA {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* +#' @srrstatsNA {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* +#' @srrstatsNA {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* +#' @srrstatsNA {BS2.15} *Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.* +#' +#' Not really relevant for SSMs, or at least difficult to check this kind of thing in general +#' @srrstatsNA {BS3.1} *Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings* +#' @srrstatsNA {BS3.2} *Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms* +#' +#' Just suggests using ggplot or bayesplot packages, with several examples +#' @srrstatsNA {BS6.1} *Software should implement a default `plot` method for return objects* +#' @srrstatsNA {BS6.2} *Software should provide and document straightforward abilities to plot sequences of posterior samples, with burn-in periods clearly distinguished* +#' @srrstatsNA {BS6.3} *Software should provide and document straightforward abilities to plot posterior distributional estimates* +#' @srrstatsNA {BS6.5} *Software may provide abilities to plot both sequences of posterior samples and distributional estimates together in single graphic* +#' +#' The computation of posterior predicted/fitted value is somewhat time consuming so it is not done automatically (there is a fitted/predict functions for that) +#' @srrstatsNA {BS7.4} *Bayesian software should implement tests which confirm that predicted or fitted values are on (approximately) the same scale as input values.* +#' #' @noRd NULL diff --git a/README.Rmd b/README.Rmd index f1f86e53..4a4dc0b5 100644 --- a/README.Rmd +++ b/README.Rmd @@ -30,7 +30,7 @@ knitr::opts_chunk$set( #' #' @srrstats {G3.0} No floating point equality comparisons are made. #' -#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The #' algorithms work correctly as per Vihola, Helske, Franks (2020) #' (all simulations were implemented with the bssm package) and Helske #' and Vihola (2021). Full replication of the results would take days/weeks @@ -43,7 +43,6 @@ knitr::opts_chunk$set( #' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} Addressed in the models.R, #' run_mcmc.R, in vignettes and in the R Journal paper. #' -#' #' @srrstats {BS2.1, BS2.1a} Tested by autotest and package examples/tests. ``` diff --git a/README.md b/README.md index 18819082..033f4476 100644 --- a/README.md +++ b/README.md @@ -157,7 +157,7 @@ fit #> #> Run time: #> user system elapsed -#> 0.89 0.01 0.90 +#> 0.89 0.02 0.91 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -230,7 +230,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 10.75 0.04 10.77 +#> 10.59 0.02 10.62 ``` Comparison: @@ -363,7 +363,7 @@ fit #> #> Run time: #> user system elapsed -#> 12.02 0.13 12.07 +#> 11.94 0.06 11.94 ``` Draw predictions: diff --git a/benchmarks/replications.Rmd b/benchmarks/replications.Rmd index 67e1380b..d2b204e7 100644 --- a/benchmarks/replications.Rmd +++ b/benchmarks/replications.Rmd @@ -6,19 +6,11 @@ output: html_document --- ```{r srr-tags, eval = FALSE, echo = FALSE} -#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The #' algorithms work correctly as per Vihola, Helske, Franks (2020) #' (all simulations were implemented with the bssm package) and Helske #' and Vihola (2021). -#' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* -#' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* -#' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* -#' @srrstats {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* -#' @srrstats {G5.5} *Correctness tests should be run with a fixed random seed* -#' @srrstats {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* -#' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* -#' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* -#' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* + ``` ```{r setup, include=FALSE} diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 3224f084..802677d9 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -13,7 +13,7 @@ as_bssm(model, kappa = 100, ...) used to replace exact diffuse elements of the original model.} \item{...}{Additional arguments to model building functions of \code{bssm} -(such as prior and updating functions).} +(such as prior and updating functions, C, and D).} } \value{ Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or diff --git a/man/bssm.Rd b/man/bssm.Rd index 4eaba2ae..b068947e 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -6,7 +6,7 @@ \title{Bayesian Inference of State Space Models} \description{ This package contains functions for efficient Bayesian inference of state -space models, where model is assumed to be either +space models (SSMs), where model is assumed to be either } \details{ \itemize{ @@ -18,12 +18,16 @@ Poisson, binomial, negative binomial or Gamma density. \item Model with continuous SDE dynamics. } +Missing values in response series are allowed as per SSM theory and can be +automatically predicted, but there can be no missing values in the system +matrices of the model. + The \code{bssm} package includes several MCMC sampling and sequential Monte Carlo methods for models outside classic linear-Gaussian framework. For -definitions of the currently supported models and methods, as -well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF -algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and -the package vignettes and the R Journal paper. +definitions of the currently supported models and methods, usage of the +package as well as some theory behind the novel IS-MCMC and +\eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, +Helske, Franks (2020), and the package vignettes. } \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and diff --git a/man/check.Rd b/man/check.Rd new file mode 100644 index 00000000..07dccf92 --- /dev/null +++ b/man/check.Rd @@ -0,0 +1,112 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_arguments.R +\name{check_y} +\alias{check_y} +\alias{check_period} +\alias{check_distribution} +\alias{check_sd} +\alias{check_xreg} +\alias{check_beta} +\alias{check_mu} +\alias{check_rho} +\alias{check_phi} +\alias{check_u} +\alias{check_prior} +\alias{check_prop} +\alias{check_D} +\alias{check_C} +\alias{create_regression} +\alias{check_Z} +\alias{check_T} +\alias{check_R} +\alias{check_a1} +\alias{check_P1} +\alias{check_H} +\alias{check_integer} +\alias{check_positive_real} +\alias{check_theta} +\alias{check_missingness} +\title{Check Arguments} +\usage{ +check_y(x, multivariate = FALSE, distribution = "gaussian") + +check_period(x, n) + +check_distribution(x, distribution) + +check_sd(x, type, add_prefix = TRUE) + +check_xreg(x, n) + +check_beta(x, k) + +check_mu(x) + +check_rho(x) + +check_phi(x) + +check_u(x, y, multivariate = FALSE) + +check_prior(x, name) + +check_prop(x, name = "target") + +check_D(x, p, n) + +check_C(x, m, n) + +create_regression(beta, xreg, n) + +check_Z(x, p, n, multivariate = FALSE) + +check_T(x, m, n) + +check_R(x, m, n) + +check_a1(x, m) + +check_P1(x, m) + +check_H(x, p, n, multivariate = FALSE) + +check_integer(x, name = "particles", positive = TRUE, max = 1e+07) + +check_positive_real(x, name) + +check_theta(x) + +check_missingness(x) +} +\arguments{ +\item{multivariate}{Logical, should \code{p} be larger than 1?} + +\item{distribution}{Distribution(s) of the responses.} + +\item{n}{Integer, number of time points.} + +\item{type}{Name to be added to the sd parameter name.} + +\item{add_prefix}{Logical, add \code{type} to parameter name.} + +\item{k}{Integer, number of predictors.} + +\item{y}{The response time series.} + +\item{name}{Name of the argument used in printing error messages.} + +\item{p}{Integer, number of time series.} + +\item{m}{Integer, dimensionality of the state vector.} + +\item{beta}{Vector of regression coefficients.} + +\item{xreg}{Matrix or vector of predictors.} + +\item{positive}{Logical, check for positiveness of \code{x}.} + +\item{max}{Maximum value of \code{x}.} +} +\description{ +Check Arguments +} diff --git a/man/figures/README-bivariate-fig-1.png 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zd~bG0)1ZZCLhDqAJMQvvktgK5J3__1Ut;t5x9Ng&i)wZ5zO-HKq|Q3^NB;ZCvEj#C zE9v=>Qf&qiH(&e*7~r38x0M^OSev#Ze74IxPwZle44(diuD_JO?8xA%hvE2U0=MPE zhxy5;5t5%;KHQrRExtRqFrJzdnZAlNu4V);%=9f}r`C@5?O6Evxo*d;nV\% } +\seealso{ +\code{fitted} for in-sample predictions. +} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 6e54543c..c140407f 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -191,7 +191,27 @@ arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and Vihola (2021) for details. } \details{ - +For linear-Gaussian models, option \code{"summary"} does not simulate +states directly but computes the posterior means and variances of states +using fast Kalman smoothing. This is slightly faster, +more memory efficient and more accurate than calculations based on +simulation smoother. In other cases, the means and +covariances are computed using the full output of particle filter +instead of subsampling one of these as in case of +\code{output_type = "full"}. The states are sampled up to the time point n+1 +where n is the length of the input time series i.e. the last values are +one-step-ahead predictions. (for predicting further, see +\code{?predict.mcmc_output}). + +Initial values for the sampling are taken from the model object +(\code{model$theta}). If you want to continue from previous run, you can +reconstruct your original model by plugging in the previously obtained +parameters to \code{model$theta}, providing the S matrix for the RAM +algorithm and setting \code{burnin = 0}. See example. Note however, that +this is not identical as running all the iterations once, due to the +RNG "discontinuity" and because even without burnin bssm does include +"theta_0" i.e. the initial theta in the final chain (even with +\code{burnin=0}). } \examples{ model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index dab2bbd1..a9cdd96d 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -87,7 +87,7 @@ where \eqn{\epsilon_t \sim N(0, I_p)}, \eqn{\eta_t \sim N(0, I_m)} and Compared to other models, these general models need a bit more effort from the user, as you must provide the several small C++ snippets which define the -model structure. See examples in the vignette. +model structure. See examples in the vignette and \code{cpp_example_model}. } \examples{ \donttest{ # Takes a while on CRAN diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index 89cfa9cc..eedd5eec 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -50,7 +50,8 @@ observations are measured at integer times (missing values are allowed). \details{ As in case of \code{ssm_nlg} models, these general models need a bit more effort from the user, as you must provide the several small C++ snippets -which define the model structure. See vignettes for an example. +which define the model structure. See vignettes for an example and +\code{cpp_example_model}. } \examples{ diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 985275ec..dca54938 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -164,24 +164,26 @@ model_bssm <- as_bssm(model_kfas, kappa = 100) # "manually" by constructing only necessary matrices, # i.e., in this case a list with H and Q) -updatefn <- function(theta) { +prior_fn <- function(theta) { + if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) +} + +update_fn <- function(theta) { model_kfas <- SSModel(log(drivers) ~ SSMtrend(1, Q = theta[1]^2)+ SSMseasonal(period = 12, sea.type = "trigonometric", Q = theta[2]^2) + log(PetrolPrice) + law, data = Seatbelts, H = theta[3]^2) - as_bssm(model_kfas, kappa = 100) + # the bssm_model object is essentially list so this is fine + as_bssm(model_kfas, kappa = 100, init_theta = init_theta, + update_fn = update_fn, prior_fn = prior_fn) } -prior <- function(theta) { - if(any(theta < 0)) -Inf else sum(dnorm(theta, 0, 0.1, log = TRUE)) -} init_theta <- rep(1e-2, 3) -c("sd_level", "sd_seasonal", "sd_y") -model_bssm <- as_bssm(model_kfas, kappa = 100, - init_theta = init_theta, - prior_fn = prior, update_fn = updatefn) +names(init_theta) <- c("sd_level", "sd_seasonal", "sd_y") + +model_bssm <- update_fn(init_theta) \donttest{ out <- run_mcmc(model_bssm, iter = 10000, burnin = 5000) diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index 443621dd..69f833ab 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -1,4 +1,4 @@ -#' @srrstats {G.5.4, G5.4a, G5.4b, G5.4c, G5.9b} Tests that the approximation +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.9b} Tests that the approximation #' coincides with KFAS and in GLM case results coincide with the glm. context("Test Gaussian approximation") diff --git a/tests/testthat/test_as_bssm.R b/tests/testthat/test_as_bssm.R index cc17ffff..475c2ba8 100644 --- a/tests/testthat/test_as_bssm.R +++ b/tests/testthat/test_as_bssm.R @@ -47,3 +47,28 @@ test_that("Test conversion from SSModel to ssm_mng", { expect_error(conv_model_bssm <- as_bssm(model_KFAS, init_theta = c(0, 0)), NA) expect_equivalent(model_bssm, conv_model_bssm) }) + +test_that("Test that time-varying parameters fail", { + library(KFAS) + model_KFAS <- SSModel(1:10 ~ 1, u = 1:10, distribution = "negative binomial") + expect_error(as_bssm(model_KFAS)) + + model_KFAS <- SSModel(1:10 ~ 1, u = 1:10, distribution = "gamma") + expect_error(as_bssm(model_KFAS)) + + model_KFAS <- SSModel(cbind(1:10, 1:10) ~ 1, u = matrix(1:20, 10, 2), + distribution = "negative binomial") + expect_error(as_bssm(model_KFAS)) + + model_KFAS <- SSModel(cbind(1:10, 1:10) ~ 1, u = matrix(1:20,10,2), + distribution = "gamma") + expect_error(as_bssm(model_KFAS)) + + model_KFAS <- SSModel(cbind(1:10, 1:10) ~ 1, u = cbind(1, 1:10), + distribution = c("gamma", "gaussian")) + expect_error(as_bssm(model_KFAS)) + + model_KFAS <- SSModel(cbind(1:10, 1:10) ~ 1, u = matrix(1:20,10,2), + distribution = c("binomial", "poisson")) + expect_error(as_bssm(model_KFAS), NA) +}) diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index 4ad6f3da..b30b243a 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -1,7 +1,7 @@ context("Test basics") #' @srrstats {G5.4, G5.4b, G5.6, G5.6a, G5.6b, G5.7} Compare with KFAS. -#' + test_that("results for Gaussian models are comparable to KFAS", { library("KFAS") model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(0.01^2, 0)), H = 2) @@ -192,3 +192,11 @@ test_that("multivariate normal pdf works", { logp3 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) expect_equivalent(logp3, -12.5587625856078, tolerance = 1e-6) }) + +test_that("asymptotic_var fails with improper weights", { + x <- rnorm(10) + expect_error(asymptotic_var(x, 0)) + expect_error(asymptotic_var(x, rep(0, length(x)))) + expect_error(asymptotic_var(x, c(-1, runif(9)))) + expect_error(asymptotic_var(x, c(Inf, runif(9)))) +}) \ No newline at end of file diff --git a/tests/testthat/test_is.R b/tests/testthat/test_is.R index a2226e50..033792f2 100644 --- a/tests/testthat/test_is.R +++ b/tests/testthat/test_is.R @@ -1,9 +1,9 @@ context("Test importance_sample") -#' @srrstats {G5.6, G5.6a, G5.6b, G5.7, G5.9b} Replicate Durbin&Koopman (1997) -#' test_that("Test that bssm recovers the parameters of the Seatbelts model", { + #' @srrstats {G5.6, G5.6a, G5.6b, G5.7, G5.9b} Replicate Durbin&Koopman (1997) + model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", sd_level = 1, sd_seasonal = 1, xreg = Seatbelts[, "law"], beta = normal(0, 0, 1)) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 45037bf4..04b55207 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -1,7 +1,41 @@ context("Test MCMC") -tol <- 1e-8 #' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5} Replicate Helske & Vihola (2021) +#' @srrstats {BS7.0, BS7.1, BS7.7} +#' @srrstats {BS7.3} + +tol <- 1e-8 + +test_that("prior and posterior distributions coincide when no data is used", { + + skip_on_cran() + + set.seed(1) + n <- 30 + x <- rnorm(n) + model <- ar1_ng(rep(NA, n), + xreg = x, + distribution = "negative binomial", + rho = uniform_prior(0.9, 0, 1), + sigma = gamma_prior(1, 2, 10), + mu = normal_prior(1, 0.2, 0.5), + phi = gamma_prior(0.4, 2, 1), + beta = normal_prior(0.5, 0, 1)) + + prior_sumr <- rbind( + rho = c(0.5, sqrt(1/12)), + sigma = c(0.2, sqrt(2)/10), + mu = c(0.2, 0.5), + phi = c(2, sqrt(2)), + beta = c(0, 1)) + + # approx is enough here, the weights are uniform when there is no data + fit <- run_mcmc(model, iter = 2e5,burnin = 1e4, mcmc_type = "approx") + expect_equivalent(prior_sumr, summary(fit), tol = 0.1) + +}) + + test_that("MCMC results from bssm paper are still correct", { skip_on_cran() @@ -29,6 +63,45 @@ test_that("MCMC results from bssm paper are still correct", { }) +test_that("scaling is linear", { + skip_on_cran() + + set.seed(1) + n <- 2^14 + + mu <- 2 + rho <- 0.7 + sd_y <- 0.1 + sigma <- 0.5 + beta <- -1 + x <- rnorm(n) + z <- y <- numeric(n) + z[1] <- rnorm(1, mu, sigma / sqrt(1 - rho^2)) + y[1] <- rnorm(1, beta * x[1] + z[1], sd_y) + for(i in 2:n) { + z[i] <- rnorm(1, mu * (1 - rho) + rho * z[i - 1], sigma) + y[i] <- rnorm(1, beta * x[i] + z[i], sd_y) + } + # run the MCMC with various number observations + m <- seq(1000, 10000, by = 3000) + times <- numeric(length(m)) + for(i in seq_along(m)) { + model <- ar1_lg(y[1:m[i]], + xreg = x[1:m[i]], + rho = uniform(0.5, -1, 1), + sigma = halfnormal(1, 10), + mu = normal(0, 0, 1), + sd_y = halfnormal(1, 10), + beta = normal(0, 0, 1)) + + times[i] <- run_mcmc(model, iter = 2e4)$time[3] + } + # standard Kalman filter has complexity of O(n * m) where n is number of time + # points and m is the number of states + expect_equivalent(min(times / m), max(times / m), tol = 0.1) +}) + + test_that("MCMC results for Gaussian model are correct", { set.seed(123) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index a7a18a9d..855e337e 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -1,10 +1,8 @@ context("Test that particle smoothers work") - #' @srrstats {G5.9, G5.9a, G5.9b} - test_that("Test that trivial noise does not affect particle_smoother", { expect_error(model_bsm <- bsm_lg(rep(1, 5), sd_level = 0.05, sd_slope = 0.01, diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index 677bd1a0..aa800318 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -1,95 +1,32 @@ context("Test SDE") test_that("MCMC for SDE works", { skip_on_cran() - code <- ' - #include - // [[Rcpp::depends(RcppArmadillo)]] - // [[Rcpp::interfaces(r, cpp)]] - - // Drift function - // [[Rcpp::export]] - double drift(const double x, const arma::vec& theta) { - return theta(0) * x; - } - // diffusion function - // [[Rcpp::export]] - double diffusion(const double x, const arma::vec& theta) { - return std::max(0.0, theta(1) * x); - } - // Derivative of the diffusion function - // [[Rcpp::export]] - double ddiffusion(const double x, const arma::vec& theta) { - return theta(1) * (x > 0.0); - } - - // log-density of the prior - // [[Rcpp::export]] - double log_prior_pdf(const arma::vec& theta) { - - double log_pdf = 0.0; - if(theta(0) <= -0.05 || theta(1) <= 0.0 || theta(2) <= 0.0) { - log_pdf = -std::numeric_limits::infinity(); - } - else { - log_pdf = R::dnorm(theta(0), 0, 0.05, 1) + - R::dnorm(theta(1), 0, 0.5, 1) + - R::dnorm(theta(2), 1, 0.1, 1); - } - return log_pdf; - } - - // log-density of observations - // given vector of sampled states alpha - // [[Rcpp::export]] - arma::vec log_obs_density(const double y, - const arma::vec& alpha, const arma::vec& theta) { - - arma::vec log_pdf(alpha.n_elem); - for (unsigned int i = 0; i < alpha.n_elem; i++) { - log_pdf(i) = R::dnorm(y, std::log(alpha(i)), theta(2), 1); - } - return log_pdf; - } - - // [[Rcpp::export]] - Rcpp::List create_xptrs() { - // typedef for a pointer of drift/volatility function - typedef double (*fnPtr)(const double x, const arma::vec& theta); - // typedef for log_prior_pdf - typedef double (*prior_fnPtr)(const arma::vec& theta); - // typedef for log_obs_density - typedef arma::vec (*obs_fnPtr)(const double y, - const arma::vec& alpha, const arma::vec& theta); - - return Rcpp::List::create( - Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), - Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), - Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), - Rcpp::Named("prior") = - Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), - Rcpp::Named("obs_density") = - Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); - } - ' - Rcpp::sourceCpp(code = code) - pntrs <- create_xptrs() + + pntrs <- cpp_example_model("sde_gbm") set.seed(1) - x <- 1 - y <- rep(NA, 10) + n <- 50 dt <- 1 mu <- 0.05 sigma_x <- 0.3 sigma_y <- 1 - for (k in 1:10) { - x <- x*exp((mu-0.5*sigma_x^2)*dt + sqrt(dt) * rnorm(1, sd=sigma_x)) - y[k] <- rnorm(1, mean=log(x), sd=sigma_y) + x <- numeric(n) + x[1] <- 1 + for (k in 2:n) { + x[k] <- x[k-1] * exp((mu - 0.5 * sigma_x^2) * dt + + sqrt(dt) * rnorm(1, sd = sigma_x)) } + y <- rnorm(n, log(x), sigma_y) - set.seed(123) model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, pntrs$ddiffusion, pntrs$obs_density, - pntrs$prior, c(0.05, 0.3, 1), x0 = 1, positive = TRUE) + pntrs$prior, c(mu = 0.08, log_sigma_x = 0.4, sigma_y = 1.5), + x0 = 1, positive = TRUE) + + expect_error(out <- run_mcmc(model, iter = 2e4, + particles = 50, mcmc_type = "is2", + L_c = 4, L_f = 6, threads = 2), NA) + expect_error(bootstrap_filter(model, 1000, L = -2)) expect_error(ll <- logLik(model, 10000, L = 3), NA) expect_equal(ll, -17, tol = 1) expect_error(out_bsf <- bootstrap_filter(model, 1000, L = 3), NA) diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index 06ac43d9..175cd074 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -28,8 +28,8 @@ knitr::opts_chunk$set(echo = TRUE) ```{r srr-tags, eval = FALSE, echo = FALSE} #' rOpenSci Statistical Software Standards addressed by the vignette #' -#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6} Here and in the cited R Journal -#' paper. +#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6, BS4.0, BS4.1} Here and in the cited +#' R Journal paper. #' To our knowledge, the package is also the first R package to #' implement delayed acceptance pseudo-marginal MCMC for general state space #' models. diff --git a/vignettes/bssm.bib b/vignettes/bssm.bib index 63c0f22b..e955682a 100644 --- a/vignettes/bssm.bib +++ b/vignettes/bssm.bib @@ -110,6 +110,13 @@ @Article{pitt-shephard1999 publisher = {Blackwell Publishers Ltd}, url = {https://dx.doi.org/10.1111/1467-9892.00126}, } + @Manual{diagis, + title = {{diagis}: Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling}, + author = {Jouni Helske}, + note = {R package version 0.2.2}, + url = {https://github.com/helske/diagis}, + } + @Article{coda, title = {{CODA}: Convergence Diagnosis and Output Analysis for {MCMC}}, diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 3a15586c..4010ac12 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -77,7 +77,7 @@ y <- p + rnorm(n, 0, H) ## Model in bssm -The functions determining the model functions are given in file `model_functions.cpp`. For example, function `T_fn` defines the state transition function $T(\cdot)$: +The functions determining the model need to be written in C++. Some example models which can be used as a template are given by the function `cpp_example_model` which returns pointers usable as an input to `nlg_ssm`. For this growth model, we could call `cpp_example_model("nlg_growth")`. In general, you need to define the functions matching the model components, log-density of the prior and few other functions. For example, in case of our model, the function `T_fn` defines the state transition function $T(\cdot)$: ```{Rcpp, eval = FALSE} // [[Rcpp::export]] @@ -105,7 +105,7 @@ Rcpp::sourceCpp("ssm_nlg_template.cpp") pntrs <- create_xptrs() ``` -This takes a few seconds. let's define our initial guess for $\theta$, the logarithms of the standard deviations of observational and process level noise, and define the prior distribution for $\alpha_1(we use log-scale in sampling for efficiency reasons, but define priors for the standard deviations, see the template file)$: +This takes a few seconds. let's define our initial guess for $\theta$, the logarithms of the standard deviations of observational and process level noise, and define the prior distribution for $\alpha_1$ (we use log-scale in sampling for efficiency reasons, but define priors for the standard deviations, see the template file at the appendix): ```{r theta} initial_theta <- c(log_H = 0, log_R1 = log(0.05), log_R2 = 0) @@ -159,7 +159,7 @@ summary(mcmc_ekf, return_se = TRUE) Using the `as.data.frame` method we can convert the state samples to a data frame for further processing with the `dplyr` package [@dplyr]: ```{r summaries} library("dplyr") -library("Hmisc") +library("diagis") d1 <- as.data.frame(mcmc_is, variable = "states") d2 <- as.data.frame(mcmc_ekf, variable = "states") d1$method <- "is2-psi" @@ -169,20 +169,20 @@ r_summary <- rbind(d1, d2) %>% filter(variable == "logit_r") %>% group_by(time, method) %>% summarise( - mean = wtd.mean(plogis(value), weight, normwt = TRUE), - lwr = wtd.quantile(plogis(value), weight, 0.025, normwt = TRUE), - upr = wtd.quantile(plogis(value), weight, 0.975, normwt = TRUE)) + mean = weighted_mean(plogis(value), weight), + lwr = weighted_quantile(plogis(value), weight, 0.025), + upr = weighted_quantile(plogis(value), weight, 0.975)) p_summary <- rbind(d1, d2) %>% filter(variable == "p") %>% group_by(time, method) %>% summarise( - mean = wtd.mean(value, weight, normwt = TRUE), - lwr = wtd.quantile(value, weight, 0.025, normwt = TRUE), - upr = wtd.quantile(value, weight, 0.975, normwt = TRUE)) + mean = weighted_mean(value, weight), + lwr = weighted_quantile(value, weight, 0.025), + upr = weighted_quantile(value, weight, 0.975)) ``` -Above we used the weighted versions of mean and quantile functions provided by the `Hmisc` [@hmisc] package as our IS-MCMC algorithm produces weighted samples of the posterior. Alternatively, we could have used argument `output_type = "summary"`, in which case the `run_mcmc` returns posterior means and covariances of the states instead of samples (these are computed using the full output of particle filter so these estimates are more accurate). +Above we used the weighted versions of mean and quantile functions provided by the `diagis`[@diagis] package as our IS-MCMC algorithm produces weighted samples of the posterior. Alternatively, we could have used argument `output_type = "summary"`, in which case the `run_mcmc` returns posterior means and covariances of the states instead of samples (these are computed using the full output of particle filter so these estimates are more accurate). Using `ggplot2` [@ggplot2] we can compare our two estimation methods: ```{r figures} @@ -213,7 +213,7 @@ mcmc_ekf$time ## Appendix -This is the full `ssm_nlg_template.cpp` file: +This is the full `ssm_nlg_template.cpp` file (identical with `nlg_growth` accessible with `cpp_example_model`): ```{Rcpp ssm_nlg_template, code=readLines('ssm_nlg_template.cpp'), eval = FALSE, echo = TRUE} ``` diff --git a/vignettes/sde_model.Rmd b/vignettes/sde_model.Rmd index e6be0b8c..a8a9b6a0 100644 --- a/vignettes/sde_model.Rmd +++ b/vignettes/sde_model.Rmd @@ -82,14 +82,15 @@ Finally, we can draw our estimated state trajectory and the the corresponding 95 ```{r} suppressMessages(library("ggplot2")) suppressMessages(library("dplyr")) +suppressMessages(library("diagis")) d <- as.data.frame(out, variable = "states") state_fit <- d %>% group_by(time) %>% - summarise(state = mean(value), - lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) + summarise(state = weighted_mean(value, weight), + lwr = weighted_quantile(value, weight, 0.025), + upr = weighted_quantile(value, weight, 0.975)) ggplot(state_fit, aes(x = time, y = state)) + geom_ribbon(aes(ymin = lwr, ymax = upr), From ba88d4c0b0096672d1cc7d424db5532cf7a14c53 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 18 Nov 2021 20:32:48 +0200 Subject: [PATCH 119/180] no strict argument --- R/check_arguments.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/check_arguments.R b/R/check_arguments.R index e5779d0f..52fb121d 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -422,7 +422,7 @@ check_positive_real <- function(x, name) { #' @rdname check check_theta <- function(x) { - if (!is.numeric(x) || !test_atomic_vector(x, strict = TRUE)) { + if (!is.numeric(x) || !test_atomic_vector(x)) { stop("Argument 'theta' should be a numeric vector.") } if (is.null(names(x))) { From ea6ba46c3e1587910c7d5dc9b27dac30e9f8d170 Mon Sep 17 00:00:00 2001 From: helske Date: Thu, 18 Nov 2021 20:40:20 +0200 Subject: [PATCH 120/180] fix links in Rds --- R/bssm-package.R | 5 +++-- R/particle_smoother.R | 20 +++++++++++--------- R/run_mcmc.R | 8 ++++---- man/exchange.Rd | 2 +- man/negbin_series.Rd | 2 +- man/particle_smoother.Rd | 20 +++++++++++--------- man/run_mcmc.Rd | 8 ++++---- 7 files changed, 35 insertions(+), 30 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index d27b578f..e4ad148f 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -78,7 +78,8 @@ NULL #' @format A vector of length 945. #' @source \url{http://www.ssfpack.com/DKbook.html}. #' @keywords datasets -#' @references James Durbin, Siem Jan Koopman (2012). +#' @references +#' [1] James Durbin, Siem Jan Koopman (2012). #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples @@ -122,7 +123,7 @@ NULL #' @format A time series \code{mts} object with 200 time points and two series. #' @keywords datasets #' @references -#' [3] Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and #' Non-Gaussian State Space Models in R. R Journal (to appear). #' https://arxiv.org/abs/2101.08492 #' diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 441c1187..932768cb 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -1,10 +1,12 @@ #' Particle Smoothing #' #' Function \code{particle_smoother} performs particle smoothing -#' based on either bootstrap particle filter [1], \eqn{\psi}-auxiliary -#' particle filter (\eqn{\psi}-APF) [2], -#' or extended Kalman particle filter [3] (or its iterated version [4]). -#' The smoothing phase is based on the filter-smoother algorithm by [5]. +#' based on either bootstrap particle filter (Gordon et al. 1993), +#' \eqn{\psi}-auxiliary particle filter (\eqn{\psi}-APF) (Vihola et al. 2020), +#' extended Kalman particle filter (Van Der Merwe et al. 2001), +#' or its version based on iterated EKF (Jazwinski, 1970). +#' The smoothing phase is based on the filter-smoother algorithm by +#' Kitagawa (1996). #' #' See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. #' @@ -33,25 +35,25 @@ #' of the states and #' estimated log-likelihood (\code{logLik}). #' @references -#' [1] Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +#' Gordon, NJ, Salmond, DJ, Smith, AFM (1993). #' Novel approach to nonlinear/non-Gaussian Bayesian state estimation. #' IEE Proceedings-F, 140, 107-113. #' https://doi.org/10.1049/ip-f-2.1993.0015 #' -#' [2] Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1-38. #' https://doi.org/10.1111/sjos.12492 #' -#' [3] Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +#' Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). #' The unscented particle filter. #' In Advances in neural information processing systems, p 584-590. #' https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf #' -#' [4] Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +#' Jazwinski, A 1970. Stochastic Processes and Filtering Theory. #' Academic Press. #' -#' [5] Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +#' Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian #' nonlinear state space models. #' Journal of Computational and Graphical Statistics, 5, 1-25. #' https://doi.org/10.2307/1390750 diff --git a/R/run_mcmc.R b/R/run_mcmc.R index a71dc17d..54947771 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -43,7 +43,7 @@ #' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the #' burn-in period in order to find good proposal distribution. #' @param thin Positive integer defining the thinning rate. All MCMC algorithms -#' in \code{bssm} use the jump chain representation (see ref [2]), and the +#' in \code{bssm} use the jump chain representation (see refs), and the #' thinning is applied to these blocks. Defaults to 1. #' For IS-corrected methods, larger value can also be #' statistically more effective. Note: With \code{output_type = "summary"}, @@ -118,15 +118,15 @@ #' is returned by run_mcmc. #' @rdname run_mcmc #' @references -#' [1] Vihola M (2012). Robust adaptive Metropolis algorithm with +#' Vihola M (2012). Robust adaptive Metropolis algorithm with #' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. #' https://doi.org/10.1007/s11222-011-9269-5 #' -#' [2] Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type #' estimators based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' -#' [3] Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and #' Non-Gaussian State Space Models in R. R Journal (to appear). #' https://arxiv.org/abs/2101.08492 #' diff --git a/man/exchange.Rd b/man/exchange.Rd index 10c2c3b0..0f518b6e 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -22,7 +22,7 @@ out <- particle_smoother(model, particles = 500) plot.ts(cbind(model$y, exp(out$alphahat))) } \references{ -James Durbin, Siem Jan Koopman (2012). +\link{1} James Durbin, Siem Jan Koopman (2012). Time Series Analysis by State Space Methods. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 } diff --git a/man/negbin_series.Rd b/man/negbin_series.Rd index a37f5402..53855e0e 100644 --- a/man/negbin_series.Rd +++ b/man/negbin_series.Rd @@ -45,7 +45,7 @@ plot.ts(ts(cbind(sd_level=draws$sd_level, draws$sd_slope), start = 1000), xlab = "Iteration", main = "Traceplots") } \references{ -\link{3} Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. R Journal (to appear). https://arxiv.org/abs/2101.08492 } diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 15ab5919..8a3bd14e 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -87,10 +87,12 @@ estimated log-likelihood (\code{logLik}). } \description{ Function \code{particle_smoother} performs particle smoothing -based on either bootstrap particle filter \link{1}, \eqn{\psi}-auxiliary -particle filter (\eqn{\psi}-APF) \link{2}, -or extended Kalman particle filter \link{3} (or its iterated version \link{4}). -The smoothing phase is based on the filter-smoother algorithm by \link{5}. +based on either bootstrap particle filter (Gordon et al. 1993), +\eqn{\psi}-auxiliary particle filter (\eqn{\psi}-APF) (Vihola et al. 2020), +extended Kalman particle filter (Van Der Merwe et al. 2001), +or its version based on iterated EKF (Jazwinski, 1970). +The smoothing phase is based on the filter-smoother algorithm by +Kitagawa (1996). } \details{ See one of the vignettes for \eqn{\psi}-APF in case of nonlinear models. @@ -108,25 +110,25 @@ ts.plot(out$alphahat, rowMeans(out2), col = 1:2) } \references{ -\link{1} Gordon, NJ, Salmond, DJ, Smith, AFM (1993). +Gordon, NJ, Salmond, DJ, Smith, AFM (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107-113. https://doi.org/10.1049/ip-f-2.1993.0015 -\link{2} Vihola, M, Helske, J, Franks, J. Importance sampling type estimators +Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -\link{3} Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). +Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). The unscented particle filter. In Advances in neural information processing systems, p 584-590. https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf -\link{4} Jazwinski, A 1970. Stochastic Processes and Filtering Theory. +Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press. -\link{5} Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian +Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25. https://doi.org/10.2307/1390750 diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index c140407f..e0ab1d73 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -105,7 +105,7 @@ Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the burn-in period in order to find good proposal distribution.} \item{thin}{Positive integer defining the thinning rate. All MCMC algorithms -in \code{bssm} use the jump chain representation (see ref \link{2}), and the +in \code{bssm} use the jump chain representation (see refs), and the thinning is applied to these blocks. Defaults to 1. For IS-corrected methods, larger value can also be statistically more effective. Note: With \code{output_type = "summary"}, @@ -350,15 +350,15 @@ theme_bw() } \references{ -\link{1} Vihola M (2012). Robust adaptive Metropolis algorithm with +Vihola M (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. https://doi.org/10.1007/s11222-011-9269-5 -\link{2} Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -\link{3} Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. R Journal (to appear). https://arxiv.org/abs/2101.08492 From 374f7861367af6f89b94918ed2ef23050d199e8c Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 19 Nov 2021 16:34:41 +0200 Subject: [PATCH 121/180] sde model, remove Hmisc, add estimate_ess --- DESCRIPTION | 115 ++++++++++++++++++------------------- NAMESPACE | 1 + R/asymptotic_var.R | 99 ++++++++++++++++++++++++++----- R/bssm-package.R | 4 +- R/cpp_example_models.R | 28 +++++---- R/importance_sample.R | 4 +- R/run_mcmc.R | 10 ++-- man/asymptotic_var.Rd | 23 +++++--- man/iact.Rd | 11 ++-- man/importance_sample.Rd | 4 +- man/run_mcmc.Rd | 10 ++-- tests/testthat/test_sde.R | 28 +++++---- vignettes/growth_model.Rmd | 2 +- 13 files changed, 218 insertions(+), 121 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 189d0652..899b4954 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,58 +1,57 @@ -Package: bssm -Type: Package -Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space - Models -Version: 1.1.7 -Authors@R: - c(person(given = "Jouni", - family = "Helske", - role = c("aut", "cre"), - email = "jouni.helske@iki.fi", - comment = c(ORCID = "0000-0001-7130-793X")), - person(given = "Matti", - family = "Vihola", - role = "aut", - comment = c(ORCID = "0000-0002-8041-7222"))) -Description: Efficient methods for Bayesian inference of state space models - via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel - importance sampling type weighted estimators - (Vihola, Helske, and Franks, 2020, ). - Gaussian, Poisson, binomial, negative binomial, and Gamma - observation densities and basic stochastic volatility models - with linear-Gaussian state dynamics, - as well as general non-linear Gaussian models and discretised - diffusion models are supported. -License: GPL (>= 2) -Depends: R (>= 3.5.0) -Suggests: - covr, - ggplot2 (>= 2.0.0), - Hmisc, - KFAS (>= 1.2.1), - knitr (>= 1.11), - MASS, - rmarkdown (>= 0.8.1), - ramcmc, - sde, - sitmo, - testthat -Imports: - magrittr, - checkmate, - coda (>= 0.18-1), - diagis, - dplyr, - posterior, - Rcpp (>= 0.12.3), - tidyr -LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo -SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) -VignetteBuilder: knitr -BugReports: https://github.com/helske/bssm/issues -URL: https://github.com/helske/bssm -ByteCompile: true -Encoding: UTF-8 -NeedsCompilation: yes -RoxygenNote: 7.1.2 -Roxygen: list(markdown = TRUE, - roclets = c("namespace", "rd", "srr::srr_stats_roclet")) +Package: bssm +Type: Package +Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space + Models +Version: 1.1.7 +Authors@R: + c(person(given = "Jouni", + family = "Helske", + role = c("aut", "cre"), + email = "jouni.helske@iki.fi", + comment = c(ORCID = "0000-0001-7130-793X")), + person(given = "Matti", + family = "Vihola", + role = "aut", + comment = c(ORCID = "0000-0002-8041-7222"))) +Description: Efficient methods for Bayesian inference of state space models + via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel + importance sampling type weighted estimators + (Vihola, Helske, and Franks, 2020, ). + Gaussian, Poisson, binomial, negative binomial, and Gamma + observation densities and basic stochastic volatility models + with linear-Gaussian state dynamics, + as well as general non-linear Gaussian models and discretised + diffusion models are supported. +License: GPL (>= 2) +Depends: R (>= 3.5.0) +Suggests: + covr, + ggplot2 (>= 2.0.0), + KFAS (>= 1.2.1), + knitr (>= 1.11), + MASS, + rmarkdown (>= 0.8.1), + ramcmc, + sde, + sitmo, + testthat +Imports: + magrittr, + checkmate, + coda (>= 0.18-1), + diagis, + dplyr, + posterior, + Rcpp (>= 0.12.3), + tidyr +LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo +SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) +VignetteBuilder: knitr +BugReports: https://github.com/helske/bssm/issues +URL: https://github.com/helske/bssm +ByteCompile: true +Encoding: UTF-8 +NeedsCompilation: yes +RoxygenNote: 7.1.2 +Roxygen: list(markdown = TRUE, + roclets = c("namespace", "rd", "srr::srr_stats_roclet")) diff --git a/NAMESPACE b/NAMESPACE index b302d676..5a0db706 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -48,6 +48,7 @@ export(ekf) export(ekf_fast_smoother) export(ekf_smoother) export(ekpf_filter) +export(estimate_ess) export(expand_sample) export(fast_smoother) export(gamma) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 3a7ca7b9..2fa4b66e 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -1,6 +1,8 @@ #' Integrated Autocorrelation Time #' -#' Estimates the integrated autocorrelation time based on Sokal (1997). +#' Estimates the integrated autocorrelation time (IACT) based on Sokal (1997). +#' Note that the estimator is not particularly good for very short series x +#' (say < 100), but that is not very practical for MCMC applications anyway. #' #' @param x A vector. #' @references @@ -13,38 +15,46 @@ #' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) -#' x <- numeric(1e4) +#' n <- 1e4 +#' x <- numeric(n) #' phi <- 0.8 -#' for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) -#' # ESS: +#' for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +#' # ESS estimate: #' length(x) / iact(x) iact <- function(x) { n <- length(x) x_ <- (x - mean(x)) / sd(x) - C <- max(5.0, log10(n)) + C <- max(5, log10(n)) tau <- 1 for (k in 1:(n - 1)) { - tau <- tau + 2.0 * (x_[1:(n-k)] %*% x_[(1+k):n]) / (n - k) + tau <- tau + 2.0 * (x_[1:(n - k)] %*% x_[(1 + k):n]) / (n - k) if (k > C * tau) break } - max(0.0, tau) + max(0, tau) } #' Asymptotic Variance of IS-type Estimators #' -#' Estimates the asymptotic variance based on Corollary 1 +#' The asymptotic variance MCMCSE^2 is based on Corollary 1 #' of Vihola et al. (2020) from weighted samples from IS-MCMC. The default #' method is based on the integrated autocorrelation time (IACT) by Sokal (1997) #' which seem to work well for reasonable problems, but it is also possible to #' use the Geyer's method as implemented in \code{ess_basic} of the #' \code{posterior} package, or the improved \code{ess_bulk} method of the same -#' package (these compute ESS instead of variances, but -#' MCSE^2 = var(x) / ESS = var(x) * IACT / length(x)). +#' package. These effective sample sizes (ESS) can be converted to MCMCSE^2 as +#' MCMCSE(x)^2 = (var(z) * iact(z) / c^2) / n where z = w*(x-) +#' +#' (var(z) * iact(z) / estimate_c^2) / length(z) +#' +#' var(x) / ESS = var(x) * IACT / n) where var(x) is the +#' variance of x based on n i.i.d.samples. #' #' @importFrom posterior ess_basic ess_bulk #' @param x Vector of samples. #' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is #' assumed). -#' @param method Method for computing the IACT. Default is \code{"sokal"} +#' @param method Method for computing the IACT. Default is \code{"sokal"}, +#' other options are \code{ess_basic} and \code{ess_bulk} which use the +#' corresponding functions of the \code{posterior} package. #' @references #' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. @@ -64,10 +74,11 @@ iact <- function(x) { #' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) -#' x <- numeric(1e4) +#' n <- 1e4 +#' x <- numeric(n) #' phi <- 0.7 -#' for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) -#' w <- rexp(1e4, 0.5 * exp(0.001 * x^2)) +#' for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +#' w <- rexp(n, 0.5 * exp(0.001 * x^2)) #' # different methods: #' asymptotic_var(x, w, method = "sokal") #' asymptotic_var(x, w, method = "ess_basic") @@ -85,7 +96,65 @@ asymptotic_var <- function(x, w, method = "sokal") { estimate_mean <- weighted_mean(x, w) z <- w * (x - estimate_mean) switch(method, - sokal = var(z) * iact(z) / length(z) / estimate_c^2, + sokal = (var(z) * iact(z) / estimate_c^2) / length(z), + # ESS(z) = n / IACT(z) ess_basic = var(z) / ess_basic(z) / estimate_c^2, ess_bulk = var(z) / ess_bulk(z) / estimate_c^2) } + +#' Effective Sample Size for IS-type Estimators +#' +#' Computes the effective sample size (ESS) based on weighted posterior +#' samples. +#' +#' The asymptotic variance MCMCSE^2 is based on Corollary 1 of +#' Vihola et al. (2020) which is used to compute an estimate for the ESS +#' using the identity ESS(x) = var(x) / MCMCSE^2 where var(x) is the +#' posterior variance of x assuming independent samples. +#' +#' @param x Vector of samples. +#' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is +#' assumed). +#' @param method Method for computing the ESS. Default is \code{"sokal"}, other +#' options are \code{ess_basic} and \code{ess_bulk} which use the corresponding +#' functions of the \code{posterior} package in computation of the asymptotic +#' variance (see also \code{asymptotic_var}). +#' @references +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 +#' +#' Sokal A. (1997). Monte Carlo Methods in Statistical Mechanics: Foundations +#' and New Algorithms. +#' In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. +#' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +#' https://doi.org/10.1007/978-1-4899-0319-8_6 +#' +#' Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). +#' Rank-normalization, folding, and localization: An improved Rhat for +#' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. +#' https://doi.org/10.1214/20-BA1221 +#' @export +#' @srrstats {BS5.3, BS5.5} +#' @examples +#' set.seed(1) +#' n <- 1e4 +#' x <- numeric(n) +#' phi <- 0.7 +#' for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +#' w <- rexp(n, 0.5 * exp(0.001 * x^2)) +#' # different methods: +#' estimate_ess(x, w, method = "sokal") +#' estimate_ess(x, w, method = "ess_basic") +#' estimate_ess(x, w, method = "ess_bulk") +#' +estimate_ess <- function(x, w, method = "sokal") { + method <- match.arg(method, c("sokal", "ess_basic", "ess_bulk")) + if (missing(w)) w <- rep(1, length(x)) + if(any(w < 0) | any(!is.finite(w))) + stop("Nonfinite or negative weights in 'w'.") + if (!any(w > 0)) { + stop("No positive weights in 'w'.") + } + weighted_var(x, w) / asymptotic_var(x, w, method = method) +} \ No newline at end of file diff --git a/R/bssm-package.R b/R/bssm-package.R index e4ad148f..1c69d909 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -95,7 +95,7 @@ NULL #' See example for code for reproducing the data. This was used in #' Vihola, Helske, Franks (2020). #' -#' @srrstats {G5.0, G5.1} used in Vihola, Helske, Franks (2020). +#' @srrstats {G5.0, G5.1, G5.4} used in Vihola, Helske, Franks (2020). #' @name poisson_series #' @docType data #' @format A vector of length 100. @@ -117,7 +117,7 @@ NULL #' See example for code for reproducing the data. This was used in #' Helske and Vihola (2021). #' -#' @srrstats {G5.0, G5.1} used in Helske and Vihola (2021). +#' @srrstats {G5.0, G5.1, G5.4} used in Helske and Vihola (2021). #' @name negbin_series #' @docType data #' @format A time series \code{mts} object with 200 time points and two series. diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 174fa83b..dfed282a 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -7,6 +7,8 @@ #' @return Returns pointers to the C++ snippets defining the model, or in case #' of \code{return_code = TRUE}, returns the example code without compiling. #' @export +#' @srrstats {G5.4} sde_gbm model used in Vihola, Helske, Franks, (2020). See +#' also tests/testthat/test_sde.R. #' @examples #' cpp_example_model("sde_poisson_OU", return_code = TRUE) #' @@ -109,8 +111,8 @@ cpp_example_model <- function(example, return_code = FALSE) { // theta: vector of parameters // theta(0) = mu - // theta(1) = log(sigma_x) - // theta(2) = log(sigma_y) + // theta(1) = sigma_x + // theta(2) = sigma_y #include // [[Rcpp::depends(RcppArmadillo)]] @@ -124,22 +126,28 @@ cpp_example_model <- function(example, return_code = FALSE) { // diffusion function // [[Rcpp::export]] double diffusion(const double x, const arma::vec& theta) { - return std::max(0.0, exp(theta(1)) * x); + return std::max(0.0, theta(1) * x); } // Derivative of the diffusion function // [[Rcpp::export]] double ddiffusion(const double x, const arma::vec& theta) { - return exp(theta(1)) * (x > 0.0); + return theta(1) * (x > 0.0); } // log-density of the prior - // Note that these differ from the ones in the paper // [[Rcpp::export]] double log_prior_pdf(const arma::vec& theta) { - // remember, dgamma is shape and scale in C side - double log_pdf = R::dnorm(theta(0), 0, 0.5, 1) + - R::dgamma(exp(theta(1)), 2, 1, 1) + theta(1); - R::dgamma(exp(theta(2)), 2, 1, 1) + theta(2); + + double log_pdf = 0.0; + + if(theta(0) < 0 || theta(1) < 0 || theta(2) < 0.5) { + log_pdf = -std::numeric_limits::infinity(); + } + else { + log_pdf = R::dnorm(theta(0), 0, 0.1, 1) + + R::dnorm(theta(1), 0, 0.5, 1) + + R::dnorm(theta(2), 1.5, 0.5, 1); + } return log_pdf; } @@ -151,7 +159,7 @@ cpp_example_model <- function(example, return_code = FALSE) { arma::vec log_pdf(alpha.n_elem); for (unsigned int i = 0; i < alpha.n_elem; i++) { - log_pdf(i) = R::dnorm(y, log(alpha(i)), exp(theta(2)), 1); + log_pdf(i) = R::dnorm(y, log(alpha(i)), theta(2), 1); } return log_pdf; } diff --git a/R/importance_sample.R b/R/importance_sample.R index 71395969..afae975c 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -26,8 +26,8 @@ #' #' est <- matrix(NA, 3, nrow(sexratio)) #' for(i in 1:ncol(est)) { -#' est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, -#' prob = c(0.05,0.5,0.95), normwt=TRUE) +#' est[, i] <- diagis::weighted_quantile(exp(imp$alpha[i, 1, ]), imp$weights, +#' prob = c(0.05,0.5,0.95)) #' } #' #' ts.plot(t(est),lty = c(2,1,2)) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 54947771..fa2f1313 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -302,11 +302,11 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' level_sumr <- d_states %>% #' filter(variable == "level") %>% #' group_by(time) %>% -#' summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), -#' lwr = Hmisc::wtd.quantile(value, weight, -#' 0.025, normwt = TRUE), -#' upr = Hmisc::wtd.quantile(value, weight, -#' 0.975, normwt = TRUE)) +#' summarise(mean = diagis::weighted_mean(value, weight), +#' lwr = diagis::weighted_quantile(value, weight, +#' 0.025), +#' upr = diagis::weighted_quantile(value, weight, +#' 0.975)) #' #' # visualize #' level_sumr %>% ggplot(aes(x = time, y = mean)) + diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd index b23d9d25..acad616f 100644 --- a/man/asymptotic_var.Rd +++ b/man/asymptotic_var.Rd @@ -12,24 +12,33 @@ asymptotic_var(x, w, method = "sokal") \item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is assumed).} -\item{method}{Method for computing the IACT. Default is \code{"sokal"}} +\item{method}{Method for computing the IACT. Default is \code{"sokal"}, +other options are \code{ess_basic} and \code{ess_bulk} which use the +corresponding functions of the \code{posterior} package.} } \description{ -Estimates the asymptotic variance based on Corollary 1 +The asymptotic variance MCMCSE^2 is based on Corollary 1 of Vihola et al. (2020) from weighted samples from IS-MCMC. The default method is based on the integrated autocorrelation time (IACT) by Sokal (1997) which seem to work well for reasonable problems, but it is also possible to use the Geyer's method as implemented in \code{ess_basic} of the \code{posterior} package, or the improved \code{ess_bulk} method of the same -package (these compute ESS instead of variances, but -MCSE^2 = var(x) / ESS = var(x) * IACT / length(x)). +package. These effective sample sizes (ESS) can be converted to MCMCSE^2 as +MCMCSE(x)^2 = (var(z) * iact(z) / c^2) / n where z = w*(x-) +} +\details{ +(var(z) * iact(z) / estimate_c^2) / length(z) + +var(x) / ESS = var(x) * IACT / n) where var(x) is the +variance of x based on n i.i.d.samples. } \examples{ set.seed(1) -x <- numeric(1e4) +n <- 1e4 +x <- numeric(n) phi <- 0.7 -for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) -w <- rexp(1e4, 0.5 * exp(0.001 * x^2)) +for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +w <- rexp(n, 0.5 * exp(0.001 * x^2)) # different methods: asymptotic_var(x, w, method = "sokal") asymptotic_var(x, w, method = "ess_basic") diff --git a/man/iact.Rd b/man/iact.Rd index f5e75bfb..98978e4e 100644 --- a/man/iact.Rd +++ b/man/iact.Rd @@ -10,14 +10,17 @@ iact(x) \item{x}{A vector.} } \description{ -Estimates the integrated autocorrelation time based on Sokal (1997). +Estimates the integrated autocorrelation time (IACT) based on Sokal (1997). +Note that the estimator is not particularly good for very short series x +(say < 100), but that is not very practical for MCMC applications anyway. } \examples{ set.seed(1) -x <- numeric(1e4) +n <- 1e4 +x <- numeric(n) phi <- 0.8 -for(t in 2:1e4) x[t] <- phi * x[t-1] + rnorm(1) -# ESS: +for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +# ESS estimate: length(x) / iact(x) } \references{ diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index 92ec2a22..a814c153 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -53,8 +53,8 @@ imp <- importance_sample(model, nsim = 1000) est <- matrix(NA, 3, nrow(sexratio)) for(i in 1:ncol(est)) { - est[, i] <- Hmisc::wtd.quantile(exp(imp$alpha[i, 1, ]), imp$weights, - prob = c(0.05,0.5,0.95), normwt=TRUE) + est[, i] <- diagis::weighted_quantile(exp(imp$alpha[i, 1, ]), imp$weights, + prob = c(0.05,0.5,0.95)) } ts.plot(t(est),lty = c(2,1,2)) diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index e0ab1d73..8e404193 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -287,11 +287,11 @@ library("ggplot2") level_sumr <- d_states \%>\% filter(variable == "level") \%>\% group_by(time) \%>\% - summarise(mean = Hmisc::wtd.mean(value, weight, normwt = TRUE), - lwr = Hmisc::wtd.quantile(value, weight, - 0.025, normwt = TRUE), - upr = Hmisc::wtd.quantile(value, weight, - 0.975, normwt = TRUE)) + summarise(mean = diagis::weighted_mean(value, weight), + lwr = diagis::weighted_quantile(value, weight, + 0.025), + upr = diagis::weighted_quantile(value, weight, + 0.975)) # visualize level_sumr \%>\% ggplot(aes(x = time, y = mean)) + diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index aa800318..bfe2796e 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -1,30 +1,38 @@ context("Test SDE") +#' @srrstats {G5.0, G5.1, G5.4, G5.4a, G5.4b, G5.4c} GBM model and data as in +#' Vihola, Helske, Franks (2020) test_that("MCMC for SDE works", { skip_on_cran() pntrs <- cpp_example_model("sde_gbm") - set.seed(1) + set.seed(42) n <- 50 dt <- 1 mu <- 0.05 sigma_x <- 0.3 sigma_y <- 1 - x <- numeric(n) - x[1] <- 1 - for (k in 2:n) { - x[k] <- x[k-1] * exp((mu - 0.5 * sigma_x^2) * dt + + x <- 1 + y <- numeric(n) + for (k in 1:n) { + x <- x * exp((mu - 0.5 * sigma_x^2) * dt + sqrt(dt) * rnorm(1, sd = sigma_x)) + y[k] <- rnorm(1, log(x), sigma_y) } - y <- rnorm(n, log(x), sigma_y) - + model <- ssm_sde(y, pntrs$drift, pntrs$diffusion, pntrs$ddiffusion, pntrs$obs_density, - pntrs$prior, c(mu = 0.08, log_sigma_x = 0.4, sigma_y = 1.5), + pntrs$prior, c(mu = 0.08, sigma_x = 0.4, sigma_y = 1.5), x0 = 1, positive = TRUE) - expect_error(out <- run_mcmc(model, iter = 2e4, + expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, particles = 50, mcmc_type = "is2", - L_c = 4, L_f = 6, threads = 2), NA) + L_c = 2, L_f = 6, threads = 2), NA) + + paper <- c(0.053, 0.253, 1.058, 1.254, 2.960) + expect_equivalent(diagis::weighted_mean(out$theta, out$weights * out$counts), + paper[1:3], tol = 0.1) + expect_equivalent(diagis::weighted_mean(t(out$alpha[c(1,50),1,]), + out$weights * out$counts), paper[4:5], tol = 0.01) expect_error(bootstrap_filter(model, 1000, L = -2)) expect_error(ll <- logLik(model, 10000, L = 3), NA) diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 4010ac12..62d40101 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -10,7 +10,7 @@ vignette: | %\VignetteIndexEntry{Non-linear models with bssm} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} - %\VignetteDepends{ggplot2, dplyr, Hmisc} + %\VignetteDepends{ggplot2, dplyr} --- ```{r, echo = FALSE} From bc9387feecac1fb2c079367cbc6c87bbc8bd35f9 Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 19 Nov 2021 18:05:16 +0200 Subject: [PATCH 122/180] more tests --- R/approx.R | 6 ++-- R/bootstrap_filter.R | 16 ++++----- R/check_arguments.R | 2 +- R/ekpf_filter.R | 4 +-- R/expand_sample.R | 7 ++-- R/importance_sample.R | 6 ++-- R/kfilter.R | 2 +- R/loglik.R | 12 +++---- R/particle_smoother.R | 22 ++++++------ R/post_correction.R | 10 +++--- R/predict.R | 4 +-- R/priors.R | 13 ++++--- R/run_mcmc.R | 56 ++++++++++++++--------------- R/sim_smoother.R | 8 ++--- R/smoother.R | 4 +-- man/check.Rd | 4 +-- man/estimate_ess.Rd | 58 ++++++++++++++++++++++++++++++ tests/testthat/test_basics.R | 6 +++- tests/testthat/test_ekpf.R | 3 ++ tests/testthat/test_mcmc.R | 39 +++++++++++++++++++- tests/testthat/test_post_correct.R | 14 ++++++++ tests/testthat/test_predict.R | 14 +++++++- tests/testthat/test_priors.R | 18 ++++++++++ tests/testthat/test_sde.R | 17 +++++++++ tests/testthat/test_sim_smoother.R | 4 +++ 25 files changed, 261 insertions(+), 88 deletions(-) create mode 100644 man/estimate_ess.Rd create mode 100644 tests/testthat/test_priors.R diff --git a/R/approx.R b/R/approx.R index 90799fbb..f3ef6ab5 100644 --- a/R/approx.R +++ b/R/approx.R @@ -50,7 +50,7 @@ gaussian_approx.nongaussian <- function(model, max_iter = 100, check_missingness(model) - model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") model$distribution <- pmatch(model$distribution, @@ -86,9 +86,9 @@ gaussian_approx.ssm_nlg <- function(model, max_iter = 100, check_missingness(model) - model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") - model$iekf_iter <- check_integer(iekf_iter, "iekf_iter") + model$iekf_iter <- check_intmax(iekf_iter, "iekf_iter") out <- gaussian_approx_model_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 28e9dce4..568afc62 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -45,8 +45,8 @@ bootstrap_filter.gaussian <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + particles <- check_intmax(particles, "particles") + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles @@ -90,8 +90,8 @@ bootstrap_filter.nongaussian <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + particles <- check_intmax(particles, "particles") + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles if (particles > 100 & nsamples > 1e12) { @@ -129,7 +129,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles @@ -137,7 +137,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, @@ -174,14 +174,14 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- length(model$y) * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, diff --git a/R/check_arguments.R b/R/check_arguments.R index 52fb121d..a11c7078 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -401,7 +401,7 @@ check_H <- function(x, p, n, multivariate = FALSE) { } #' @rdname check -check_integer <- function(x, name = "particles", positive = TRUE, max = 1e7) { +check_intmax <- function(x, name = "particles", positive = TRUE, max = 1e7) { if (!test_count(x, positive)) { stop(paste0("Argument '", name, "' should be a ", ifelse(positive, "positive", "non-negative"), " integer. ")) diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 8f1aa18e..eab96f71 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -56,7 +56,7 @@ ekpf_filter.ssm_nlg <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles @@ -65,7 +65,7 @@ ekpf_filter.ssm_nlg <- function(model, particles, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) out <- ekpf(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/expand_sample.R b/R/expand_sample.R index 53dbd549..41233adf 100644 --- a/R/expand_sample.R +++ b/R/expand_sample.R @@ -54,15 +54,16 @@ expand_sample <- function(x, variable = "theta", times, states, if (missing(times)) { times <- seq_len(nrow(x$alpha)) } else { - if (!check_integer(times) || any(times < 1) || any(times > nrow(x$alpha))) + if (!test_integerish(times, lower = 1, upper = nrow(x$alpha), + any.missing = FALSE, unique = TRUE)) stop(paste0("Argument 'times' should contain indices between 1 and ", nrow(x$alpha),".")) } if (missing(states)) { states <- seq_len(ncol(x$alpha)) } else { - if (!check_integer(states) || any(states < 1) || - any(states > ncol(x$alpha))) + if (!test_integerish(states, lower = 1, upper = ncol(x$alpha), + any.missing = FALSE, unique = TRUE)) stop(paste0("Argument 'states' should contain indices between 1 and ", ncol(x$alpha),".")) } diff --git a/R/importance_sample.R b/R/importance_sample.R index afae975c..ec94bc36 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -45,9 +45,9 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, check_missingness(model) - model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") - nsim <- check_integer(nsim, "nsim") + nsim <- check_intmax(nsim, "nsim") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * nsim @@ -55,7 +55,7 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, warning(paste("Trying to sample ", nsamples, "values, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 diff --git a/R/kfilter.R b/R/kfilter.R index a83cf065..073be2a8 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -99,7 +99,7 @@ ekf <- function(model, iekf_iter = 0) { check_missingness(model) - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) out <- ekf_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/loglik.R b/R/loglik.R index 83c6d6ea..a415a12b 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -74,7 +74,7 @@ logLik.nongaussian <- function(object, particles, method = "psi", check_missingness(object) - object$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + object$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) object$conv_tol <- check_positive_real(conv_tol, "conv_tol") if (missing(particles)) { @@ -117,13 +117,13 @@ logLik.ssm_nlg <- function(object, particles, method = "bsf", method <- match.arg(method, c("psi", "bsf", "ekf")) if (method == "bsf" && particles == 0) - stop("'particles' must be positive for bootstrap particle filter.") + stop("'particles' must be positive for bootstrap filter.") method <- pmatch(method, c("psi", "bsf", NA, "ekf")) - max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) nonlinear_loglik(t(object$y), object$Z, object$H, object$T, object$R, object$Z_gn, object$T_gn, object$a1, object$P1, @@ -150,7 +150,7 @@ logLik.ssm_sde <- function(object, particles, L, particles <- nsim } } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) loglik_sde(object$y, object$x0, object$positive, object$drift, object$diffusion, object$ddiffusion, diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 932768cb..9643b43c 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -92,7 +92,7 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles @@ -100,7 +100,7 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (method == "psi") { out <- list() @@ -148,16 +148,16 @@ particle_smoother.nongaussian <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") method <- match.arg(tolower(method), c("bsf", "psi")) @@ -199,7 +199,7 @@ particle_smoother.ssm_nlg <- function(model, particles, particles <- nsim } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles @@ -207,10 +207,10 @@ particle_smoother.ssm_nlg <- function(model, particles, warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) - max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) + max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) method <- match.arg(tolower(method), c("bsf", "psi", "ekf")) @@ -263,13 +263,13 @@ particle_smoother.ssm_sde <- function(model, particles, L, particles <- nsim } } - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") nsamples <- length(model$y) * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_smoother_sde(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, diff --git a/R/post_correction.R b/R/post_correction.R index 0de4b305..ba21389a 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -83,8 +83,8 @@ suggest_N <- function(model, theta, check_missingness(model) - replications <- check_integer(replications, "replications") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + replications <- check_intmax(replications, "replications") + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_integerish(candidates, lower = 1, any.missing = FALSE, min.len = 1)) { @@ -239,10 +239,10 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, check_missingness(model) - particles <- check_integer(particles, "particles") - threads <- check_integer(threads, "threads") + particles <- check_intmax(particles, "particles") + threads <- check_intmax(threads, "threads") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") diff --git a/R/predict.R b/R/predict.R index d69abb04..0ccb1f0d 100644 --- a/R/predict.R +++ b/R/predict.R @@ -124,8 +124,8 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", if (!inherits(model, "bbsm_model")) { stop("Argument 'model' should be an object of class 'bssm_model'.") } - nsim <- check_integer(nsim, "nsim") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + nsim <- check_intmax(nsim, "nsim", max = 10 * object$iter) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") diff --git a/R/priors.R b/R/priors.R index 3e93ba87..c3854d94 100644 --- a/R/priors.R +++ b/R/priors.R @@ -155,9 +155,14 @@ tnormal_prior <- function(init, mean, sd, min = -Inf, max = Inf) { if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } + if (init < min | init > max) { + stop(paste("Initial value for parameter with truncated Normal is not", + "between the lower and upper bounds.", sep = " ")) + } + if (any(sd < 0)) { - stop(paste("Standard deviation parameter for Normal distribution must be", - "positive.", sep = " ")) + stop(paste("Standard deviation parameter for truncated Normal distribution", + "must be positive.", sep = " ")) } n <- max(length(init), length(mean), length(sd)) @@ -185,10 +190,10 @@ gamma_prior <- function(init, shape, rate) { if (any(!is.numeric(init), !is.numeric(shape), !is.numeric(rate))) { stop("Parameters for priors must be numeric.") } - if (any(shape < 0)) { + if (!all(shape > 0)) { stop("Shape parameter for Gamma distribution must be positive.") } - if (any(rate < 0)) { + if (!all(rate > 0)) { stop("Rate parameter for Gamma distribution must be positive.") } n <- max(length(init), length(shape), length(rate)) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index fa2f1313..c229e02a 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -171,12 +171,12 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - threads <- check_integer(threads, "threads") - thin <- check_integer(thin, "thin", max = 100) - iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_integer(burnin, "burnin", max = 1e12) + threads <- check_intmax(threads, "threads") + thin <- check_intmax(thin, "thin", max = 100) + iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_intmax(burnin, "burnin", max = 1e12) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -376,7 +376,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -384,17 +384,17 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", warning(paste("Argument `nsim` is deprecated. Use argument `particles`", "instead.", sep = " ")) particles <- nsim - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") } } else { - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") } - threads <- check_integer(threads, "threads") - model$max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + threads <- check_intmax(threads, "threads") + model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") - thin <- check_integer(thin, "thin", max = 100) - iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_integer(burnin, "burnin", max = 1e12) + thin <- check_intmax(thin, "thin", max = 100) + iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_intmax(burnin, "burnin", max = 1e12) if (!test_flag(local_approx)) { stop("Argument 'local_approx' should be TRUE or FALSE. ") } else model$local_approx <- local_approx @@ -536,7 +536,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -544,19 +544,19 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", warning(paste("Argument `nsim` is deprecated. Use argument `particles`", "instead.", sep = " ")) particles <- nsim - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") } } else { - particles <- check_integer(particles, "particles") + particles <- check_intmax(particles, "particles") } - threads <- check_integer(threads, "threads") - max_iter <- check_integer(max_iter, "max_iter", positive = FALSE) + threads <- check_intmax(threads, "threads") + max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") - thin <- check_integer(thin, "thin", max = 100) - iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_integer(burnin, "burnin", max = 1e12) - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + thin <- check_intmax(thin, "thin", max = 100) + iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_intmax(burnin, "burnin", max = 1e12) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -699,7 +699,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) @@ -709,11 +709,11 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", particles <- nsim } } - particles <- check_integer(particles, "particles") - threads <- check_integer(threads, "threads") - thin <- check_integer(thin, "thin", max = 100) - iter <- check_integer(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_integer(burnin, "burnin", max = 1e12) + particles <- check_intmax(particles, "particles") + threads <- check_intmax(threads, "threads") + thin <- check_intmax(thin, "thin", max = 100) + iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) + burnin <- check_intmax(burnin, "burnin", max = 1e12) if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 5b375fec..83b46e45 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -34,8 +34,8 @@ sim_smoother.gaussian <- function(model, nsim = 1, check_missingness(model) - nsim <- check_integer(nsim, "nsim") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + nsim <- check_intmax(nsim, "nsim") + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) stop("Argument 'use_antithetic' should be TRUE or FALSE. ") @@ -50,8 +50,8 @@ sim_smoother.gaussian <- function(model, nsim = 1, sim_smoother.nongaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { - nsim <- check_integer(nsim, "nsim") - seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) + nsim <- check_intmax(nsim, "nsim") + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) stop("Argument 'use_antithetic' should be TRUE or FALSE. ") diff --git a/R/smoother.R b/R/smoother.R index 6895b8be..882edd61 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -124,7 +124,7 @@ ekf_smoother <- function(model, iekf_iter = 0) { check_missingness(model) - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) out <- ekf_smoother_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, @@ -145,7 +145,7 @@ ekf_fast_smoother <- function(model, iekf_iter = 0) { check_missingness(model) - iekf_iter <- check_integer(iekf_iter, "iekf_iter", positive = FALSE) + iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) out <- ekf_fast_smoother_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/man/check.Rd b/man/check.Rd index 07dccf92..9c49fda1 100644 --- a/man/check.Rd +++ b/man/check.Rd @@ -22,7 +22,7 @@ \alias{check_a1} \alias{check_P1} \alias{check_H} -\alias{check_integer} +\alias{check_intmax} \alias{check_positive_real} \alias{check_theta} \alias{check_missingness} @@ -70,7 +70,7 @@ check_P1(x, m) check_H(x, p, n, multivariate = FALSE) -check_integer(x, name = "particles", positive = TRUE, max = 1e+07) +check_intmax(x, name = "particles", positive = TRUE, max = 1e+07) check_positive_real(x, name) diff --git a/man/estimate_ess.Rd b/man/estimate_ess.Rd new file mode 100644 index 00000000..0d0bc821 --- /dev/null +++ b/man/estimate_ess.Rd @@ -0,0 +1,58 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/asymptotic_var.R +\name{estimate_ess} +\alias{estimate_ess} +\title{Effective Sample Size for IS-type Estimators} +\usage{ +estimate_ess(x, w, method = "sokal") +} +\arguments{ +\item{x}{Vector of samples.} + +\item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is +assumed).} + +\item{method}{Method for computing the ESS. Default is \code{"sokal"}, other +options are \code{ess_basic} and \code{ess_bulk} which use the corresponding +functions of the \code{posterior} package in computation of the asymptotic +variance (see also \code{asymptotic_var}).} +} +\description{ +Computes the effective sample size (ESS) based on weighted posterior +samples. +} +\details{ +The asymptotic variance MCMCSE^2 is based on Corollary 1 of +Vihola et al. (2020) which is used to compute an estimate for the ESS +using the identity ESS(x) = var(x) / MCMCSE^2 where var(x) is the +posterior variance of x assuming independent samples. +} +\examples{ +set.seed(1) +n <- 1e4 +x <- numeric(n) +phi <- 0.7 +for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) +w <- rexp(n, 0.5 * exp(0.001 * x^2)) +# different methods: +estimate_ess(x, w, method = "sokal") +estimate_ess(x, w, method = "ess_basic") +estimate_ess(x, w, method = "ess_bulk") + +} +\references{ +Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. +Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 + +Sokal A. (1997). Monte Carlo Methods in Statistical Mechanics: Foundations +and New Algorithms. +In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. +NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. +https://doi.org/10.1007/978-1-4899-0319-8_6 + +Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). +Rank-normalization, folding, and localization: An improved Rhat for +assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. +https://doi.org/10.1214/20-BA1221 +} diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index b30b243a..22f53845 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -7,7 +7,11 @@ test_that("results for Gaussian models are comparable to KFAS", { model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(0.01^2, 0)), H = 2) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - + + expect_error(bsm_lg(1:10, P1 = diag(1e2, 2), sd_slope = 0, + sd_level = 0.01)) + expect_error(bsm_lg(1:10, P1 = diag(1e2, 2), sd_slope = 0, + sd_y = 0.01)) model_bssm <- bsm_lg(1:10, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, sd_y = sqrt(2)) diff --git a/tests/testthat/test_ekpf.R b/tests/testthat/test_ekpf.R index c647f124..fe5e00c2 100644 --- a/tests/testthat/test_ekpf.R +++ b/tests/testthat/test_ekpf.R @@ -78,5 +78,8 @@ test_that("EKF and IEKF work", { ekf_smoother(model_nlg, iekf_iter = 2)$alphahat) expect_error(ukf(model_nlg), NA) + expect_error(ukf(model_nlg, alpha = -1)) + expect_error(ukf(model_nlg, beta = -1)) + expect_error(ukf(model_nlg, kappa = -1)) expect_error(bootstrap_filter(model_nlg, 10), NA) }) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 04b55207..3408bad8 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -102,7 +102,24 @@ test_that("scaling is linear", { }) - +test_that("run_mcmc throws error with improper arguments", { + set.seed(123) + model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, + sd_y = uniform(1, 0, 10), + sd_level = uniform(1, 0, 10)) + + expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, + end_adaptive_phase = 4), NA) + expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, + local_approx = 4), NA) + expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, + particls = 1), NA) + out <- run_mcmc(model_bssm, iter = 10, output_type = "theta") + expect_error(summary(out, return_se = 2)) + expect_error(summary(out, only_theta = 2)) + expect_error(summary(out, variable = "both")) +}) + test_that("MCMC results for Gaussian model are correct", { set.seed(123) model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, @@ -176,6 +193,9 @@ test_that("MCMC results for Gaussian model are correct", { expect_lt(max(out$theta), Inf) expect_true(is.finite(sum(out$alpha))) + model2 <- ssm_ulg(y, Z, H, T, R, a1, P1) + expect_error(run_mcmc(model2, iter = 50)) + expect_equal( run_mcmc(model, iter = 100, seed = 1)[-14], run_mcmc(model, iter = 100, seed = 1)[-14]) @@ -266,6 +286,15 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { states <- expand_sample(mcmc_poisson, variable = "states") + expect_error(expand_sample(mcmc_poisson, variable = "blaablaa")) + expect_error(expand_sample(mcmc_poisson, variable = "states", by_states = 2)) + expect_error(expand_sample(mcmc_poisson, variable = "states", times = 0)) + expect_error(expand_sample(mcmc_poisson, variable = "states", times = 1:100)) + expect_error(expand_sample(mcmc_poisson, variable = "states", states = 0)) + expect_error(expand_sample(mcmc_poisson, variable = "states", states = "a")) + expect_error(expand_sample(mcmc_poisson, variable = "states", + states = list(4))) + expect_equal(as.numeric(sumr$states$Mean[,1]), as.numeric(colMeans(states$level))) @@ -288,6 +317,8 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { output_type = "theta", particles = 5)[-13 - z]) } + expect_error(expand_sample(run_mcmc(model_bssm, iter = 100, seed = 1, + output_type = "theta", mcmc_type = "approx"), variable = "states")) }) @@ -319,8 +350,14 @@ test_that("MCMC using SPDK for Gamma model works", { test_that("MCMC results for SV model using IS-correction are correct", { set.seed(123) + + expect_error(svm(rnorm(10), rho = uniform(0.95, -0.999, 0.999), + sd_ar = halfnormal(1, 5), mu = 4, sigma = halfnormal(1, 2))) + expect_error(model_bssm <- svm(rnorm(10), rho = uniform(0.95, -0.999, 0.999), sd_ar = halfnormal(1, 5), sigma = halfnormal(1, 2)), NA) + expect_error(logLik(model_bssm, particles = 0, method = "bsf")) + expect_equal(run_mcmc(model_bssm, iter = 100, particles = 10, mcmc_type = "is1", seed = 1)[-16], diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index 0ee8e247..a332c491 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -33,6 +33,18 @@ test_that("Test post correction for AR1 model", { expect_identical(estN$N, 5) + + expect_error(post_correct(data.frame(1), out_approx, particles = estN$N, + threads = 2)) + expect_error(post_correct(model, out_approx, particles = estN$N, + threads = 2, particles = 1e12)) + expect_error(post_correct(model, out_approx, particles = estN$N, + threads = 2, particles = 10, theta = diag(2))) + expect_error(post_correct(model, out_approx, particles = estN$N, + threads = 2, particles = 10, theta = rep(1:6))) + expect_error(post_correct(model, 1:5, particles = estN$N, + threads = 2)) + # Can't really test for correctness with limited time expect_error(out_is2 <- post_correct(model, out_approx, particles = estN$N, threads = 2), NA) @@ -41,6 +53,8 @@ test_that("Test post correction for AR1 model", { expect_lt(sum(out_is2$Vt), Inf) expect_lt(max(out_is2$weights), Inf) expect_gt(max(out_is2$weights), 0) + + }) test_that("Test post correction for non-linear model", { diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index daf10d68..cd946908 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -68,9 +68,21 @@ test_that("Gaussian predictions work", { future = FALSE, nsim = 100), NA) expect_equal(yrep, yrep2) expect_equal(meanrep, meanrep2) + expect_error(predict(mcmc_results2, model, type = "response", future = FALSE, nsim = 100)) - + expect_error(predict(mcmc_results2, model2, type = "response", + future = FALSE, nsim = 0)) + expect_error(predict(mcmc_results2, model2, type = "response", + future = 5, nsim = 100)) + expect_error(predict(mcmc_results2, model = 465, type = "response", + future = FALSE, nsim = 100)) + mcmc_results3 <- run_mcmc(model2, iter = 1000, output_type = "theta") + expect_error(predict(mcmc_results3, model2, type = "response", + future = FALSE, nsim = 100)) + class(model) <- "aa" + expect_error(predict(mcmc_results3, model2, type = "response", + future = FALSE, nsim = 100)) }) test_that("Non-gaussian predictions work", { diff --git a/tests/testthat/test_priors.R b/tests/testthat/test_priors.R new file mode 100644 index 00000000..133f3229 --- /dev/null +++ b/tests/testthat/test_priors.R @@ -0,0 +1,18 @@ +context("Test rest of warnings and errors") + +#' @srrstats {G5.2, G5.2a, G5.2b} Test that rest of the warnings are triggered. +test_that("priors give errors with wrong arguments", { + expect_error(normal("a", 0, 1)) + expect_error(uniform(1, 2, 0)) + expect_error(uniform(2, 0, 1)) + expect_error(normal(0, 0, -1)) + expect_error(halfnormal(0, -1)) + expect_error(halfnormal(-1, 0, 1)) + expect_error(tnormal(0, 0, -1)) + expect_error(tnormal(10, 0, 4, 0, 5)) + expect_error(gamma("a", 2, 1)) + expect_error(gamma(1, 0, 1)) + expect_error(gamma(1, -1, 1)) + expect_error(gamma(1, 2, 0)) +}) + diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index bfe2796e..f0c79fe8 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -34,7 +34,24 @@ test_that("MCMC for SDE works", { expect_equivalent(diagis::weighted_mean(t(out$alpha[c(1,50),1,]), out$weights * out$counts), paper[4:5], tol = 0.01) + expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, + particles = 50, mcmc_type = "is2", + L_c = 2, L_f = 6, threads = -1)) + + expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, + particles = 50, mcmc_type = "is2", + L_c = 2, L_f = -1)) + + expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, + particles = 50, mcmc_type = "is2", + L_c = 2, L_f = 1)) + + expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, + particles = 50, mcmc_type = "pm", L_c = 0)) + expect_error(bootstrap_filter(model, 1000, L = -2)) + expect_error(particle_smoother(model, 1000, L = 0)) + expect_error(ll <- logLik(model, 10000, L = -3)) expect_error(ll <- logLik(model, 10000, L = 3), NA) expect_equal(ll, -17, tol = 1) expect_error(out_bsf <- bootstrap_filter(model, 1000, L = 3), NA) diff --git a/tests/testthat/test_sim_smoother.R b/tests/testthat/test_sim_smoother.R index 43733cbd..395b9998 100644 --- a/tests/testthat/test_sim_smoother.R +++ b/tests/testthat/test_sim_smoother.R @@ -9,6 +9,10 @@ test_that("Test that sim_smoother for LGSSM works as Kalman smoother", { use_antithetic = TRUE), NA) expect_equal(smoother(model_bsm)$alphahat, as.ts(apply(sims, 1:2, mean))) + expect_error(sims <- sim_smoother(model_bsm, nsim = 10, + use_antithetic = "blaa")) + expect_error(sims <- sim_smoother(model_bsm, nsim = 10, + use_antithetic = 1)) }) From 565982931dfe1681f442234f4aa48007977c23af Mon Sep 17 00:00:00 2001 From: helske Date: Fri, 19 Nov 2021 20:14:54 +0200 Subject: [PATCH 123/180] fix tests and typo --- R/predict.R | 2 +- tests/testthat/test_mcmc.R | 16 +++++++++------- tests/testthat/test_sde.R | 2 +- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/R/predict.R b/R/predict.R index 0ccb1f0d..66822714 100644 --- a/R/predict.R +++ b/R/predict.R @@ -121,7 +121,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", check_missingness(model) - if (!inherits(model, "bbsm_model")) { + if (!inherits(model, "bssm_model")) { stop("Argument 'model' should be an object of class 'bssm_model'.") } nsim <- check_intmax(nsim, "nsim", max = 10 * object$iter) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 3408bad8..edd168d0 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -40,8 +40,8 @@ test_that("MCMC results from bssm paper are still correct", { skip_on_cran() data(negbin_series) - bssm_model <- bsm_ng(negbin_series, - xreg = x, + bssm_model <- bsm_ng(negbin_series[, 1], + xreg = negbin_series[, 2], beta = normal(0, 0, 10), phi = halfnormal(1, 10), sd_level = halfnormal(0.1, 1), @@ -109,11 +109,8 @@ test_that("run_mcmc throws error with improper arguments", { sd_level = uniform(1, 0, 10)) expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, - end_adaptive_phase = 4), NA) - expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, - local_approx = 4), NA) - expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, - particls = 1), NA) + end_adaptive_phase = 4)) + out <- run_mcmc(model_bssm, iter = 10, output_type = "theta") expect_error(summary(out, return_se = 2)) expect_error(summary(out, only_theta = 2)) @@ -243,6 +240,11 @@ test_that("MCMC for ssm_mng work", { distribution = c("gamma", "binomial"), update_fn = update_fn, prior_fn = prior_fn), NA) + expect_error(run_mcmc(model, iter = 50, + local_approx = 4)) + expect_error(run_mcmc(model, iter = 50, + particles = 1)) + for(type in c("pm", "da", "is1", "is3", "is3", "approx")) { for(method in c("psi", "bsf", "spdk")) { for(output in c("full", "summary", "theta")) { diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index f0c79fe8..891e783e 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -32,7 +32,7 @@ test_that("MCMC for SDE works", { expect_equivalent(diagis::weighted_mean(out$theta, out$weights * out$counts), paper[1:3], tol = 0.1) expect_equivalent(diagis::weighted_mean(t(out$alpha[c(1,50),1,]), - out$weights * out$counts), paper[4:5], tol = 0.01) + out$weights * out$counts), paper[4:5], tol = 0.1) expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, particles = 50, mcmc_type = "is2", From 2c2ca326f2cb95551e820d1c2b305ab8c8a44aaf Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 00:10:50 +0200 Subject: [PATCH 124/180] rewrote summary and print methods, fixed asymptotic_var options --- DESCRIPTION | 2 +- NAMESPACE | 3 +- NEWS | 7 + R/as.data.frame.mcmc_output.R | 10 +- R/as_draws.R | 13 +- R/asymptotic_var.R | 46 ++--- R/bssm-package.R | 5 +- R/check_diagnostics.R | 11 +- R/fitted.R | 2 + R/models.R | 3 +- R/print_mcmc.R | 309 +++++++++------------------- man/as.data.frame.mcmc_output.Rd | 10 +- man/as_draws.Rd | 10 +- man/asymptotic_var.Rd | 25 +-- man/check_diagnostics.Rd | 9 +- man/estimate_ess.Rd | 13 +- man/exchange.Rd | 5 +- man/fitted.mcmc_output.Rd | 3 + man/print.mcmc_output.Rd | 4 +- man/summary.mcmc_output.Rd | 33 ++- tests/testthat/test_as_data_frame.R | 2 +- tests/testthat/test_mcmc.R | 29 ++- 22 files changed, 232 insertions(+), 322 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 899b4954..1940bc18 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.7 +Version: 1.1.8 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NAMESPACE b/NAMESPACE index 5a0db706..2773584c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -101,8 +101,7 @@ importFrom(magrittr,"%>%") importFrom(posterior,as_draws) importFrom(posterior,as_draws_df) importFrom(posterior,default_convergence_measures) -importFrom(posterior,ess_basic) -importFrom(posterior,ess_bulk) +importFrom(posterior,ess_mean) importFrom(posterior,summarise_draws) importFrom(stats,"tsp<-") importFrom(stats,as.ts) diff --git a/NEWS b/NEWS index 5ddec832..26b528da 100644 --- a/NEWS +++ b/NEWS @@ -2,6 +2,13 @@ bssm 1.1.8 (Release date: -) ============== * Added a fitted method for extraction of summary statistics of posterior predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. + * Rewrote the summary method completely, which now returns data.frame. This + also resulted in some changes in order of the function arguments. + * The asymptotic_var and iact functions are now exported to users, and they + also contain alternative methods based on the posterior package. + * New function estimate_ess can be used to compute effective sample size + from weighted MCMC. + * Large number of new tests, and improved documentation. bssm 1.1.7 (Release date: 2021-09-15) diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index e1b3c26a..cce0523c 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -1,9 +1,9 @@ -#' Convert MCMC chain to data.frame +#' Convert MCMC Output to data.frame #' -#' Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. +#' Converts the MCMC output of \code{\link{run_mcmc}} to \code{data.frame}. #' #' @method as.data.frame mcmc_output -#' @param x Output from \code{\link{run_mcmc}}. +#' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. #' @param row.names Ignored. #' @param optional Ignored. #' @param variable Return samples of \code{"theta"} (default) or @@ -13,7 +13,7 @@ #' @param states Vector of indices. In case of states, #' what states to return? Default is all. #' @param expand Should the jump-chain be expanded? -#' Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. +#' Defaults to \code{TRUE}. #' For \code{expand = FALSE} and always for IS-MCMC, #' the resulting data.frame contains variable weight (= counts * IS-weights). #' @param use_times If \code{TRUE} (default), transforms the values of the time @@ -43,7 +43,7 @@ as.data.frame.mcmc_output <- function(x, row.names, optional, variable = c("theta", "states"), times, states, - expand = !(x$mcmc_type %in% paste0("is", 1:3)), + expand = TRUE, use_times = TRUE, ...) { variable <- match.arg(tolower(variable), c("theta", "states")) diff --git a/R/as_draws.R b/R/as_draws.R index 4f2c41a1..6f139892 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -14,6 +14,8 @@ #' @param x An object of class \code{mcmc_output}. #' @param times Vector of indices defining which time points to return? #' Default is all. +#' @param states Vector of indices defining which states to return. +#' Default is all. #' @param ... Ignored. #' @return A \code{draws_df} object. #' @rdname as_draws @@ -31,7 +33,7 @@ #' library("posterior") #' draws <- as_draws(fit1) #' head(draws, 4) -#' ess_bulk(draws$sd_y) +#' estimate_ess(draws$sd_y) #' summary(fit1, return_se = TRUE) #' #' # More chains: @@ -40,22 +42,21 @@ #' model$theta[] <- c(150, 50) # change initial value #' fit3 <- run_mcmc(model, iter = 2000) #' -#' draws <- bind_draws(as_draws(fit1), +#' draws <- posterior::bind_draws(as_draws(fit1), #' as_draws(fit2), as_draws(fit3), along = "chain") #' # it is actually enough to transform first mcmc_output to draws object, #' # rest are transformed automatically inside bind_draws #' posterior::rhat(draws$sd_y) -#' posterior::ess_bulk(draws$sd_y) #' posterior::summarise_draws(draws) #' -as_draws_df.mcmc_output <- function(x, times, ...) { +as_draws_df.mcmc_output <- function(x, times, states, ...) { d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) if (missing(times)) times <- seq_len(nrow(x$alpha)) + if (missing(states)) states <- seq_len(ncol(x$alpha)) d_states <- as.data.frame(x, variable = "states", expand = TRUE, - times = times, - use_times = FALSE) + times = times, states = states, use_times = FALSE) d <- cbind( tidyr::pivot_wider(d_theta, diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 2fa4b66e..39afac4e 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -38,25 +38,17 @@ iact <- function(x) { #' of Vihola et al. (2020) from weighted samples from IS-MCMC. The default #' method is based on the integrated autocorrelation time (IACT) by Sokal (1997) #' which seem to work well for reasonable problems, but it is also possible to -#' use the Geyer's method as implemented in \code{ess_basic} of the -#' \code{posterior} package, or the improved \code{ess_bulk} method of the same -#' package. These effective sample sizes (ESS) can be converted to MCMCSE^2 as -#' MCMCSE(x)^2 = (var(z) * iact(z) / c^2) / n where z = w*(x-) +#' use the Geyer's method as implemented in \code{ess_mean} of the +#' \code{posterior} package. #' -#' (var(z) * iact(z) / estimate_c^2) / length(z) -#' -#' var(x) / ESS = var(x) * IACT / n) where var(x) is the -#' variance of x based on n i.i.d.samples. -#' -#' @importFrom posterior ess_basic ess_bulk +#' @importFrom posterior ess_mean #' @param x Vector of samples. #' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is #' assumed). -#' @param method Method for computing the IACT. Default is \code{"sokal"}, -#' other options are \code{ess_basic} and \code{ess_bulk} which use the -#' corresponding functions of the \code{posterior} package. +#' @param method Method for computing IACT. Default is \code{"sokal"}, +#' other option \code{"geyer"}. #' @references -#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' Vihola M, Helske J, Franks J. (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @@ -66,6 +58,9 @@ iact <- function(x) { #' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. #' https://doi.org/10.1007/978-1-4899-0319-8_6 #' +#' Gelman, A, Carlin J B, Stern H S, Dunson, D B, Vehtari A, Rubin D B. (2013). +#' Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. +#' #' Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). #' Rank-normalization, folding, and localization: An improved Rhat for #' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. @@ -81,11 +76,10 @@ iact <- function(x) { #' w <- rexp(n, 0.5 * exp(0.001 * x^2)) #' # different methods: #' asymptotic_var(x, w, method = "sokal") -#' asymptotic_var(x, w, method = "ess_basic") -#' asymptotic_var(x, w, method = "ess_bulk") +#' asymptotic_var(x, w, method = "geyer") #' asymptotic_var <- function(x, w, method = "sokal") { - method <- match.arg(method, c("sokal", "ess_basic", "ess_bulk")) + method <- match.arg(method, c("sokal", "geyer")) if (missing(w)) w <- rep(1, length(x)) if(any(w < 0) | any(!is.finite(w))) stop("Nonfinite or negative weights in 'w'.") @@ -98,8 +92,7 @@ asymptotic_var <- function(x, w, method = "sokal") { switch(method, sokal = (var(z) * iact(z) / estimate_c^2) / length(z), # ESS(z) = n / IACT(z) - ess_basic = var(z) / ess_basic(z) / estimate_c^2, - ess_bulk = var(z) / ess_bulk(z) / estimate_c^2) + ess_basic = var(z) / posterior::ess_mean(z) / estimate_c^2) } #' Effective Sample Size for IS-type Estimators @@ -116,9 +109,7 @@ asymptotic_var <- function(x, w, method = "sokal") { #' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is #' assumed). #' @param method Method for computing the ESS. Default is \code{"sokal"}, other -#' options are \code{ess_basic} and \code{ess_bulk} which use the corresponding -#' functions of the \code{posterior} package in computation of the asymptotic -#' variance (see also \code{asymptotic_var}). +#' option are \code{"geyer"} (see also \code{asymptotic_var}). #' @references #' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. @@ -130,10 +121,8 @@ asymptotic_var <- function(x, w, method = "sokal") { #' NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. #' https://doi.org/10.1007/978-1-4899-0319-8_6 #' -#' Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). -#' Rank-normalization, folding, and localization: An improved Rhat for -#' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. -#' https://doi.org/10.1214/20-BA1221 +#' Gelman, A, Carlin J B, Stern H S, Dunson, D B, Vehtari A, Rubin D B. (2013). +#' Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. #' @export #' @srrstats {BS5.3, BS5.5} #' @examples @@ -145,11 +134,10 @@ asymptotic_var <- function(x, w, method = "sokal") { #' w <- rexp(n, 0.5 * exp(0.001 * x^2)) #' # different methods: #' estimate_ess(x, w, method = "sokal") -#' estimate_ess(x, w, method = "ess_basic") -#' estimate_ess(x, w, method = "ess_bulk") +#' estimate_ess(x, w, method = "geyer") #' estimate_ess <- function(x, w, method = "sokal") { - method <- match.arg(method, c("sokal", "ess_basic", "ess_bulk")) + method <- match.arg(method, c("sokal", "geyer")) if (missing(w)) w <- rep(1, length(x)) if(any(w < 0) | any(!is.finite(w))) stop("Nonfinite or negative weights in 'w'.") diff --git a/R/bssm-package.R b/R/bssm-package.R index 1c69d909..e5abe38a 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -71,7 +71,8 @@ NULL NULL #' Pound/Dollar daily exchange rates #' -#' Dataset containing daily log-returns from 1/10/81-28/6/85 as in [1] +#' Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and +#' Koopman (2012). #' #' @name exchange #' @docType data @@ -79,7 +80,7 @@ NULL #' @source \url{http://www.ssfpack.com/DKbook.html}. #' @keywords datasets #' @references -#' [1] James Durbin, Siem Jan Koopman (2012). +#' James Durbin, Siem Jan Koopman (2012). #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 40f9f033..07220dd7 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -4,9 +4,12 @@ #' largest Rhat values for a quick first check that the sampling worked. For #' further checks, see e.g. \code{bayesplot} and \code{coda} packages. #' -#' For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior -#' package are based on the approximate posterior which should look reasonable, -#' otherwise the IS-correction does not make much sense. +#' For methods other than IS-MCMC, the estimates are based on the improved +#' diagnostics from the \code{posterior} package.For IS-MCMC, these Rhat, +#' bulk-ESS, and tail-ESS estimates are based on the approximate posterior +#' which should look reasonable, otherwise the IS-correction does not make much +#' sense. For IS-MCMC, ESS estimates based on a weighted posterior are also +#' computed. #' #' @importFrom dplyr across #' @importFrom posterior summarise_draws default_convergence_measures @@ -73,7 +76,7 @@ check_diagnostics <- function(x) { "and ESS measures below.\n", sep="") } - sumr <- summarise_draws(draws, default_convergence_measures()) + sumr <- posterior::summarise_draws(draws, posterior::default_convergence_measures()) min_ess <- which.min(sumr$ess_bulk) cat("\nSmallest bulk-ESS: ", round(sumr$ess_bulk[min_ess]), " (", sumr$variable[min_ess], ")", sep = "") diff --git a/R/fitted.R b/R/fitted.R index 698f4de3..e508158e 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -12,6 +12,8 @@ #' @param object Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}} based on the input model. #' @param model A \code{bssm_model} object. +#' @param probs Numeric vector defining the quantiles of interest. Default is +#' \code{c(0.025, 0.975)}. #' @param ... Ignored. #' @examples #' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) diff --git a/R/models.R b/R/models.R index 2f8733e7..7ed4534a 100644 --- a/R/models.R +++ b/R/models.R @@ -846,7 +846,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' out <- run_mcmc(model, iter = 1e5, particles = 10) #' summary(out, variable = "theta", return_se = TRUE) #' # should be about 0.093 and 0.016 -#' summary(out, variable = "states", return_se = TRUE)$Mean[c(1,100),1] +#' summary(out, variable = "states", return_se = TRUE, +#' states = 1, times = c(1, 100))$Mean #' # should be about -0.075, 2.618 #' } #' diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 607d3fbc..1d0daf7c 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,109 +1,6 @@ -#' Print Results from MCMC Run -#' -#' Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. -#' -#' @method print mcmc_output -#' @importFrom diagis weighted_mean weighted_var weighted_se ess -#' @importFrom coda mcmc -#' @importFrom stats var -#' @param x Output from \code{\link{run_mcmc}}. -#' @param ... Ignored. -#' @srrstats {BS6.0} -#' @export -print.mcmc_output <- function(x, ...) { - - if (x$mcmc_type %in% paste0("is", 1:3)) { - theta <- mcmc(x$theta) - if (x$output_type == 1) - alpha <- mcmc(matrix(x$alpha[nrow(x$alpha), , ], ncol = ncol(x$alpha), - byrow = TRUE, dimnames = list(NULL, colnames(x$alpha)))) - w <- x$counts * x$weights - } else { - theta <- expand_sample(x, "theta") - if (x$output_type == 1) - alpha <- - expand_sample(x, "state", times = nrow(x$alpha), by_states = FALSE)[[1]] - } - - cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), - "\n", sep = "") - - cat("\n", "Iterations = ", x$burnin + 1, ":", x$iter, "\n", sep = "") - cat("Thinning interval = ", x$thin, "\n", sep = "") - cat("Length of the final jump chain = ", length(x$counts), "\n", sep = "") - cat("\nAcceptance rate after the burn-in period: ", - paste(round(x$acceptance_rate, 3), "\n", sep = "")) - - cat("\nSummary for theta:\n\n") - if (x$mcmc_type %in% paste0("is", 1:3)) { - mean_theta <- weighted_mean(theta, w) - sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) - se_theta_is <- weighted_se(theta, w) - se_theta <- sqrt(apply(theta, 2, function(x) asymptotic_var(x, w))) - - stats <- matrix(c(mean_theta, sd_theta, se_theta, se_theta_is), ncol = 4, - dimnames = list(colnames(x$theta), c("Mean", "SD", "SE", "SE-IS"))) - } else { - mean_theta <- colMeans(theta) - sd_theta <- apply(theta, 2, sd) - se_theta <- sqrt(apply(theta, 2, function(x) iact(x) * var(x) / length(x))) - #se_theta <- sqrt(spectrum0.ar(theta)$spec / nrow(theta)) - stats <- matrix(c(mean_theta, sd_theta, se_theta), ncol = 3, - dimnames = list(colnames(x$theta), c("Mean", "SD", "SE"))) - } - - print(stats) - - cat("\nEffective sample sizes for theta:\n\n") - esss <- matrix((sd_theta / se_theta)^2, ncol = 1, - dimnames = list(colnames(x$theta), c("ESS"))) - print(esss) - if (x$output_type != 3) { - - n <- nrow(x$alpha) - cat(paste0("\nSummary for alpha_", n), ":\n\n", sep = "") - - if (is.null(x$alphahat)) { - if (x$mcmc_type %in% paste0("is", 1:3)) { - mean_alpha <- weighted_mean(alpha, w) - sd_alpha <- sqrt(diag(weighted_var(alpha, w, method = "moment"))) - se_alpha_is <- weighted_se(alpha, w) - se_alpha <- sqrt(apply(alpha, 2, function(x) asymptotic_var(x, w))) - stats <- matrix(c(mean_alpha, sd_alpha, se_alpha, se_alpha_is), - ncol = 4, - dimnames = list(colnames(x$alpha), c("Mean", "SD", "SE", "SE-IS"))) - } else { - mean_alpha <- colMeans(alpha) - sd_alpha <- apply(alpha, 2, sd) - se_alpha <- sqrt(apply(alpha, 2, function(x) iact(x) * var(x) / length(x))) - #sqrt(spectrum0.ar(alpha)$spec / nrow(alpha)) - stats <- matrix(c(mean_alpha, sd_alpha, se_alpha), ncol = 3, - dimnames = list(colnames(x$alpha), c("Mean", "SD", "SE"))) - } - print(stats) - - - cat(paste0("\nEffective sample sizes for alpha_", n), ":\n\n", sep = "") - esss <- matrix((sd_alpha / se_alpha)^2, ncol = 1, - dimnames = list(colnames(x$alpha), c("ESS"))) - - print(esss) - - } else { - if (ncol(x$alphahat) == 1) { - print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(x$Vt[, , n]))) - } else { - print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(diag(x$Vt[, , n])))) - } - } - } else cat("\nNo posterior samples for states available.\n") - cat("\nRun time:\n") - print(x$time) -} - -#' Summary of MCMC object +#' Summary Statistics of Posterior Samples #' -#' This functions returns a list containing mean, standard deviations, +#' This functions returns a data frame containing mean, standard deviations, #' standard errors, and effective sample size estimates for parameters and #' states. #' @@ -114,12 +11,21 @@ print.mcmc_output <- function(x, ...) { #' #' #' @param object Output from \code{run_mcmc} -#' @param return_se if \code{FALSE} (default), computation of standard -#' errors and effective sample sizes is omitted. #' @param variable Are the summary statistics computed for either #' \code{"theta"} (default), \code{"states"}, or \code{"both"}? -#' @param only_theta Deprecated. If \code{TRUE}, summaries are computed only -#' for hyperparameters theta, not latent states alpha. +#' @param return_se if \code{FALSE} (default), computation of standard +#' errors and effective sample sizes is omitted (as they can take considerable +#' time for models with large number of states and time points). +#' @param probs Numeric vector defining the quantiles of interest. Default is +#' \code{c(0.025, 0.975)}. +#' @param times Vector of indices. For states, for what time points the +#' summaries should be computed? Default is all, ignored if +#' \code{variable = "theta"}. +#' @param states Vector of indices. For what states the summaries should be +#' computed?. Default is all, ignored if +#' \code{variable = "theta"}. +#' @param method Method for computing integrated autocorrelation time. Default +#' is \code{"sokal"}, other option is \code{"geyer"}. #' @param ... Ignored. #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based @@ -128,126 +34,103 @@ print.mcmc_output <- function(x, ...) { #' @export #' @srrstats {BS6.4} summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", - only_theta = FALSE, ...) { - + probs = c(0.025, 0.975), times, states, method = "sokal", ...) { if (!test_flag(return_se)) stop("Argument 'return_se' should be TRUE or FALSE. ") - if (!test_flag(only_theta)) - stop("Argument 'only_theta' should be TRUE or FALSE. ") + + method <- match.arg(method, c("sokal", "geyer")) - if (only_theta) { - variable <- "theta" - warning(paste("Argument 'only_theta' is deprecated. Use argument", - "'variable' instead. ", sep = " ")) - } variable <- match.arg(tolower(variable), c("theta", "states", "both")) if (variable %in% c("theta", "both")) { - if (object$mcmc_type %in% paste0("is", 1:3)) { - theta <- mcmc(object$theta) - w <- object$counts * object$weights - mean_theta <- weighted_mean(theta, w) - sd_theta <- sqrt(diag(weighted_var(theta, w, method = "moment"))) - - if (return_se) { - se_theta_is <- weighted_se(theta, w) - se_theta <- sqrt(apply(theta, 2, function(x) asymptotic_var(x, w))) - ess_theta <- (sd_theta / se_theta)^2 - ess_w <- apply(object$theta, 2, function(x) ess(w, identity, x)) - summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta, - se_theta_is, ess_w), ncol = 6, - dimnames = list(colnames(object$theta), - c("Mean", "SD", "SE", "ESS", "SE-IS", "ESS-IS"))) - } else { - summary_theta <- matrix(c(mean_theta, sd_theta), ncol = 2, - dimnames = list(colnames(object$theta), c("Mean", "SD"))) - } - } else { - theta <- expand_sample(object, "theta") - mean_theta <- colMeans(theta) - sd_theta <- apply(theta, 2, sd) - - if (return_se) { - #sqrt(spectrum0.ar(theta)$spec / nrow(theta)) - se_theta <- sqrt(apply(theta, 2, function(x) iact(x) * var(x) / length(x))) - ess_theta <- (sd_theta / se_theta)^2 - summary_theta <- matrix(c(mean_theta, sd_theta, se_theta, ess_theta), - ncol = 4, - dimnames = list(colnames(object$theta), c("Mean", "SD", "SE", "ESS"))) - } else { - summary_theta <- matrix(c(mean_theta, sd_theta), ncol = 2, - dimnames = list(colnames(object$theta), c("Mean", "SD"))) - } - } + d <- tidyr::pivot_wider( + as.data.frame(object, variable = "theta", expand = TRUE), + values_from = value, names_from = variable) } if (variable %in% c("states", "both")) { if (object$output_type != 1) stop("Cannot return summary of states as the MCMC type was not 'full'. ") - m <- ncol(object$alpha) + if (missing(times)) times <- seq_len(nrow(object$alpha)) + if (missing(states)) states <- seq_len(ncol(object$alpha)) - if (object$mcmc_type %in% paste0("is", 1:3)) { - w <- object$counts * object$weights - mean_alpha <- ts(weighted_mean(object$alpha, w), - start = attr(object, "ts")$start, - frequency = attr(object, "ts")$frequency, - names = colnames(object$alpha)) - sd_alpha <- weighted_var(object$alpha, w, method = "moment") - sd_alpha <- if (m > 1) { - sqrt(t(apply(sd_alpha, 3, diag))) - } else matrix(sqrt(sd_alpha), ncol = 1) - - - if (return_se) { - se_alpha_is <- apply(object$alpha, 2, - function(x) weighted_se(t(x), w)) - - se_alpha <- apply(object$alpha, 2, - function(z) sqrt(apply(z, 1, function(x) asymptotic_var(x, w)))) - alpha_ess <- (sd_alpha / se_alpha)^2 - ess_w <- apply(object$alpha, 2, - function(z) apply(z, 1, function(x) ess(w, identity, x))) - summary_alpha <- list( - "Mean" = mean_alpha, "SD" = sd_alpha, - "SE" = se_alpha, "ESS" = alpha_ess, - "SE-IS" = se_alpha_is, "ESS-IS" = ess_w) - } else { - summary_alpha <- list("Mean" = mean_alpha, "SD" = sd_alpha) - } - + d_states <- tidyr::pivot_wider( + as.data.frame(object, variable = "states", expand = TRUE, + times = times, states = states, use_times = FALSE), + values_from = value, + names_from = c(variable, time), + names_glue = "{variable}[{time}]") + if (variable == "both") { + d <- cbind(d, d_states[, -(1:2)]) + } else d <- d_states + } + + + if (object$mcmc_type %in% paste0("is", 1:3)) { + summary_f_is <- function(x, w) { + c(Mean = weighted_mean(x, w), + SE = sqrt(asymptotic_var(x, w, method)), + SD = sqrt(diagis::weighted_var(x, w)), + diagis::weighted_quantile(x, w, probs), + ESS = round(estimate_ess(x, w, method)), + SE_IS = diagis::weighted_se(x, w), + ESS_IS = round(diagis::ess(w, identity, x))) + } + as.data.frame(t(apply(d[, -(1:2)], 2, summary_f_is, w = d$weight))) + } else { + summary_f <- function(x) { + c(Mean = mean(x), SE = sqrt(asymptotic_var(x, method = method)), + SD = sd(x), quantile(x, probs), + ESS = round(estimate_ess(x, method = method))) + } + as.data.frame(t(apply(d[, -(1:2)], 2, summary_f))) + } +} + + +#' Print Results from MCMC Run +#' +#' Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. +#' +#' @method print mcmc_output +#' @importFrom diagis weighted_mean weighted_var weighted_se ess +#' @importFrom coda mcmc +#' @importFrom stats var +#' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. +#' @param ... Ignored. +#' @srrstats {BS6.0} +#' @export +print.mcmc_output <- function(x, ...) { + + cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), + "\n", sep = "") + + cat("\n", "Iterations = ", x$burnin + 1, ":", x$iter, "\n", sep = "") + cat("Thinning interval = ", x$thin, "\n", sep = "") + cat("Length of the final jump chain = ", length(x$counts), "\n", sep = "") + cat("\nAcceptance rate after the burn-in period: ", + paste(round(x$acceptance_rate, 3), "\n", sep = "")) + + cat("\nSummary for theta:\n\n") + stats <- summary(x, variable = "theta") + print(stats) + if (x$output_type != 3) { + n <- nrow(x$alpha) + cat(paste0("\nSummary for alpha_", n), ":\n\n", sep = "") + + if (is.null(x$alphahat)) { + stats <- summary(x, variable = "states", times = n) + print(stats) } else { - alpha <- expand_sample(object, "states") - mean_alpha <- ts(vapply(alpha, colMeans, numeric(nrow(object$alpha))), - start = attr(object, "ts")$start, - frequency = attr(object, "ts")$frequency, - names = colnames(object$alpha)) - sd_alpha <- vapply(alpha, function(x) apply(x, 2, sd), - numeric(nrow(object$alpha))) - - if (return_se) { - # se_alpha <- vapply(alpha, function(x) - # apply(x, 2, function(z) - # sqrt(spectrum0.ar(z)$spec / length(z))), - # numeric(nrow(object$alpha))) - se_alpha <- vapply(alpha, function(x) - apply(x, 2, function(z) - sqrt(apply(theta, 2, function(x) iact(z) * var(z) / length(z)))), - numeric(nrow(object$alpha))) - - ess_alpha <- (sd_alpha / se_alpha)^2 - summary_alpha <- list( - "Mean" = mean_alpha, "SD" = sd_alpha, - "SE" = se_alpha, "ESS" = ess_alpha) + if (ncol(x$alphahat) == 1) { + print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(x$Vt[, , n]))) } else { - summary_alpha <- list("Mean" = mean_alpha, "SD" = sd_alpha) + print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(diag(x$Vt[, , n])))) } } - } - switch(variable, - "both" = return(list(theta = summary_theta, states = summary_alpha)), - "theta" = return(summary_theta), - "states" = return(summary_alpha) - ) + } else cat("\nNo posterior samples for states available.\n") + cat("\nRun time:\n") + print(x$time) } diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index daf16f9c..2bc00bb0 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/as.data.frame.mcmc_output.R \name{as.data.frame.mcmc_output} \alias{as.data.frame.mcmc_output} -\title{Convert MCMC chain to data.frame} +\title{Convert MCMC Output to data.frame} \usage{ \method{as.data.frame}{mcmc_output}( x, @@ -11,13 +11,13 @@ variable = c("theta", "states"), times, states, - expand = !(x$mcmc_type \%in\% paste0("is", 1:3)), + expand = TRUE, use_times = TRUE, ... ) } \arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} +\item{x}{Object of class \code{mcmc_output} from \code{\link{run_mcmc}}.} \item{row.names}{Ignored.} @@ -33,7 +33,7 @@ what time points to return? Default is all.} what states to return? Default is all.} \item{expand}{Should the jump-chain be expanded? -Defaults to \code{TRUE} for non-IS-MCMC, and \code{FALSE} for IS-MCMC. +Defaults to \code{TRUE}. For \code{expand = FALSE} and always for IS-MCMC, the resulting data.frame contains variable weight (= counts * IS-weights).} @@ -44,7 +44,7 @@ time is based on the indexing starting from 1.} \item{...}{Ignored.} } \description{ -Converts the MCMC chain output of \code{\link{run_mcmc}} to data.frame. +Converts the MCMC output of \code{\link{run_mcmc}} to \code{data.frame}. } \examples{ data("poisson_series") diff --git a/man/as_draws.Rd b/man/as_draws.Rd index 6184e745..64ef0a45 100644 --- a/man/as_draws.Rd +++ b/man/as_draws.Rd @@ -4,7 +4,7 @@ \alias{as_draws_df.mcmc_output} \title{Convert \code{run_mcmc} output to \code{draws_df} format} \usage{ -\method{as_draws_df}{mcmc_output}(x, times, ...) +\method{as_draws_df}{mcmc_output}(x, times, states, ...) } \arguments{ \item{x}{An object of class \code{mcmc_output}.} @@ -12,6 +12,9 @@ \item{times}{Vector of indices defining which time points to return? Default is all.} +\item{states}{Vector of indices defining which states to return. +Default is all.} + \item{...}{Ignored.} } \value{ @@ -41,7 +44,7 @@ fit1 <- run_mcmc(model, iter = 2000) library("posterior") draws <- as_draws(fit1) head(draws, 4) -ess_bulk(draws$sd_y) +estimate_ess(draws$sd_y) summary(fit1, return_se = TRUE) # More chains: @@ -50,12 +53,11 @@ fit2 <- run_mcmc(model, iter = 2000) model$theta[] <- c(150, 50) # change initial value fit3 <- run_mcmc(model, iter = 2000) -draws <- bind_draws(as_draws(fit1), +draws <- posterior::bind_draws(as_draws(fit1), as_draws(fit2), as_draws(fit3), along = "chain") # it is actually enough to transform first mcmc_output to draws object, # rest are transformed automatically inside bind_draws posterior::rhat(draws$sd_y) -posterior::ess_bulk(draws$sd_y) posterior::summarise_draws(draws) } diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd index acad616f..6b1616e9 100644 --- a/man/asymptotic_var.Rd +++ b/man/asymptotic_var.Rd @@ -12,25 +12,16 @@ asymptotic_var(x, w, method = "sokal") \item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is assumed).} -\item{method}{Method for computing the IACT. Default is \code{"sokal"}, -other options are \code{ess_basic} and \code{ess_bulk} which use the -corresponding functions of the \code{posterior} package.} +\item{method}{Method for computing IACT. Default is \code{"sokal"}, +other option \code{"geyer"}.} } \description{ The asymptotic variance MCMCSE^2 is based on Corollary 1 of Vihola et al. (2020) from weighted samples from IS-MCMC. The default method is based on the integrated autocorrelation time (IACT) by Sokal (1997) which seem to work well for reasonable problems, but it is also possible to -use the Geyer's method as implemented in \code{ess_basic} of the -\code{posterior} package, or the improved \code{ess_bulk} method of the same -package. These effective sample sizes (ESS) can be converted to MCMCSE^2 as -MCMCSE(x)^2 = (var(z) * iact(z) / c^2) / n where z = w*(x-) -} -\details{ -(var(z) * iact(z) / estimate_c^2) / length(z) - -var(x) / ESS = var(x) * IACT / n) where var(x) is the -variance of x based on n i.i.d.samples. +use the Geyer's method as implemented in \code{ess_mean} of the +\code{posterior} package. } \examples{ set.seed(1) @@ -41,12 +32,11 @@ for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) w <- rexp(n, 0.5 * exp(0.001 * x^2)) # different methods: asymptotic_var(x, w, method = "sokal") -asymptotic_var(x, w, method = "ess_basic") -asymptotic_var(x, w, method = "ess_bulk") +asymptotic_var(x, w, method = "geyer") } \references{ -Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +Vihola M, Helske J, Franks J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 @@ -56,6 +46,9 @@ In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0319-8_6 +Gelman, A, Carlin J B, Stern H S, Dunson, D B, Vehtari A, Rubin D B. (2013). +Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. + Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. diff --git a/man/check_diagnostics.Rd b/man/check_diagnostics.Rd index 94d59e31..115da116 100644 --- a/man/check_diagnostics.Rd +++ b/man/check_diagnostics.Rd @@ -16,9 +16,12 @@ largest Rhat values for a quick first check that the sampling worked. For further checks, see e.g. \code{bayesplot} and \code{coda} packages. } \details{ -For IS-MCMC, the Rhat, bulk-ESS, and tail-ESS returned by the posterior -package are based on the approximate posterior which should look reasonable, -otherwise the IS-correction does not make much sense. +For methods other than IS-MCMC, the estimates are based on the improved +diagnostics from the \code{posterior} package.For IS-MCMC, these Rhat, +bulk-ESS, and tail-ESS estimates are based on the approximate posterior +which should look reasonable, otherwise the IS-correction does not make much +sense. For IS-MCMC, ESS estimates based on a weighted posterior are also +computed. } \examples{ set.seed(1) diff --git a/man/estimate_ess.Rd b/man/estimate_ess.Rd index 0d0bc821..3e3a0d35 100644 --- a/man/estimate_ess.Rd +++ b/man/estimate_ess.Rd @@ -13,9 +13,7 @@ estimate_ess(x, w, method = "sokal") assumed).} \item{method}{Method for computing the ESS. Default is \code{"sokal"}, other -options are \code{ess_basic} and \code{ess_bulk} which use the corresponding -functions of the \code{posterior} package in computation of the asymptotic -variance (see also \code{asymptotic_var}).} +option are \code{"geyer"} (see also \code{asymptotic_var}).} } \description{ Computes the effective sample size (ESS) based on weighted posterior @@ -36,8 +34,7 @@ for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) w <- rexp(n, 0.5 * exp(0.001 * x^2)) # different methods: estimate_ess(x, w, method = "sokal") -estimate_ess(x, w, method = "ess_basic") -estimate_ess(x, w, method = "ess_bulk") +estimate_ess(x, w, method = "geyer") } \references{ @@ -51,8 +48,6 @@ In: DeWitt-Morette C, Cartier P, Folacci A (eds) Functional Integration. NATO ASI Series (Series B: Physics), vol 361. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0319-8_6 -Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. (2021). -Rank-normalization, folding, and localization: An improved Rhat for -assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. -https://doi.org/10.1214/20-BA1221 +Gelman, A, Carlin J B, Stern H S, Dunson, D B, Vehtari A, Rubin D B. (2013). +Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. } diff --git a/man/exchange.Rd b/man/exchange.Rd index 0f518b6e..68ed0b93 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -11,7 +11,8 @@ A vector of length 945. \url{http://www.ssfpack.com/DKbook.html}. } \description{ -Dataset containing daily log-returns from 1/10/81-28/6/85 as in \link{1} +Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and +Koopman (2012). } \examples{ data("exchange") @@ -22,7 +23,7 @@ out <- particle_smoother(model, particles = 500) plot.ts(cbind(model$y, exp(out$alphahat))) } \references{ -\link{1} James Durbin, Siem Jan Koopman (2012). +James Durbin, Siem Jan Koopman (2012). Time Series Analysis by State Space Methods. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 } diff --git a/man/fitted.mcmc_output.Rd b/man/fitted.mcmc_output.Rd index 6302dcbb..0fb9d0ff 100644 --- a/man/fitted.mcmc_output.Rd +++ b/man/fitted.mcmc_output.Rd @@ -12,6 +12,9 @@ \item{model}{A \code{bssm_model} object.} +\item{probs}{Numeric vector defining the quantiles of interest. Default is +\code{c(0.025, 0.975)}.} + \item{...}{Ignored.} } \description{ diff --git a/man/print.mcmc_output.Rd b/man/print.mcmc_output.Rd index 5ddf0ba6..d7d6bb24 100644 --- a/man/print.mcmc_output.Rd +++ b/man/print.mcmc_output.Rd @@ -7,10 +7,10 @@ \method{print}{mcmc_output}(x, ...) } \arguments{ -\item{x}{Output from \code{\link{run_mcmc}}.} +\item{x}{Object of class \code{mcmc_output} from \code{\link{run_mcmc}}.} \item{...}{Ignored.} } \description{ -Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. +Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. } diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 02ce09c6..2ea6c1cc 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -2,26 +2,47 @@ % Please edit documentation in R/print_mcmc.R \name{summary.mcmc_output} \alias{summary.mcmc_output} -\title{Summary of MCMC object} +\title{Summary Statistics of Posterior Samples} \usage{ -\method{summary}{mcmc_output}(object, return_se = FALSE, variable = "theta", only_theta = FALSE, ...) +\method{summary}{mcmc_output}( + object, + return_se = FALSE, + variable = "theta", + probs = c(0.025, 0.975), + times, + states, + method = "sokal", + ... +) } \arguments{ \item{object}{Output from \code{run_mcmc}} \item{return_se}{if \code{FALSE} (default), computation of standard -errors and effective sample sizes is omitted.} +errors and effective sample sizes is omitted (as they can take considerable +time for models with large number of states and time points).} \item{variable}{Are the summary statistics computed for either \code{"theta"} (default), \code{"states"}, or \code{"both"}?} -\item{only_theta}{Deprecated. If \code{TRUE}, summaries are computed only -for hyperparameters theta, not latent states alpha.} +\item{probs}{Numeric vector defining the quantiles of interest. Default is +\code{c(0.025, 0.975)}.} + +\item{times}{Vector of indices. For states, for what time points the +summaries should be computed? Default is all, ignored if +\code{variable = "theta"}.} + +\item{states}{Vector of indices. For what states the summaries should be +computed?. Default is all, ignored if +\code{variable = "theta"}.} + +\item{method}{Method for computing integrated autocorrelation time. Default +is \code{"sokal"}, other option is \code{"geyer"}.} \item{...}{Ignored.} } \description{ -This functions returns a list containing mean, standard deviations, +This functions returns a data frame containing mean, standard deviations, standard errors, and effective sample size estimates for parameters and states. } diff --git a/tests/testthat/test_as_data_frame.R b/tests/testthat/test_as_data_frame.R index e720fd9b..cdeee5c2 100644 --- a/tests/testthat/test_as_data_frame.R +++ b/tests/testthat/test_as_data_frame.R @@ -43,6 +43,6 @@ test_that("expanded and not expanded data frame work equally for states", { expect_error(sumr <- summary(mcmc_bsm, variable = "both", return_se = TRUE), NA) expect_equal(mean(d$value[d$variable == "sd_y"]), - sumr$theta["sd_y", "Mean"]) + sumr["sd_y", "Mean"]) }) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index edd168d0..13466bbc 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -22,16 +22,16 @@ test_that("prior and posterior distributions coincide when no data is used", { phi = gamma_prior(0.4, 2, 1), beta = normal_prior(0.5, 0, 1)) - prior_sumr <- rbind( + prior_sumr <- as.data.frame(rbind( rho = c(0.5, sqrt(1/12)), sigma = c(0.2, sqrt(2)/10), mu = c(0.2, 0.5), phi = c(2, sqrt(2)), - beta = c(0, 1)) + beta = c(0, 1))) # approx is enough here, the weights are uniform when there is no data fit <- run_mcmc(model, iter = 2e5,burnin = 1e4, mcmc_type = "approx") - expect_equivalent(prior_sumr, summary(fit), tol = 0.1) + expect_equivalent(prior_sumr, summary(fit)[, c(1, 3)], tol = 0.1) }) @@ -56,7 +56,8 @@ test_that("MCMC results from bssm paper are still correct", { paper_theta <- c(0.092, 0.003, 5.392, -0.912) expect_equivalent(sumr_theta, paper_theta, tol = 0.01) - expect_error(sumr_alpha <- summary(fit_bssm, variable = "states")$Mean[200,], + expect_error(sumr_alpha <- summary(fit_bssm, + variable = "states", times = 200)$Mean, NA) paper_alpha <- c(6.962,0.006) expect_equivalent(sumr_alpha, paper_alpha, tol = 0.01) @@ -113,7 +114,6 @@ test_that("run_mcmc throws error with improper arguments", { out <- run_mcmc(model_bssm, iter = 10, output_type = "theta") expect_error(summary(out, return_se = 2)) - expect_error(summary(out, only_theta = 2)) expect_error(summary(out, variable = "both")) }) @@ -280,11 +280,9 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { expect_lt(max(mcmc_poisson$theta), Inf) expect_true(is.finite(sum(mcmc_poisson$alpha))) - expect_warning(summary(mcmc_poisson, only_theta = TRUE)) - sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) - expect_lt(sum(abs(sumr$theta - c(0.25892090511681, 0.186796779799571))), 0.5) + expect_lt(sum(abs(sumr[1, c(1, 3)] - c(0.25892090511681, 0.186796779799571))), 0.5) states <- expand_sample(mcmc_poisson, variable = "states") @@ -297,9 +295,18 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { expect_error(expand_sample(mcmc_poisson, variable = "states", states = list(4))) - expect_equal(as.numeric(sumr$states$Mean[,1]), + + expect_equal(sumr$Mean[seq(2, nrow(sumr), by = 2)], as.numeric(colMeans(states$level))) + expect_error(posterior::as_draws(mcmc_poisson), NA) + expect_error(d <- as.data.frame(mcmc_poisson, variable = "state"), NA) + x <- dplyr::pull(dplyr::summarise( + dplyr::group_by( + dplyr::filter(d, variable == "level"), time), + mean = mean(value)), mean) + expect_equal(x, as.numeric(colMeans(states$level))) + for(type in c("pm", "da", "is1", "is3", "is3", "approx")) { z <- 2*type%in%c("is1", "is3", "is3", "approx") expect_equal( @@ -345,7 +352,7 @@ test_that("MCMC using SPDK for Gamma model works", { expect_lt(max(mcmc_gamma$theta), Inf) expect_true(is.finite(sum(mcmc_gamma$alpha))) - expect_lt(sum(abs(summary(mcmc_gamma)[,"Mean"] - + expect_lt(sum(abs(summary(mcmc_gamma)$Mean - c(0.542149368711246, 12.353642743311))), 2) }) @@ -397,7 +404,7 @@ test_that("MCMC results for SV model using IS-correction are correct", { mcmc_type = "is2", seed = 1, sampling_mcmc_type = "bsf"), NA) expect_warning(expand_sample(mcmc_sv)) - sumr <- expect_error(summary(mcmc_sv, variable = "both"), NA) + expect_error(summary(mcmc_sv, variable = "both"), NA) expect_gt(mcmc_sv$acceptance_rate, 0) expect_true(is.finite(sum(mcmc_sv$theta))) expect_true(is.finite(sum(mcmc_sv$alpha))) From ebad22a5f9ca2c596333daf0eaf76611656627e1 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 00:31:39 +0200 Subject: [PATCH 125/180] fix typos in standards tags --- R/srr-stats-standards.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index d60f7370..53e3ce81 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -93,7 +93,7 @@ #' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* #' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* #' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* -#' @srrstats {B31.2a} *The main package `README`, either as textual description or example code* [**B31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* +#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* [**BS31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* #' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* #' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* #' @srrstats {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* From 9607a519f5259913fa52a54668df700fe5b74cfa Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 00:39:49 +0200 Subject: [PATCH 126/180] ... --- R/srr-stats-standards.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 53e3ce81..59c16b93 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -93,7 +93,7 @@ #' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* #' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* #' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* -#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* [**BS31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**B31.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* +#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* [**BS31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**BS1.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* #' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* #' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* #' @srrstats {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* From 85074a0d379e153b266d64ff1069a7d2eabc2d43 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 13:16:00 +0200 Subject: [PATCH 127/180] fix some standards and add newlines --- R/as_draws.R | 2 +- R/asymptotic_var.R | 2 +- R/bssm-package.R | 2 +- R/check_arguments.R | 2 +- R/run_mcmc.R | 2 +- R/srr-stats-standards.R | 9 +++++++-- README.Rmd | 4 ++-- tests/testthat/test_basics.R | 2 +- vignettes/growth_model.Rmd | 2 +- 9 files changed, 16 insertions(+), 11 deletions(-) diff --git a/R/as_draws.R b/R/as_draws.R index 6f139892..16eed520 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -80,4 +80,4 @@ as_draws_df.mcmc_output <- function(x, times, states, ...) { as_draws(d) } #' @exportS3Method posterior::as_draws mcmc_output -as_draws.mcmc_output <- function(x, times, ...) as_draws_df.mcmc_output(x, times, ...) \ No newline at end of file +as_draws.mcmc_output <- function(x, times, ...) as_draws_df.mcmc_output(x, times, ...) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 39afac4e..8770f645 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -145,4 +145,4 @@ estimate_ess <- function(x, w, method = "sokal") { stop("No positive weights in 'w'.") } weighted_var(x, w) / asymptotic_var(x, w, method = method) -} \ No newline at end of file +} diff --git a/R/bssm-package.R b/R/bssm-package.R index e5abe38a..bb1d8c22 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -159,4 +159,4 @@ NULL #' #' plot.ts(ts(cbind(sd_level=draws$sd_level, draws$sd_slope), start = 1000), #' xlab = "Iteration", main = "Traceplots") -NULL \ No newline at end of file +NULL diff --git a/R/check_arguments.R b/R/check_arguments.R index a11c7078..e04f9777 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -448,4 +448,4 @@ check_missingness <- function(x) { "(except in components 'y' and 'prior_parameters').")) } } -} \ No newline at end of file +} diff --git a/R/run_mcmc.R b/R/run_mcmc.R index c229e02a..40cc310e 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -110,7 +110,7 @@ #' @export #' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} #' @srrstats {BS2.6} -#' @srrstats {BS2.7, BS1.3a, BS2.8} Explained in docs. +#' @srrstats {BS2.7, BS1.3, BS1.3a, BS1.3b, BS2.8} Explained in docs. #' @srrstats {BS2.9} The argument 'seed' is set to random value if not #' specified by the user. #' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 59c16b93..f86deb77 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -93,8 +93,12 @@ #' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* #' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* #' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* -#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* [**BS31.2b**]{#BS1_2b} *At least one package vignette, both as general and applied textual descriptions, and example code* [**BS1.2c**]{#BS1_2c} *Function-level documentation, preferably with code included in examples* [**BS1.3**]{#BS1_3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* -#' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* [**BS1.3b**]{#BS1_3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* [**BS1.4**]{#BS1_4} *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* +#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* +#' @srrstats {BS1.2b} *At least one package vignette, both as general and applied textual descriptions, and example code* +#' @srrstats {BS1.2c} *Function-level documentation, preferably with code included in examples* +#' @srrstats {BS1.3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* +#' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* +#' @srrstats {BS1.3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* #' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* #' @srrstats {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* #' @srrstats {BS4.1} *Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).* @@ -182,6 +186,7 @@ NULL #' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* #' #' No automatic stopping at converge (converge checkers) is supported +#' @srrstatsNA {BS1.4] *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* #' @srrstatsNA {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* #' @srrstatsNA {BS4.3} *Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.* #' @srrstatsNA {BS4.4} *Enable computations to be stopped on convergence (although not necessarily by default).* diff --git a/README.Rmd b/README.Rmd index 4a4dc0b5..479401a2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -40,8 +40,8 @@ knitr::opts_chunk$set( #' testthat tests. #' @srrstats {G5.9, G5.9a, G5.9b} Tested with autotest and the testthat tests. #' -#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} Addressed in the models.R, -#' run_mcmc.R, in vignettes and in the R Journal paper. +#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b, BS1.3b} Addressed in the +#' models.R, run_mcmc.R, in vignettes and in the R Journal paper. #' #' @srrstats {BS2.1, BS2.1a} Tested by autotest and package examples/tests. ``` diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index 22f53845..90019491 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -203,4 +203,4 @@ test_that("asymptotic_var fails with improper weights", { expect_error(asymptotic_var(x, rep(0, length(x)))) expect_error(asymptotic_var(x, c(-1, runif(9)))) expect_error(asymptotic_var(x, c(Inf, runif(9)))) -}) \ No newline at end of file +}) diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 62d40101..eddd8165 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -38,7 +38,7 @@ y_t = Z(t, \alpha_t, \theta) + H(t, \alpha_t, \theta)\epsilon_t,\\ $$ with $t=1,\ldots, n$, $\epsilon_t \sim N(0,\textrm{I}_p)$, and $\eta \sim N(0,\textrm{I}_k)$. Here vector $\theta$ contains the unknown model parameters. -As some of the model matrices may depend on the current state $\alpha_t$, constructing for example $T(t,\alpha_t,\theta)$ by calling user-defined `R` function is not feasible, as this should be done repeatedly within the particle filter which would negate the benefits of the whole `C++` implementation of the particle filter and Markov chain Monte Carlo. Therefore the functions $T(\cdot)$, $H(\cdot)$, $T(\cdot)$, $R(\cdot)$,$a_1(\cdot)$, $P_1(\cdot)$, as well as functions defining the Jacobians of $Z(\cdot)$ and $T(\cdot)$ and the prior distribution for $\theta$ must be defined by user as a external pointers to `C++` functions. +As some of the model matrices may depend on the current state $\alpha_t$, constructing for example $T(t,\alpha_t,\theta)$ by calling user-defined `R` function is not feasible, as this should be done repeatedly within the particle filter which would negate the benefits of the whole `C++` implementation of the particle filter and Markov chain Monte Carlo. Therefore the functions $T(\cdot)$, $H(\cdot)$, $T(\cdot)$, $R(\cdot)$,$a_1(\cdot)$, $P_1(\cdot)$, as well as functions defining the Jacobians of $Z(\cdot)$ and $T(\cdot)$ and the prior distribution for $\theta$ must be defined by user as a external pointers to `C++` functions. For the log-density of theta, we can call R's own C-level density functions, for example `R::dnorm`` (see the template for an example). As an example, a logistic growth model of form $$ From eb27593337ed410bc68ed036e02512ccd30a5b47 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 22:11:02 +0200 Subject: [PATCH 128/180] added progress bar, getting ready for 2.0.0 --- DESCRIPTION | 4 +- NEWS | 10 +- R/RcppExports.R | 44 +++--- R/as_draws.R | 4 +- R/asymptotic_var.R | 6 +- R/bssm-package.R | 2 +- R/check_arguments.R | 7 +- R/check_diagnostics.R | 3 +- R/cpp_example_models.R | 136 ++++++++++------- R/models.R | 15 +- R/particle_smoother.R | 1 - R/post_correction.R | 9 +- R/predict.R | 6 +- R/print_mcmc.R | 4 +- R/priors.R | 5 +- R/run_mcmc.R | 75 +++++++--- R/srr-stats-standards.R | 233 ++++------------------------- README.Rmd | 122 ++------------- README.md | 119 ++++++--------- man/asymptotic_var.Rd | 6 +- man/bsm_ng.Rd | 3 +- man/particle_smoother.Rd | 1 - man/post_correct.Rd | 6 +- man/run_mcmc.Rd | 8 +- man/ssm_nlg.Rd | 8 +- src/R_mcmc.cpp | 37 ++--- src/R_sde.cpp | 12 +- src/RcppExports.cpp | 99 ++++++------ src/approx_mcmc.cpp | 114 ++++++++++++-- src/approx_mcmc.h | 9 +- src/mcmc.cpp | 144 ++++++++++++++---- src/mcmc.h | 3 +- tests/testthat/test_mcmc.R | 39 +++-- tests/testthat/test_models.R | 4 +- tests/testthat/test_post_correct.R | 5 +- tests/testthat/test_priors.R | 4 +- tests/testthat/test_sde.R | 4 +- vignettes/bssm.Rmd | 4 +- 38 files changed, 656 insertions(+), 659 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1940bc18..c2091b47 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 1.1.8 +Version: 2.0.0 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -44,7 +44,7 @@ Imports: posterior, Rcpp (>= 0.12.3), tidyr -LinkingTo: Rcpp, RcppArmadillo, ramcmc, sitmo +LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues diff --git a/NEWS b/NEWS index 26b528da..51dd3e67 100644 --- a/NEWS +++ b/NEWS @@ -1,5 +1,6 @@ -bssm 1.1.8 (Release date: -) +bssm 2.0.0 (Release date: -) ============== + * Added a progress bar for run_mcmc. * Added a fitted method for extraction of summary statistics of posterior predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. * Rewrote the summary method completely, which now returns data.frame. This @@ -8,7 +9,12 @@ bssm 1.1.8 (Release date: -) also contain alternative methods based on the posterior package. * New function estimate_ess can be used to compute effective sample size from weighted MCMC. - * Large number of new tests, and improved documentation. + * Added compatibility with the posterior package by defining as_draws + method for converting run_mcmc output to draws object. + * New function check_diagnostics for quick glance of ESS and Rhat values. + * Large number of new tests, and improved documentation with added examples. + * Large number of internal tweaks so that the package complies with + goodpractices package and Ropensci statistical software standards. bssm 1.1.7 (Release date: 2021-09-15) diff --git a/R/RcppExports.R b/R/RcppExports.R index 290bcbb6..047ab420 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -65,36 +65,36 @@ nonlinear_loglik <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf .Call('_bssm_nonlinear_loglik', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) } -gaussian_mcmc <- function(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) { - .Call('_bssm_gaussian_mcmc', PACKAGE = 'bssm', model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) +gaussian_mcmc <- function(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type, verbose) { + .Call('_bssm_gaussian_mcmc', PACKAGE = 'bssm', model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type, verbose) } -nongaussian_pm_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { - .Call('_bssm_nongaussian_pm_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) +nongaussian_pm_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose) { + .Call('_bssm_nongaussian_pm_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose) } -nongaussian_da_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) { - .Call('_bssm_nongaussian_da_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) +nongaussian_da_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose) { + .Call('_bssm_nongaussian_da_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose) } -nongaussian_is_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) { - .Call('_bssm_nongaussian_is_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) +nongaussian_is_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx, verbose) { + .Call('_bssm_nongaussian_is_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx, verbose) } -nonlinear_pm_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { - .Call('_bssm_nonlinear_pm_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) +nonlinear_pm_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose) { + .Call('_bssm_nonlinear_pm_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose) } -nonlinear_da_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) { - .Call('_bssm_nonlinear_da_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) +nonlinear_da_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose) { + .Call('_bssm_nonlinear_da_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose) } -nonlinear_ekf_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) { - .Call('_bssm_nonlinear_ekf_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) +nonlinear_ekf_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type, verbose) { + .Call('_bssm_nonlinear_ekf_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type, verbose) } -nonlinear_is_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) { - .Call('_bssm_nonlinear_is_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) +nonlinear_is_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx, verbose) { + .Call('_bssm_nonlinear_is_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx, verbose) } R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) { @@ -165,16 +165,16 @@ bsf_smoother_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffu .Call('_bssm_bsf_smoother_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) } -sde_pm_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { - .Call('_bssm_sde_pm_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) +sde_pm_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose) { + .Call('_bssm_sde_pm_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose) } -sde_da_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) { - .Call('_bssm_sde_da_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) +sde_da_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose) { + .Call('_bssm_sde_da_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose) } -sde_is_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) { - .Call('_bssm_sde_is_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) +sde_is_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type, verbose) { + .Call('_bssm_sde_is_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type, verbose) } sde_state_sampler_bsf_is2 <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) { diff --git a/R/as_draws.R b/R/as_draws.R index 16eed520..9dfaad13 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -80,4 +80,6 @@ as_draws_df.mcmc_output <- function(x, times, states, ...) { as_draws(d) } #' @exportS3Method posterior::as_draws mcmc_output -as_draws.mcmc_output <- function(x, times, ...) as_draws_df.mcmc_output(x, times, ...) +as_draws.mcmc_output <- function(x, times, ...) { + as_draws_df.mcmc_output(x, times, ...) +} diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 8770f645..3918bdf0 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -36,9 +36,9 @@ iact <- function(x) { #' #' The asymptotic variance MCMCSE^2 is based on Corollary 1 #' of Vihola et al. (2020) from weighted samples from IS-MCMC. The default -#' method is based on the integrated autocorrelation time (IACT) by Sokal (1997) -#' which seem to work well for reasonable problems, but it is also possible to -#' use the Geyer's method as implemented in \code{ess_mean} of the +#' method is based on the integrated autocorrelation time (IACT) by Sokal +#' (1997) which seem to work well for reasonable problems, but it is also +#' possible to use the Geyer's method as implemented in \code{ess_mean} of the #' \code{posterior} package. #' #' @importFrom posterior ess_mean diff --git a/R/bssm-package.R b/R/bssm-package.R index bb1d8c22..00dd23b6 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -118,7 +118,7 @@ NULL #' See example for code for reproducing the data. This was used in #' Helske and Vihola (2021). #' -#' @srrstats {G5.0, G5.1, G5.4} used in Helske and Vihola (2021). +#' @srrstats {G5.0, G5.1, G5.4, BS7.2} used in Helske and Vihola (2021). #' @name negbin_series #' @docType data #' @format A time series \code{mts} object with 200 time points and two series. diff --git a/R/check_arguments.R b/R/check_arguments.R index e04f9777..c30072da 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -76,6 +76,8 @@ check_period <- function(x, n) { } x } +#' @srrstats {BS2.5} Checks that observations are compatible with their +#' distributions are made. #' @rdname check check_distribution <- function(x, distribution) { for (i in seq_len(ncol(x))) { @@ -103,7 +105,8 @@ check_sd <- function(x, type, add_prefix = TRUE) { param <- type } if (length(x) != 1) { - stop(paste0("Argument ", param, " must be of length one (scalar or bssm_prior).")) + stop(paste0("Argument ", param, + " must be of length one (scalar or bssm_prior).")) } if (!is.numeric(x)) { stop(paste0("Argument ", param, " must be numeric.")) @@ -426,7 +429,7 @@ check_theta <- function(x) { stop("Argument 'theta' should be a numeric vector.") } if (is.null(names(x))) { - names(x) <- paste("theta_", 1:length(x)) + names(x) <- paste("theta_", seq_len(length(x))) } x } diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 07220dd7..ad881906 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -76,7 +76,8 @@ check_diagnostics <- function(x) { "and ESS measures below.\n", sep="") } - sumr <- posterior::summarise_draws(draws, posterior::default_convergence_measures()) + sumr <- posterior::summarise_draws(draws, + posterior::default_convergence_measures()) min_ess <- which.min(sumr$ess_bulk) cat("\nSmallest bulk-ESS: ", round(sumr$ess_bulk[min_ess]), " (", sumr$variable[min_ess], ")", sep = "") diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index dfed282a..18522492 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -79,7 +79,7 @@ cpp_example_model <- function(example, return_code = FALSE) { return log_pdf; } - // Function which returns the pointers to above functions (no need to modify) + // Function returning the pointers to above functions (no need to modify) // [[Rcpp::export]] Rcpp::List create_xptrs() { @@ -94,9 +94,12 @@ cpp_example_model <- function(example, return_code = FALSE) { return Rcpp::List::create( Rcpp::Named("drift") = Rcpp::XPtr(new fnPtr(&drift)), Rcpp::Named("diffusion") = Rcpp::XPtr(new fnPtr(&diffusion)), - Rcpp::Named("ddiffusion") = Rcpp::XPtr(new fnPtr(&ddiffusion)), - Rcpp::Named("prior") = Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), - Rcpp::Named("obs_density") = Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); + Rcpp::Named("ddiffusion") = + Rcpp::XPtr(new fnPtr(&ddiffusion)), + Rcpp::Named("prior") = + Rcpp::XPtr(new prior_fnPtr(&log_prior_pdf)), + Rcpp::Named("obs_density") = + Rcpp::XPtr(new obs_fnPtr(&log_obs_density))); } ' }, @@ -218,8 +221,10 @@ cpp_example_model <- function(example, return_code = FALSE) { // Function for the observational level standard deviation // [[Rcpp::export]] - arma::mat H_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat H(1,1); H(0, 0) = exp(theta(3)); return H; @@ -227,8 +232,10 @@ cpp_example_model <- function(example, return_code = FALSE) { // Function for the Cholesky of state level covariance // [[Rcpp::export]] - arma::mat R_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat R(1, 1); R(0, 0) = exp(theta(2)); return R; @@ -237,14 +244,18 @@ cpp_example_model <- function(example, return_code = FALSE) { // Z function // [[Rcpp::export]] - arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, const + arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + return exp(alpha); } // Jacobian of Z function // [[Rcpp::export]] - arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat Z_gn(1, 1); Z_gn(0, 0) = exp(alpha(0)); return Z_gn; @@ -252,16 +263,18 @@ cpp_example_model <- function(example, return_code = FALSE) { // T function // [[Rcpp::export]] - arma::vec T_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { return theta(0) * (1 - theta(1)) + theta(1) * alpha; } // Jacobian of T function // [[Rcpp::export]] - arma::mat T_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { arma::mat Tg(1, 1); Tg(0, 0) = theta(1); @@ -276,8 +289,8 @@ cpp_example_model <- function(example, return_code = FALSE) { double log_pdf = R::dnorm(theta(0), 0, 10, 1) + // N(0,10) for mu R::dbeta(theta(1), 2, 2, 1) + // beta(2, 2) for rho - R::dnorm(exp(theta(2)), 0, 1, 1) + theta(2) + //half-N(0, 1) for sigmas - R::dnorm(exp(theta(3)), 0, 1, 1) + theta(3); + R::dnorm(exp(theta(2)), 0, 1, 1) + theta(2) + + R::dnorm(exp(theta(3)), 0, 1, 1) + theta(3);//half-N(0, 1) for sigmas return log_pdf; } @@ -285,21 +298,27 @@ cpp_example_model <- function(example, return_code = FALSE) { // [[Rcpp::export]] Rcpp::List create_xptrs() { // typedef for a pointer of nonlinear function of model equation returning vec - typedef arma::vec (*vec_fnPtr)(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + typedef arma::vec (*vec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear function of model equation returning mat - typedef arma::mat (*mat_fnPtr)(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, + // for a pointer of nonlinear function of model equation returning mat + typedef arma::mat (*mat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear function of model equation returning vec - typedef arma::vec (*vec_initfnPtr)(const arma::vec& theta, const arma::vec& known_params); - // typedef for a pointer of nonlinear function of model equation returning mat - typedef arma::mat (*mat_initfnPtr)(const arma::vec& theta, const arma::vec& known_params); + // for a pointer of nonlinear function of model equation returning vec + typedef arma::vec (*vec_initfnPtr)(const arma::vec& theta, + const arma::vec& known_params); + // for a pointer of nonlinear function of model equation returning mat + typedef arma::mat (*mat_initfnPtr)(const arma::vec& theta, + const arma::vec& known_params); // typedef for a pointer of log-prior function typedef double (*double_fnPtr)(const arma::vec&); return Rcpp::List::create( - Rcpp::Named("a1_fn") = Rcpp::XPtr(new vec_initfnPtr(&a1_fn)), - Rcpp::Named("P1_fn") = Rcpp::XPtr(new mat_initfnPtr(&P1_fn)), + Rcpp::Named("a1_fn") = + Rcpp::XPtr(new vec_initfnPtr(&a1_fn)), + Rcpp::Named("P1_fn") = + Rcpp::XPtr(new mat_initfnPtr(&P1_fn)), Rcpp::Named("Z_fn") = Rcpp::XPtr(new vec_fnPtr(&Z_fn)), Rcpp::Named("H_fn") = Rcpp::XPtr(new mat_fnPtr(&H_fn)), Rcpp::Named("T_fn") = Rcpp::XPtr(new vec_fnPtr(&T_fn)), @@ -345,8 +364,10 @@ cpp_example_model <- function(example, return_code = FALSE) { // Function for the observational level standard deviation // [[Rcpp::export]] - arma::mat H_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat H_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat H(1,1); H(0, 0) = exp(theta(0)); return H; @@ -354,8 +375,10 @@ cpp_example_model <- function(example, return_code = FALSE) { // Function for the Cholesky of state level covariance // [[Rcpp::export]] - arma::mat R_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat R_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat R(2, 2, arma::fill::zeros); R(0, 0) = exp(theta(1)); R(1, 1) = exp(theta(2)); @@ -364,16 +387,20 @@ cpp_example_model <- function(example, return_code = FALSE) { // Z function // [[Rcpp::export]] - arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::vec Z_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::vec tmp(1); tmp(0) = alpha(1); return tmp; } // Jacobian of Z function // [[Rcpp::export]] - arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat Z_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { + arma::mat Z_gn(1, 2); Z_gn(0, 0) = 0.0; Z_gn(0, 1) = 1.0; @@ -382,8 +409,9 @@ cpp_example_model <- function(example, return_code = FALSE) { // T function // [[Rcpp::export]] - arma::vec T_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::vec T_fn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { double dT = known_params(0); double K = known_params(1); @@ -398,18 +426,21 @@ cpp_example_model <- function(example, return_code = FALSE) { // Jacobian of T function // [[Rcpp::export]] - arma::mat T_gn(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, - const arma::vec& known_params, const arma::mat& known_tv_params) { + arma::mat T_gn(const unsigned int t, const arma::vec& alpha, + const arma::vec& theta, const arma::vec& known_params, + const arma::mat& known_tv_params) { double dT = known_params(0); double K = known_params(1); double r = exp(alpha(0)) / (1 + exp(alpha(0))); - double tmp = exp(r * dT) / std::pow(K + alpha(1) * (exp(r * dT) - 1), 2); + double tmp = + exp(r * dT) / std::pow(K + alpha(1) * (exp(r * dT) - 1), 2); arma::mat Tg(2, 2); Tg(0, 0) = 1.0; Tg(0, 1) = 0; - Tg(1, 0) = dT * K * alpha(1) * (K - alpha(1)) * tmp * r / (1 + exp(alpha(0))); + Tg(1, 0) = + dT * K * alpha(1) * (K - alpha(1)) * tmp * r / (1 + exp(alpha(0))); Tg(1, 1) = K * K * tmp; return Tg; @@ -423,7 +454,8 @@ cpp_example_model <- function(example, return_code = FALSE) { // Note that the sampling is on log-scale, // so we need to add jacobians of the corresponding transformations // we could also sample on natural scale with check such as - // if(arma::any(theta < 0)) return -std::numeric_limits::infinity(); + // if(arma::any(theta < 0)) + // return -std::numeric_limits::infinity(); // but this would be less efficient. // You can use R::dnorm and similar functions, see, e.g. @@ -441,17 +473,21 @@ cpp_example_model <- function(example, return_code = FALSE) { // [[Rcpp::export]] Rcpp::List create_xptrs() { - // typedef for a pointer of nonlinear function of model equation returning vec (T, Z) - typedef arma::vec (*nvec_fnPtr)(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); - // typedef for a pointer of nonlinear function returning mat (Tg, Zg, H, R) - typedef arma::mat (*nmat_fnPtr)(const unsigned int t, const arma::vec& alpha, - const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); + // typedef for a pointer of nonlinear function returning vec (T, Z) + typedef arma::vec (*nvec_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); + // for a pointer of nonlinear function returning mat (Tg, Zg, H, R) + typedef arma::mat (*nmat_fnPtr)(const unsigned int t, + const arma::vec& alpha, const arma::vec& theta, + const arma::vec& known_params, const arma::mat& known_tv_params); // typedef for a pointer returning a1 - typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, const arma::vec& known_params); + typedef arma::vec (*a1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); // typedef for a pointer returning P1 - typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, const arma::vec& known_params); + typedef arma::mat (*P1_fnPtr)(const arma::vec& theta, + const arma::vec& known_params); // typedef for a pointer of log-prior function typedef double (*prior_fnPtr)(const arma::vec& theta); diff --git a/R/models.R b/R/models.R index 7ed4534a..e7633696 100644 --- a/R/models.R +++ b/R/models.R @@ -1,8 +1,9 @@ +#' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower used where applicable. #' @srrstats {G2.7, G2.8, G2.9} Only matrix/mts/arrays as tabular data are #' supported, not data.frame or similar objects. -#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, BS3.0} Missing observations are handled -#' automatically as per SSM theory, whereas missing values are not allowed -#' elsewhere. +#' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, BS3.0} Missing observations are +#' handled automatically as per SSM theory, whereas missing values are not +#' allowed elsewhere. #' @srrstats {BS1.0, BS1.1, BS1.2} NULL @@ -1472,10 +1473,10 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' #' @param y Observations as multivariate time series (or matrix) of length #' \eqn{n}. -#' @param Z,H,T,R An external pointers (object of class \code{externalptr}) for the -#' C++ functions which define the corresponding model functions. -#' @param Z_gn,T_gn An external pointers (object of class \code{externalptr}) for -#' the C++ functions which define the gradients of the corresponding model +#' @param Z,H,T,R An external pointers (object of class \code{externalptr}) +#' for the C++ functions which define the corresponding model functions. +#' @param Z_gn,T_gn An external pointers (object of class \code{externalptr}) +#' for the C++ functions which define the gradients of the corresponding model #' functions. #' @param a1 Prior mean for the initial state as object of class #' \code{externalptr} diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 9643b43c..55de7efc 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -48,7 +48,6 @@ #' Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). #' The unscented particle filter. #' In Advances in neural information processing systems, p 584-590. -#' https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf #' #' Jazwinski, A 1970. Stochastic Processes and Filtering Theory. #' Academic Press. diff --git a/R/post_correction.R b/R/post_correction.R index ba21389a..d1286f3c 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -155,13 +155,13 @@ suggest_N <- function(model, theta, #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. #' @references -#' A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). +#' Doucet A, Pitt M K, Deligiannidis G, Kohn R (2018). #' Efficient implementation of Markov chain Monte Carlo when using an unbiased #' likelihood estimator. Biometrika, 102, 2, 295-313, #' https://doi.org/10.1093/biomet/asu075 #' -#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. +#' Vihola M, Helske J, Franks J (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @examples @@ -246,7 +246,8 @@ post_correct <- function(model, mcmc_output, particles, threads = 1L, if (!inherits(mcmc_output, "mcmc_output")) stop("Object 'mcmc_output' is not valid output from 'run_mcmc'.") - is_type <- pmatch(match.arg(tolower(is_type), paste0("is", 1:3)), paste0("is", 1:3)) + is_type <- pmatch(match.arg(tolower(is_type), paste0("is", 1:3)), + paste0("is", 1:3)) a <- proc.time() if (inherits(model, "nongaussian")) { diff --git a/R/predict.R b/R/predict.R index 66822714..a07c046a 100644 --- a/R/predict.R +++ b/R/predict.R @@ -243,11 +243,13 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { if (!identical(nrow(object$alpha) - 1L, nrow(model$y))) { - stop("Number of observations of the model and MCMC output do not match.") + stop(paste0("Number of observations in the model and MCMC output do ", + "not match.")) } } else { if (!identical(nrow(object$alpha) - 1L, length(model$y))) { - stop("Number of observations of the model and MCMC output do not match.") + stop(paste0("Number of observations in the model and MCMC output do ", + "not match.")) } } w <- object$counts * diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 1d0daf7c..052c0f40 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -32,7 +32,7 @@ #' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @export -#' @srrstats {BS6.4} +#' @srrstats {BS5.3, BS5.5, BS6.4} summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", probs = c(0.025, 0.975), times, states, method = "sokal", ...) { @@ -100,7 +100,7 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", #' @importFrom stats var #' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. #' @param ... Ignored. -#' @srrstats {BS6.0} +#' @srrstats {BS5.3, BS5.5, BS6.0} #' @export print.mcmc_output <- function(x, ...) { diff --git a/R/priors.R b/R/priors.R index c3854d94..81554884 100644 --- a/R/priors.R +++ b/R/priors.R @@ -28,7 +28,10 @@ #' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case #' of multiple priors (i.e. multiple regression coefficients). #' @export -#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6} Correct prior definitions. +#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6, BS2.7} Explains prior definitions and +#' initial values. +#' @srrstats {BS2.5} Checks are in place for the distributional parameters of +#' priors and their initial values. #' @examples #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: #' uniform(init = 0.2, min = -1.0, max = 1.0) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 40cc310e..7db418e6 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -106,16 +106,23 @@ #' @param L_c,L_f For \code{ssm_sde} models, Positive integer values defining #' the discretization levels for first and second stages (defined as 2^L). #' For pseudo-marginal methods (\code{"pm"}), maximum of these is used. +#' @param verbose If \code{TRUE} (default), prints a progress bar to the +#' console. Set to \code{FALSE} if number of iterations is less than 50. #' @param ... Ignored. #' @export +#' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower used where applicable. #' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} #' @srrstats {BS2.6} +#' @srrstats {BS2.7} Illustrated in the examples. #' @srrstats {BS2.7, BS1.3, BS1.3a, BS1.3b, BS2.8} Explained in docs. #' @srrstats {BS2.9} The argument 'seed' is set to random value if not #' specified by the user. #' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the #' input model, whereas some metadata (like the class of input model and seed) #' is returned by run_mcmc. +#' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off with +#' \code{verbose = FALSE}. +#' @srrstats {BS2.14} No warnings are issues during MCMC. #' @rdname run_mcmc #' @references #' Vihola M (2012). Robust adaptive Metropolis algorithm with @@ -164,7 +171,7 @@ run_mcmc <- function(model, ...) { run_mcmc.gaussian <- function(model, iter, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, - seed = sample(.Machine$integer.max, size = 1), ...) { + seed = sample(.Machine$integer.max, size = 1), verbose = TRUE, ...) { check_missingness(model) @@ -177,6 +184,9 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + if (iter < 50) verbose <- FALSE if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") @@ -202,7 +212,8 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } if(output_type == "full") { - nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { warning(paste("Number of state samples to be stored is approximately", @@ -211,7 +222,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", } out <- gaussian_mcmc(model, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, - end_adaptive_phase, threads, model_type(model)) + end_adaptive_phase, threads, model_type(model), verbose) if (output_type == 1) { colnames(out$alpha) <- names(model$a1) @@ -368,8 +379,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, local_approx = TRUE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, - conv_tol = 1e-8, ...) { - + conv_tol = 1e-8, verbose = TRUE, ...) { check_missingness(model) @@ -389,12 +399,18 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", } else { particles <- check_intmax(particles, "particles") } + threads <- check_intmax(threads, "threads") model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) + + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + if (iter < 50) verbose <- FALSE + if (!test_flag(local_approx)) { stop("Argument 'local_approx' should be TRUE or FALSE. ") } else model$local_approx <- local_approx @@ -423,7 +439,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", pmatch(model$distribution, dists, duplicates.ok = TRUE) - 1 if(output_type == "full") { - nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { warning(paste("Number of state samples to be stored is approximately", @@ -455,20 +472,18 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - - switch(mcmc_type, "da" = { out <- nongaussian_da_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, - sampling_method, model_type(model)) + sampling_method, model_type(model), verbose) }, "pm" = { out <- nongaussian_pm_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, - sampling_method, model_type(model)) + sampling_method, model_type(model), verbose) }, "is1" =, "is2" =, @@ -477,13 +492,14 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", particles, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, sampling_method, - pmatch(mcmc_type, paste0("is", 1:3)), model_type(model), FALSE) + pmatch(mcmc_type, paste0("is", 1:3)), model_type(model), FALSE, + verbose) }, "approx" = { out <- nongaussian_is_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, - sampling_method, 2, model_type(model), TRUE) + sampling_method, 2, model_type(model), TRUE, verbose) }) if (output_type == 1) { colnames(out$alpha) <- names(model$a1) @@ -528,8 +544,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, - conv_tol = 1e-8, iekf_iter = 0, ...) { - + conv_tol = 1e-8, iekf_iter = 0, verbose = TRUE, ...) { check_missingness(model) @@ -558,6 +573,10 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", burnin <- check_intmax(burnin, "burnin", max = 1e12) iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + if (iter < 50) verbose <- FALSE + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() @@ -581,13 +600,15 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", "'approx' or 'ekf' instead.", sep = " ")) if(output_type == "full") { - nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { warning(paste("Number of state samples to be stored is approximately", nsamples, "you might run out of memory.")) } } + out <- switch(mcmc_type, "da" = { nonlinear_da_mcmc(t(model$y), model$Z, model$H, model$T, @@ -597,7 +618,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, max_iter, conv_tol, - sampling_method, iekf_iter, output_type) + sampling_method, iekf_iter, output_type, verbose) }, "pm" = { nonlinear_pm_mcmc(t(model$y), model$Z, model$H, model$T, @@ -607,7 +628,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, max_iter, conv_tol, - sampling_method, iekf_iter, output_type) + sampling_method, iekf_iter, output_type, verbose) }, "is1" =, "is2" =, @@ -631,7 +652,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$known_tv_params, as.integer(model$time_varying), model$n_states, model$n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, - end_adaptive_phase, threads, iekf_iter, output_type) + end_adaptive_phase, threads, iekf_iter, output_type, verbose) }, "approx" = { nonlinear_is_mcmc(t(model$y), model$Z, model$H, model$T, @@ -642,7 +663,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, 2, sampling_method, max_iter, conv_tol, - iekf_iter, output_type, TRUE) + iekf_iter, output_type, TRUE, verbose) } ) if (output_type == 1) { @@ -686,7 +707,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", L_c, L_f, burnin = floor(iter/2), thin = 1, gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, - threads = 1, seed = sample(.Machine$integer.max, size = 1), ...) { + threads = 1, seed = sample(.Machine$integer.max, size = 1), verbose = TRUE, + ...) { check_missingness(model) @@ -714,7 +736,11 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) - + + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + if (iter < 50) verbose <- FALSE + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() @@ -745,6 +771,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", nsamples, "you might run out of memory.")) } } + out <- switch(mcmc_type, "da" = { out <- sde_da_mcmc(model$y, model$x0, model$positive, @@ -752,7 +779,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", model$prior_pdf, model$obs_pdf, model$theta, particles, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, - end_adaptive_phase, output_type) + end_adaptive_phase, output_type, verbose) }, "pm" = { @@ -761,7 +788,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", model$prior_pdf, model$obs_pdf, model$theta, particles, L, seed, iter, burnin, thin, gamma, target_acceptance, S, - end_adaptive_phase, output_type) + end_adaptive_phase, output_type, verbose) }, "is1" =, "is2" =, @@ -772,7 +799,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", particles, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, pmatch(mcmc_type, paste0("is", 1:3)), - threads, output_type) + threads, output_type, verbose) }) colnames(out$alpha) <- model$state_names diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index f86deb77..6ddc0a5b 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -8,211 +8,40 @@ #' (These comments may be deleted at any time.) #' #' @srrstatsVerbose TRUE -#' -# Standards for general statistical software -# General documentation, addressed by the paper vignette and the corresponding R Journal paper#' -#' @srrstats {G1.0} *Statistical Software should list at least one primary reference from published academic literature.* -#' @srrstats {G1.1} *Statistical Software should document whether the algorithm(s) it implements are:* - *The first implementation of a novel algorithm*; or - *The first implementation within **R** of an algorithm which has previously been implemented in other languages or contexts*; or - *An improvement on other implementations of similar algorithms in **R***. -#' @srrstats {G1.2} *Statistical Software should include a* Life Cycle Statement *describing current and anticipated future states of development.* -#' @srrstats {G1.3} *All statistical terminology should be clarified and unambiguously defined.* -#' @srrstats {G1.4} *Software should use [`roxygen2`](https://roxygen2.r-lib.org/) to document all functions.* -#' @srrstats {G1.4a} *All internal (non-exported) functions should also be documented in standard [`roxygen2`](https://roxygen2.r-lib.org/) format, along with a final `@noRd` tag to suppress automatic generation of `.Rd` files.* -#' @srrstats {G1.5} *Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.* -#' @srrstats {G1.6} *Software should include code necessary to compare performance claims with alternative implementations in other R packages.* -#' -#' As tested by autotest -#' @srrstats {G2.0} *Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.* -#' @srrstats {G2.0a} *Provide explicit secondary documentation of any expectations on lengths of inputs.* -#' @srrstats {G2.1} *Implement assertions on types of inputs (see the initial point on nomenclature above).* -#' @srrstats {G2.1a} *Provide explicit secondary documentation of expectations on data types of all vector inputs.* -#' @srrstats {G2.2} *Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.* -#' -#' Explicit conversions are used where necessary -#' @srrstats {G2.4} *Provide appropriate mechanisms to convert between different data types, potentially including:* -#' @srrstats {G2.4a} *explicit conversion to `integer` via `as.integer()`* -#' @srrstats {G2.4b} *explicit conversion to continuous via `as.numeric()`* -#' @srrstats {G2.4c} *explicit conversion to character via `as.character()` (and not `paste` or `paste0`)* -#' @srrstats {G2.6} *Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.* -#' -#' match.arg and tolower are used where applicable. -#' @srrstats {G2.3} *For univariate character input:* -#' @srrstats {G2.3a} *Use `match.arg()` or equivalent where applicable to only permit expected values.* -#' @srrstats {G2.3b} *Either: use `tolower()` or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.* -#' -#' Only matrix/mts/arrays are supported, not data.frame style objects. -#' @srrstats {G2.7} *Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.* -#' @srrstats {G2.8} *Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.* -#' @srrstats {G2.9} *Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with [`sf`-format](https://r-spatial.github.io/sf/) data) or added (such as insertion of variable or column names where none were provided).* -#' -#' Missing observations (y) are handled automatically as per SSM theory, whereas missing values are not allowed elsewhere. Inputing or ignoring them does not make sense in time series context. -#' @srrstats {G2.14} *Where possible, all functions should provide options for users to specify how to handle missing (`NA`) data, with options minimally including:* -#' @srrstats {G2.14a} *error on missing data* -#' @srrstats {G2.14b} *ignore missing data with default warnings or messages issued* -#' @srrstats {G2.14c} *replace missing data with appropriately imputed values* -#' @srrstats {G2.15} *Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default `na.rm = FALSE`-type parameters (such as [`mean()`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/mean.html), [`sd()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/sd.html) or [`cor()`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html)).* -#' @srrstats {G2.16} *All functions should also provide options to handle undefined values (e.g., `NaN`, `Inf` and `-Inf`), including potentially ignoring or removing such values.* -#' -#' @srrstats {G3.0} *Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.* -#' -#' Simulated datasets are used in several occasions -#' @srrstats {G5.0} *Where applicable or practicable, tests should use standard data sets with known properties (for example, the [NIST Standard Reference Datasets](https://www.itl.nist.gov/div898/strd/), or data sets provided by other widely-used R packages).* -#' @srrstats {G5.1} *Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.* -#' -#' @srrstats {G5.2} *Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,* -#' @srrstats {G5.2a} *Every message produced within R code by `stop()`, `warning()`, `message()`, or equivalent should be unique* -#' @srrstats {G5.2b} *Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.* -#' @srrstats {G5.3} *For functions which are expected to return objects containing no missing (`NA`) or undefined (`NaN`, `Inf`) values, the absence of any such values in return objects should be explicitly tested.* -#' -#' Correctness is demonstrated in Helske & Vihola (2021) (comparison with Stan) and Vihola & Helske & Franks (2020) (correcness of the IS-MCMC etc) -#' Full correctness tests cannot be performed without overly extensive time requirements due to Monte Carlo variation. -#' @srrstats {G5.4} **Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as [RStata](https://github.com/lbraglia/RStata)).* -#' @srrstats {G5.4a} *For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.* -#' @srrstats {G5.4b} *For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.* -#' @srrstats {G5.4c} *Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available* -#' @srrstats {G5.5} *Correctness tests should be run with a fixed random seed* -#' @srrstats {G5.6} **Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.* -#' @srrstats {G5.6a} *Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.* -#' @srrstats {G5.6b} *Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).* -#' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.* -#' -#' Tested by autotest and testthat tests -#' @srrstats {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:* -#' @srrstats {G5.8a} *Zero-length data* -#' @srrstats {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)* -#' @srrstats {G5.8c} *Data with all-`NA` fields or columns or all identical fields or columns* -#' @srrstats {G5.8d} *Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)* -#' -#' Tested by autotest and testthat tests -#' @srrstats {G5.9} **Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:* -#' @srrstats {G5.9a} *Adding trivial noise (for example, at the scale of `.Machine$double.eps`) to data does not meaningfully change results* -#' @srrstats {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results* -#' -#' Standards for Bayesian software -#' -#' Addressed by the vignette, the corresponding R Journal paper, models.R and run_mcmc.R -#' @srrstats {BS1.0} *Bayesian software which uses the term "hyperparameter" should explicitly clarify the meaning of that term in the context of that software.* -#' @srrstats {BS1.1} *Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.* -#' @srrstats {BS1.2} *Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:* -#' @srrstats {BS1.2a} *The main package `README`, either as textual description or example code* -#' @srrstats {BS1.2b} *At least one package vignette, both as general and applied textual descriptions, and example code* -#' @srrstats {BS1.2c} *Function-level documentation, preferably with code included in examples* -#' @srrstats {BS1.3} *Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:* -#' @srrstats {BS1.3a} *Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.* -#' @srrstats {BS1.3b} *Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.* -#' @srrstats {BS2.8} *Enable results of previous runs to be used as starting points for subsequent runs.* -#' @srrstats {BS4.0} *Packages should document sampling algorithms (generally via literary citation, or reference to other software)* -#' @srrstats {BS4.1} *Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).* -#' @srrstats {BS5.3} *Bayesian Software should return convergence statistics or equivalent* -#' @srrstats {BS5.5} *Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.* -#' -#' Tested by autotest and package documentation -#' @srrstats {BS2.1} *Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.* -#' @srrstats {BS2.1a} *The effects of such routines should be tested.* -#' -#' Handled by the prior construction functions -#' @srrstats {BS2.2} *Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.* -#' @srrstats {BS2.3} *Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).* -#' @srrstats {BS2.4} *Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)* -#' @srrstats {BS2.5} *Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.* -#' -#' Checked, explained and done in run_mcmc -#' @srrstats {BS2.6} *Check that values for computational parameters lie within plausible ranges.* -#' @srrstats {BS2.7} *Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational "chains."* -#' @srrstats {BS2.9} *Ensure each chain is started with a different seed by default.* -#' -#' As with the the general standards. -#' @srrstats {BS3.0} *Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (`NA`, `Inf`) values, and that such values, or entire rows including any such values, will be automatically removed from input data.* -#' -#' run_mcmc returns some of these, other are embedded in the input model itself (starting values) -#' @srrstats {BS5.0} *Return values should include starting value(s) or seed(s), including values for each sequence where multiple sequences are included* -#' @srrstats {BS5.1} *Return values should include appropriate metadata on types (or classes) and dimensions of input data* -#' @srrstats {BS5.2} *Bayesian Software should either return the input function or prior distributional specification in the return object; or enable direct access to such via additional functions which accept the return object as single argument.* -#' -#' Implemented. -#' @srrstats {BS6.0} *Software should implement a default `print` method for return objects* -#' @srrstats {BS6.4} *Software may provide `summary` methods for return objects* -#' -#' Demonstrated in the vignettes, examples, papers and tests -#' @srrstats {BS7.0} *Software should demonstrate and confirm recovery of parametric estimates of a prior distribution* -#' @srrstats {BS7.1} *Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information* -#' @srrstats {BS7.2} *Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data* -#' @srrstats {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* -#' @srrstats {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* -#' -#' Demonstrated in test_mcmc.R. -#' The scales do not matter (in terms of runtime) in random walk Metropolis nor in particle filters, as long as numerical issues are not encountered -#' @srrstats {BS7.3} *Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.* -#' @srrstats {BS7.4a} *The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.* -#' #' @noRd NULL #' NA_standards #' -#' Any non-applicable standards can have their tags changed from `@srrstatsTODO` -#' to `@srrstatsNA`, and placed together in this block, along with explanations -#' for why each of these standards have been deemed not applicable. -#' (These comments may also be deleted at any time.) -#' -#' General standards not applicable -#' -#' Factor types are not used nor supported: -#' @srrstatsNA {G2.4d} *explicit conversion to factor via `as.factor()`* -#' @srrstatsNA {G2.4e} *explicit conversion from factor via `as...()` functions* -#' @srrstatsNA {G2.5} *Where inputs are expected to be of `factor` type, secondary documentation should explicitly state whether these should be `ordered` or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.* -#' -#' No data.frame style tabular data is used/supported as input -#' @srrstatsNA {G2.10} *Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.* -#' @srrstatsNA {G2.11} *Software should ensure that `data.frame`-like tabular objects which have columns which do not themselves have standard class attributes (typically, `vector`) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the [`units` package](https://github.com/r-quantities/units/) provide a good test case.* -#' @srrstatsNA {G2.12} *Software should ensure that `data.frame`-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.* -#' @srrstatsNA {G2.13} *Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.* -#' -#' @srrstatsNA {G3.1} *Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the `stats::cov` function.* -#' @srrstatsNA {G3.1a} *The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).* -#' -#' No output is written to local files -#' @srrstatsNA {G4.0} *Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.* -#' -#' Package does not contain extended tests (although benchmarks folder contains template for running such very time-consuming tests) -#' @srrstatsNA {G5.10} *Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a `_EXTENDED_TESTS=1` environment variable.* -#' @srrstatsNA {G5.11} *Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.* -#' @srrstatsNA {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.* -#' @srrstatsNA {G5.12} *Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a `CONTRIBUTING.md` or `tests/README.md` file.* -#' -#' Bayesian standards not applicable -#' -#' Not applicable as only single-chain runs are supported. -#' @srrstatsNA {BS2.10} *Issue diagnostic messages when identical seeds are passed to distinct computational chains.* -#' Starting values are not accepted in this form. -#' @srrstatsNA {BS2.11} *Software which accepts starting values as a vector should provide the parameter with a plural name: for example, "starting_values" and not "starting_value".* -#' -#' No automatic stopping at converge (converge checkers) is supported -#' @srrstatsNA {BS1.4] *For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.* -#' @srrstatsNA {BS1.5} *For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.* -#' @srrstatsNA {BS4.3} *Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.* -#' @srrstatsNA {BS4.4} *Enable computations to be stopped on convergence (although not necessarily by default).* -#' @srrstatsNA {BS4.5} *Ensure that appropriate mechanisms are provided for models which do not converge.* -#' @srrstatsNA {BS4.6} *Implement tests to confirm that results with convergence checker are statistically equivalent to results from equivalent fixed number of samples without convergence checking.* -#' @srrstatsNA {BS4.7} *Where convergence checkers are themselves parametrised, the effects of such parameters should also be tested. For threshold parameters, for example, lower values should result in longer sequence lengths.* -#' @srrstatsNA {BS5.4} *Where multiple checkers are enabled, Bayesian Software should return details of convergence checker used* -#' -#' Of course applicable, but at the moment this is not done (issue has been open several years in github...). -#' @srrstatsNA {BS2.12} *Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.* -#' @srrstatsNA {BS2.13} *Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.* -#' @srrstatsNA {BS2.14} *Bayesian Software should enable suppression of warnings where appropriate. This should be tested.* -#' @srrstatsNA {BS2.15} *Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.* -#' -#' Not really relevant for SSMs, or at least difficult to check this kind of thing in general -#' @srrstatsNA {BS3.1} *Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings* -#' @srrstatsNA {BS3.2} *Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms* -#' -#' Just suggests using ggplot or bayesplot packages, with several examples -#' @srrstatsNA {BS6.1} *Software should implement a default `plot` method for return objects* -#' @srrstatsNA {BS6.2} *Software should provide and document straightforward abilities to plot sequences of posterior samples, with burn-in periods clearly distinguished* -#' @srrstatsNA {BS6.3} *Software should provide and document straightforward abilities to plot posterior distributional estimates* -#' @srrstatsNA {BS6.5} *Software may provide abilities to plot both sequences of posterior samples and distributional estimates together in single graphic* -#' -#' The computation of posterior predicted/fitted value is somewhat time consuming so it is not done automatically (there is a fitted/predict functions for that) -#' @srrstatsNA {BS7.4} *Bayesian software should implement tests which confirm that predicted or fitted values are on (approximately) the same scale as input values.* -#' +#' Any non-applicable standards can have their tags changed from +#' `@srrstatsTODO` to `@srrstatsNA`, and placed together in this block, +#' along with explanations for why each of these standards have been deemed not +#' applicable. (These comments may also be deleted at any time.) +#' +#' @srrstatsNA {G2.4d, G2.4e, G2.5} Factor types are not used nor supported. +#' @srrstatsNA {G2.10, G2.11, G2.12, G2.13} No data.frame style tabular data is +#' used/supported as input +#' @srrstatsNA {G3.1, G3.1a} No sample covariance calculations done. +#' @srrstatsNA {G4.0} No output is written to local files. +#' @srrstatsNA {G5.3} Some functions can produce NAs and nonfinite values, +#' and there are some checks for these (e.g. in C++ side) but not explicitly +#' tested everywhere. +#' @srrstatsNA {G5.10, G5.11, G5.11a, G5.12} Package does not contain extended +#' tests (although benchmarks folder contains template for running such very +#' time-consuming tests), although some of the automatic tests are switched off +#' for CRAN due to the time limits. +#' @srrstatsNA {BS2.10} Not applicable as only single-chain runs are supported +#' (but several such runs can be combined with posterior package). +#' @srrstatsNA {BS2.11} Starting values are not accepted in this form. +#' @srrstatsNA {BS1.4, BS1.5, BS4.3, BS4.4, BS4.5, BS4.6, BS4.7, BS5.4] No +#' support for automatic stopping at converge (converge checkers). +#' @srrstatsNA {BS2.15} Errors are normal R errors so they can be caught? But +#' not sure what is meant here. +#' @srrstatsNA {BS3.1, BS3.2} Not really relevant for SSMs, or at least +#' difficult to check this kind of thing in general. +#' @srrstatsNA {BS6.1, BS6.2, BS6.3, BS6.5} Just suggests and illustrates using +#' ggplot or bayesplot packages, with several examples.#' +#' @srrstatsNA {BS7.4} The computation of posterior predicted/fitted value can +#' be somewhat time consuming so it is not done automatically (there is a +#' fitted/predict functions for that) #' @noRd NULL diff --git a/README.Rmd b/README.Rmd index 479401a2..5b6d21ef 100644 --- a/README.Rmd +++ b/README.Rmd @@ -22,6 +22,11 @@ knitr::opts_chunk$set( #' #' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower are used where #' applicable. +#' @srrstats {G1.0, G1.1, G1.3, G1.4, G1.4a, G1.5, G1.6} General +#' documentation, addressed by the paper vignette and the corresponding R +#' Journal paper. +#' @srrstats {G2.4, G2.4a, G2.4b, G2.4c, G2.6} Explicit conversions are used +#' where necessary. #' #' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, G2.15, G2.16} Missing observations #' (y) are handled automatically as per SSM theory, whereas missing values are @@ -43,7 +48,12 @@ knitr::opts_chunk$set( #' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b, BS1.3b} Addressed in the #' models.R, run_mcmc.R, in vignettes and in the R Journal paper. #' -#' @srrstats {BS2.1, BS2.1a} Tested by autotest and package examples/tests. +#' @srrstats {BS2.1, BS2.1a, BS2.6} Tested and demonstrated by autotest and +#' package examples/tests. +#' @srrstats {BS7.4, BS7.4a} The scales do not matter (in terms of runtime) +#' in random walk Metropolis nor in particle filters, as long as numerical +#' issues are not encountered + ``` # bssm @@ -79,7 +89,7 @@ There are also couple posters and a talk related to IS-correction methodology an The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, and 311877. Current development is focused on -increased usability and stability. +increased usability. For recent changes, see NEWS file. ## Installation @@ -261,111 +271,3 @@ pred %>% filter(Variable == "Ozone") %>% theme_bw() ``` -## Recent changes (For all changes, see NEWS file.) - -#### bssm 1.1.6 (Release date: 2021-09-06) - -* Cleaned codes and added more comprehensive tests in line with pkgcheck -tests. This resulted in finding and fixing multiple bugs: -* Fixed a bug in EKF-based particle filter which returned filtered estimates -also in place of one-step ahead predictions. -* Fixed a bug which caused an error in suggest_N for nlg_ssm. -* Fixed a bug which caused incorrect sampling of smoothing distribution for -ar1_lg model when predicting past or when using simulation smoother. -* Fixed a bug which caused an error when predicting past values in -multivariate time series case. -* Fixed sampling of negative binomial distribution in predict method, which -used std::negative_binomial which converts non-integer phi to integer. -Sampling now uses Gamma-Poisson mixture for simulation. - -#### bssm 1.1.4 (Release date: 2021-04-13) - -* Better documentation for SV model, and changed ordering of arguments to emphasise the -recommended parameterization. -* Fixed predict method for SV model. - -#### bssm 1.1.3-2 (Release date: 2021-02-24) - -* Fixed missing parenthesis causing compilation fail in case of no OpenMP support. -* Added pandoc version >= 1.12.3 to system requirements. - -#### bssm 1.1.3-1 (Release date: 2021-02-22) - -* Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. -* Added vignette for SDE models. -* Updated citation information and streamlined the main vignette. - -#### bssm 1.1.2 (Release date: 2021-02-08) - -* Some bug fixes, see NEWS for details. - -#### bssm 1.1.0 (Release date: 2021-01-19) - - -* Added function `suggest_N` which can be used to choose -suitable number of particles for IS-MCMC. -* Added function `post_correct` which can be used to update -previous approximate MCMC with IS-weights. -* Gamma priors are now supported in easy-to-use models such as `bsm_lg`. -* The adaptation of the proposal distribution now continues also after the burn-in by default. -* Changed default MCMC type to typically most efficient and robust IS2. -* Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). -* Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. -This resulted error within MCMC algorithms. -* Fixed a dimension drop bug in the predict method which caused error for univariate models. -* Fixed few typos in vignette (thanks Kyle Hussman) and added more examples. - -#### bssm 1.0.1-1 (Release date: 2020-11-12) - - -* Added an argument `future` for predict method which allows -predictions for current time points by supplying the original model -(e.g., for posterior predictive checks). -At the same time the argument name `future_model` was changed to `model`. -* Fixed a bug in summary.mcmc_run which resulted error when -trying to obtain summary for states only. -* Added a check for Kalman filter for a degenerate case where all -observational level and state level variances are zero. -* Renamed argument `n_threads` to `threads` for consistency -with `iter` and `burnin` arguments. -* Improved documentation, added examples. -* Added a vignette regarding psi-APF for non-linear models. - -#### bssm 1.0.0 (Release date: 2020-06-09) - -Major update - -* Major changes for model definitions, now model updating and priors -can be defined via R functions (non-linear and SDE models still rely on C++ snippets). -* Added support for multivariate non-Gaussian models. -* Added support for gamma distributions. -* Added the function as.data.frame for mcmc output which converts the MCMC samples -to data.frame format for easier post-processing. -* Added truncated normal prior. -* Many argument names and model building functions have been changed for clarity and consistency. -* Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. -* Allow zero as initial value for positive-constrained parameters of bsm models. -* Small changes to summary method which can now return also only summaries of the states. -* Fixed a bug in initializing run_mcmc for negative binomial model. -* Fixed a bug in phi-APF for non-linear models. -* Reimplemented predict method which now always produces data frame of samples. - -#### bssm 0.1.11 (Release date: 2020-02-25) - -* Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, -as it seems to work better with noisy likelihood estimates. -* Print and summary methods for MCMC output are now coherent in their output. - -#### bssm 0.1.10 (Release date: 2020-02-04) - -* Fixed missing weight update for IS-SPDK without OPENMP flag. -* Removed unused usage argument ... from expand_sample. - -#### bssm 0.1.9 (Release date: 2020-01-27) - -* Fixed state sampling for PM-MCMC with SPDK. -* Added ts attribute for svm model. -* Corrected asymptotic variance for summary methods. - -For older versions, see NEWS file. - diff --git a/README.md b/README.md index 033f4476..b5e3b4ea 100644 --- a/README.md +++ b/README.md @@ -127,37 +127,23 @@ fit #> #> Summary for theta: #> -#> Mean SD SE -#> sd_y 20.8618647 1.9369381 0.068145131 -#> sd_level 6.3731836 2.8013937 0.113153715 -#> sd_slope 0.3388712 0.2833955 0.010355574 -#> Wind -2.5183269 0.5764833 0.020978488 -#> Temp 1.0265846 0.2064343 0.007497538 -#> -#> Effective sample sizes for theta: -#> -#> ESS -#> sd_y 807.9079 -#> sd_level 612.9297 -#> sd_slope 748.9238 -#> Wind 755.1359 -#> Temp 758.1001 +#> Mean SE SD 2.5% 97.5% ESS +#> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 +#> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 +#> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 +#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 +#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 #> #> Summary for alpha_154: #> -#> Mean SD SE -#> level -28.3163054 20.132341 0.69650977 -#> slope -0.3740463 1.685733 0.03683278 -#> -#> Effective sample sizes for alpha_154: -#> -#> ESS -#> level 835.4763 -#> slope 2094.6363 +#> Mean SE SD 2.5% 97.5% ESS +#> level[154] -28.3163054 0.69650977 20.132341 -69.271049 11.797133 835 +#> slope[154] -0.3740463 0.03683278 1.685733 -4.065499 2.830134 2094 #> #> Run time: #> user system elapsed -#> 0.89 0.02 0.91 +#> 0.88 0.02 0.89 + obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -200,37 +186,31 @@ fit2 #> #> Summary for theta: #> -#> Mean SD SE SE-IS -#> sd_level 0.057158663 0.035366227 0.0019652211 7.416778e-04 -#> sd_slope 0.003894013 0.003654978 0.0001818441 7.567405e-05 -#> phi 4.006977632 0.536273508 0.0157319790 1.071056e-02 -#> Wind -0.057351094 0.015411504 0.0004389087 3.051982e-04 -#> Temp 0.052808820 0.008701489 0.0002478631 1.716846e-04 -#> -#> Effective sample sizes for theta: -#> -#> ESS -#> sd_level 323.8580 -#> sd_slope 403.9906 -#> phi 1161.9996 -#> Wind 1232.9392 -#> Temp 1232.4328 +#> Mean SE SD 2.5% 97.5% ESS +#> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 +#> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 +#> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 +#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 +#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 +#> SE_IS ESS_IS +#> sd_level 2.927386e-04 10591 +#> sd_slope 3.031489e-05 7766 +#> phi 4.411840e-03 14611 +#> Wind 1.263047e-04 13905 +#> Temp 7.128104e-05 14485 #> #> Summary for alpha_154: #> -#> Mean SD SE SE-IS -#> level -0.200656509 0.73134471 0.0205560413 0.0145184378 -#> slope -0.002689176 0.02289051 0.0005131012 0.0005050788 -#> -#> Effective sample sizes for alpha_154: -#> -#> ESS -#> level 1265.801 -#> slope 1990.238 +#> Mean SE SD 2.5% 97.5% ESS +#> level[154] -0.200656509 0.0201721601 0.73134471 -1.62501396 1.24522802 1314 +#> slope[154] -0.002689176 0.0005121944 0.02289051 -0.04650504 0.04724173 1997 +#> SE_IS ESS_IS +#> level[154] 0.005987284 9458 +#> slope[154] 0.000191620 6448 #> #> Run time: #> user system elapsed -#> 10.59 0.02 10.62 +#> 10.54 0.05 10.54 ``` Comparison: @@ -331,39 +311,23 @@ fit #> #> Summary for theta: #> -#> Mean SD SE -#> theta_1 -3.89121114 0.58715113 0.0233827004 -#> theta_2 0.98712126 0.18819758 0.0051506907 -#> theta_3 0.06324657 0.02417334 0.0004672314 -#> theta_4 0.82577262 0.67134723 0.0165661049 -#> theta_5 4.75567622 0.05858454 0.0010887250 -#> theta_6 3.05462451 0.07640392 0.0014803971 -#> -#> Effective sample sizes for theta: -#> -#> ESS -#> theta_1 630.5368 -#> theta_2 1335.0487 -#> theta_3 2676.7595 -#> theta_4 1642.3041 -#> theta_5 2895.5411 -#> theta_6 2663.6361 +#> Mean SE SD 2.5% 97.5% ESS +#> theta_1 -3.89121114 0.0233827004 0.58715113 -5.0085134 -2.6915137 631 +#> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 +#> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 +#> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 +#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 +#> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 #> #> Summary for alpha_154: #> -#> Mean SD SE -#> alpha -16.44435 14.99708 0.3659912 -#> mu 223.60490 116.49063 1.3409568 -#> -#> Effective sample sizes for alpha_154: -#> -#> ESS -#> alpha 1679.082 -#> mu 7546.619 +#> Mean SE SD 2.5% 97.5% ESS +#> alpha[154] -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 +#> mu[154] 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 #> #> Run time: #> user system elapsed -#> 11.94 0.06 11.94 +#> 11.97 0.11 12.04 ``` Draw predictions: @@ -387,6 +351,7 @@ pred %>% filter(Variable == "Solar") %>% ``` r + obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd index 6b1616e9..7ebd3f12 100644 --- a/man/asymptotic_var.Rd +++ b/man/asymptotic_var.Rd @@ -18,9 +18,9 @@ other option \code{"geyer"}.} \description{ The asymptotic variance MCMCSE^2 is based on Corollary 1 of Vihola et al. (2020) from weighted samples from IS-MCMC. The default -method is based on the integrated autocorrelation time (IACT) by Sokal (1997) -which seem to work well for reasonable problems, but it is also possible to -use the Geyer's method as implemented in \code{ess_mean} of the +method is based on the integrated autocorrelation time (IACT) by Sokal +(1997) which seem to work well for reasonable problems, but it is also +possible to use the Geyer's method as implemented in \code{ess_mean} of the \code{posterior} package. } \examples{ diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index a8f18d05..212825bb 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -96,7 +96,8 @@ model <- bsm_ng(poisson_series, sd_level = uniform(0.115, 0, 2 * s), out <- run_mcmc(model, iter = 1e5, particles = 10) summary(out, variable = "theta", return_se = TRUE) # should be about 0.093 and 0.016 -summary(out, variable = "states", return_se = TRUE)$Mean[c(1,100),1] +summary(out, variable = "states", return_se = TRUE, + states = 1, times = c(1, 100))$Mean # should be about -0.075, 2.618 } diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 8a3bd14e..776b1e9c 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -123,7 +123,6 @@ https://doi.org/10.1111/sjos.12492 Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). The unscented particle filter. In Advances in neural information processing systems, p 584-590. -https://proceedings.neurips.cc/paper/2000/file/f5c3dd7514bf620a1b85450d2ae374b1-Paper.pdf Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press. diff --git a/man/post_correct.Rd b/man/post_correct.Rd index 4f8860fa..b20f6342 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -120,12 +120,12 @@ ggplot(aes(time, mean, colour = method)) + } } \references{ -A. Doucet, M. K. Pitt, G. Deligiannidis, R. Kohn (2018). +Doucet A, Pitt M K, Deligiannidis G, Kohn R (2018). Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator. Biometrika, 102, 2, 295-313, https://doi.org/10.1093/biomet/asu075 -Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based -on approximate marginal Markov chain Monte Carlo. +Vihola M, Helske J, Franks J (2020). Importance sampling type estimators +based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 } diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 8e404193..3a5d8f22 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -22,6 +22,7 @@ run_mcmc(model, ...) end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), + verbose = TRUE, ... ) @@ -43,6 +44,7 @@ run_mcmc(model, ...) seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-08, + verbose = TRUE, ... ) @@ -64,6 +66,7 @@ run_mcmc(model, ...) max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, + verbose = TRUE, ... ) @@ -83,6 +86,7 @@ run_mcmc(model, ...) end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), + verbose = TRUE, ... ) } @@ -181,7 +185,9 @@ with \code{iekf_iter} iterations. Used only for models of class \item{L_c, L_f}{For \code{ssm_sde} models, Positive integer values defining the discretization levels for first and second stages (defined as 2^L). -For pseudo-marginal methods (\code{"pm"}), maximum of these is used.} +For pseudo-marginal methods (\code{"pm"}), maximum of these is used. +@param verbose If \code{TRUE} (default), prints a progress bar to the +console. Set to \code{FALSE} if number of iterations is less than 50.} } \description{ Adaptive Markov chain Monte Carlo simulation for SSMs using diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index a9cdd96d..d165205d 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -28,11 +28,11 @@ ssm_nlg( \item{y}{Observations as multivariate time series (or matrix) of length \eqn{n}.} -\item{Z, H, T, R}{An external pointers (object of class \code{externalptr}) for the -C++ functions which define the corresponding model functions.} +\item{Z, H, T, R}{An external pointers (object of class \code{externalptr}) +for the C++ functions which define the corresponding model functions.} -\item{Z_gn, T_gn}{An external pointers (object of class \code{externalptr}) for -the C++ functions which define the gradients of the corresponding model +\item{Z_gn, T_gn}{An external pointers (object of class \code{externalptr}) +for the C++ functions which define the gradients of the corresponding model functions.} \item{a1}{Prior mean for the initial state as object of class diff --git a/src/R_mcmc.cpp b/src/R_mcmc.cpp index 41b408dd..5919c01c 100644 --- a/src/R_mcmc.cpp +++ b/src/R_mcmc.cpp @@ -15,7 +15,7 @@ Rcpp::List gaussian_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, - const unsigned int n_threads, const int model_type) { + const unsigned int n_threads, const int model_type, const bool verbose) { arma::vec a1 = Rcpp::as(model_["a1"]); unsigned int m = a1.n_elem; @@ -29,7 +29,7 @@ Rcpp::List gaussian_mcmc(const Rcpp::List model_, n = y.n_rows; } mcmc mcmc_run(iter, burnin, thin, n, m, - target_acceptance, gamma, S, output_type); + target_acceptance, gamma, S, output_type, verbose); switch (model_type) { case 0: { @@ -171,7 +171,8 @@ Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, - const unsigned int sampling_method, const unsigned int model_type) { + const unsigned int sampling_method, const unsigned int model_type, + const bool verbose) { arma::vec a1 = Rcpp::as(model_["a1"]); unsigned int m = a1.n_elem; @@ -186,7 +187,7 @@ Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, } mcmc mcmc_run(iter, burnin, thin, n, m, - target_acceptance, gamma, S, output_type); + target_acceptance, gamma, S, output_type, verbose); switch (model_type) { case 0: { @@ -251,7 +252,8 @@ Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, - const unsigned int sampling_method, const int model_type) { + const unsigned int sampling_method, const int model_type, + const bool verbose) { arma::vec a1 = Rcpp::as(model_["a1"]); unsigned int m = a1.n_elem; @@ -264,7 +266,8 @@ Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, arma::mat y = Rcpp::as(model_["y"]); n = y.n_rows; } - mcmc mcmc_run(iter, burnin, thin, n, m, target_acceptance, gamma, S, output_type); + mcmc mcmc_run(iter, burnin, thin, n, m, target_acceptance, gamma, S, + output_type, verbose); switch (model_type) { case 0: { @@ -332,7 +335,7 @@ Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int is_type, - const int model_type, const bool approx) { + const int model_type, const bool approx, const bool verbose) { arma::vec a1 = Rcpp::as(model_["a1"]); unsigned int m = a1.n_elem; @@ -348,7 +351,7 @@ Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, p = y.n_cols; } approx_mcmc mcmc_run(iter, burnin, thin, n, m, p, - target_acceptance, gamma, S, output_type, true); + target_acceptance, gamma, S, output_type, true, verbose); if (nsim <= 1) { mcmc_run.alpha_storage.zeros(); @@ -374,7 +377,6 @@ Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, if(is_type == 3) { mcmc_run.expand(); } - switch (sampling_method) { case 1: mcmc_run.is_correction_psi(model, nsim, is_type, n_threads, model_["update_fn"]); @@ -557,7 +559,7 @@ Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, - const unsigned int output_type) { + const unsigned int output_type, const bool verbose) { Rcpp::XPtr xpfun_Z(Z); @@ -575,7 +577,7 @@ Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, time_varying, seed); mcmc mcmc_run(iter, burnin, thin, model.n, - model.m, target_acceptance, gamma, S, output_type); + model.m, target_acceptance, gamma, S, output_type, verbose); mcmc_run.pm_mcmc(model, sampling_method, nsim, end_ram); switch (output_type) { @@ -617,7 +619,7 @@ Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, - const unsigned int output_type) { + const unsigned int output_type, const bool verbose) { Rcpp::XPtr xpfun_Z(Z); @@ -635,7 +637,7 @@ Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, time_varying, seed); mcmc mcmc_run(iter, burnin, thin, model.n, - model.m, target_acceptance, gamma, S, output_type); + model.m, target_acceptance, gamma, S, output_type, verbose); mcmc_run.da_mcmc(model, sampling_method, nsim, end_ram); switch (output_type) { @@ -676,7 +678,8 @@ Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, - const unsigned int iekf_iter, const unsigned int output_type) { + const unsigned int iekf_iter, const unsigned int output_type, + const bool verbose) { Rcpp::XPtr xpfun_Z(Z); @@ -694,7 +697,7 @@ Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, time_varying, seed); approx_mcmc mcmc_run(iter, burnin, thin, model.n, - model.m, model.m, target_acceptance, gamma, S, output_type, false); + model.m, model.m, target_acceptance, gamma, S, output_type, false, verbose); mcmc_run.ekf_mcmc(model, end_ram); @@ -742,7 +745,7 @@ Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, const unsigned int sampling_method, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int output_type, - const bool approx) { + const bool approx, const bool verbose) { Rcpp::XPtr xpfun_Z(Z); Rcpp::XPtr xpfun_H(H); @@ -759,7 +762,7 @@ Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, time_varying, seed, iekf_iter, max_iter, conv_tol); approx_mcmc mcmc_run(iter, burnin, thin, model.n, - model.m, model.m, target_acceptance, gamma, S, output_type); + model.m, model.m, target_acceptance, gamma, S, output_type, true, verbose); mcmc_run.amcmc(model, sampling_method, end_ram); if(approx) { diff --git a/src/R_sde.cpp b/src/R_sde.cpp index 7e8ba04d..9d5a5474 100644 --- a/src/R_sde.cpp +++ b/src/R_sde.cpp @@ -119,7 +119,7 @@ Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, - const bool end_ram, const unsigned int type) { + const bool end_ram, const unsigned int type, const bool verbose) { Rcpp::XPtr xpfun_drift(drift_pntr); Rcpp::XPtr xpfun_diffusion(diffusion_pntr); @@ -132,7 +132,7 @@ Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, L, L, seed); mcmc mcmc_run(iter, burnin, - thin, model.n, 1, target_acceptance, gamma, S, type); + thin, model.n, 1, target_acceptance, gamma, S, type, verbose); mcmc_run.pm_mcmc(model, nsim, end_ram); @@ -173,7 +173,7 @@ Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, - const bool end_ram, const unsigned int type) { + const bool end_ram, const unsigned int type, const bool verbose) { Rcpp::XPtr xpfun_drift(drift_pntr); Rcpp::XPtr xpfun_diffusion(diffusion_pntr); @@ -186,7 +186,7 @@ Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, L_f, L_c, seed); mcmc mcmc_run(iter, burnin, - thin, model.n, 1, target_acceptance, gamma, S, type); + thin, model.n, 1, target_acceptance, gamma, S, type, verbose); mcmc_run.da_mcmc(model, nsim, end_ram); @@ -227,7 +227,7 @@ Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int is_type, const unsigned int n_threads, - const unsigned int type) { + const unsigned int type, const bool verbose) { Rcpp::XPtr xpfun_drift(drift_pntr); Rcpp::XPtr xpfun_diffusion(diffusion_pntr); @@ -240,7 +240,7 @@ Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, L_f, L_c, seed); approx_mcmc mcmc_run(iter, burnin, thin, model.n, 1, 1, - target_acceptance, gamma, S, type, false); + target_acceptance, gamma, S, type, false, verbose); mcmc_run.amcmc(model, nsim, end_ram); diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 10be8262..e51693b4 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -362,8 +362,8 @@ BEGIN_RCPP END_RCPP } // gaussian_mcmc -Rcpp::List gaussian_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const int model_type); -RcppExport SEXP _bssm_gaussian_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP model_typeSEXP) { +Rcpp::List gaussian_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const int model_type, const bool verbose); +RcppExport SEXP _bssm_gaussian_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP model_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -379,13 +379,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(gaussian_mcmc(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(gaussian_mcmc(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type, verbose)); return rcpp_result_gen; END_RCPP } // nongaussian_pm_mcmc -Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int model_type); -RcppExport SEXP _bssm_nongaussian_pm_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { +Rcpp::List nongaussian_pm_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int model_type, const bool verbose); +RcppExport SEXP _bssm_nongaussian_pm_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -403,13 +404,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); Rcpp::traits::input_parameter< const unsigned int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_pm_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_pm_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose)); return rcpp_result_gen; END_RCPP } // nongaussian_da_mcmc -Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const int model_type); -RcppExport SEXP _bssm_nongaussian_da_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP) { +Rcpp::List nongaussian_da_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const int model_type, const bool verbose); +RcppExport SEXP _bssm_nongaussian_da_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP model_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -427,13 +429,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_da_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_da_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type, verbose)); return rcpp_result_gen; END_RCPP } // nongaussian_is_mcmc -Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int is_type, const int model_type, const bool approx); -RcppExport SEXP _bssm_nongaussian_is_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP is_typeSEXP, SEXP model_typeSEXP, SEXP approxSEXP) { +Rcpp::List nongaussian_is_mcmc(const Rcpp::List model_, const unsigned int output_type, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const unsigned int seed, const bool end_ram, const unsigned int n_threads, const unsigned int sampling_method, const unsigned int is_type, const int model_type, const bool approx, const bool verbose); +RcppExport SEXP _bssm_nongaussian_is_mcmc(SEXP model_SEXP, SEXP output_typeSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP seedSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP sampling_methodSEXP, SEXP is_typeSEXP, SEXP model_typeSEXP, SEXP approxSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -453,13 +456,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); Rcpp::traits::input_parameter< const int >::type model_type(model_typeSEXP); Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); - rcpp_result_gen = Rcpp::wrap(nongaussian_is_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nongaussian_is_mcmc(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx, verbose)); return rcpp_result_gen; END_RCPP } // nonlinear_pm_mcmc -Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_pm_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +Rcpp::List nonlinear_pm_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type, const bool verbose); +RcppExport SEXP _bssm_nonlinear_pm_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -494,13 +498,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_pm_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_pm_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose)); return rcpp_result_gen; END_RCPP } // nonlinear_da_mcmc -Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_da_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +Rcpp::List nonlinear_da_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int max_iter, const double conv_tol, const unsigned int sampling_method, const unsigned int iekf_iter, const unsigned int output_type, const bool verbose); +RcppExport SEXP _bssm_nonlinear_da_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP sampling_methodSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -535,13 +540,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type sampling_method(sampling_methodSEXP); Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_da_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_da_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type, verbose)); return rcpp_result_gen; END_RCPP } // nonlinear_ekf_mcmc -Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int iekf_iter, const unsigned int output_type); -RcppExport SEXP _bssm_nonlinear_ekf_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP) { +Rcpp::List nonlinear_ekf_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int iekf_iter, const unsigned int output_type, const bool verbose); +RcppExport SEXP _bssm_nonlinear_ekf_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -572,13 +578,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_ekf_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_ekf_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type, verbose)); return rcpp_result_gen; END_RCPP } // nonlinear_is_mcmc -Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int is_type, const unsigned int sampling_method, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int output_type, const bool approx); -RcppExport SEXP _bssm_nonlinear_is_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP sampling_methodSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP approxSEXP) { +Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, SEXP T, SEXP R, SEXP Zg, SEXP Tg, SEXP a1, SEXP P1, const arma::vec& theta, SEXP log_prior_pdf, const arma::vec& known_params, const arma::mat& known_tv_params, const arma::uvec& time_varying, const unsigned int n_states, const unsigned int n_etas, const unsigned int seed, const unsigned int nsim, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int n_threads, const unsigned int is_type, const unsigned int sampling_method, const unsigned int max_iter, const double conv_tol, const unsigned int iekf_iter, const unsigned int output_type, const bool approx, const bool verbose); +RcppExport SEXP _bssm_nonlinear_is_mcmc(SEXP ySEXP, SEXP ZSEXP, SEXP HSEXP, SEXP TSEXP, SEXP RSEXP, SEXP ZgSEXP, SEXP TgSEXP, SEXP a1SEXP, SEXP P1SEXP, SEXP thetaSEXP, SEXP log_prior_pdfSEXP, SEXP known_paramsSEXP, SEXP known_tv_paramsSEXP, SEXP time_varyingSEXP, SEXP n_statesSEXP, SEXP n_etasSEXP, SEXP seedSEXP, SEXP nsimSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP n_threadsSEXP, SEXP is_typeSEXP, SEXP sampling_methodSEXP, SEXP max_iterSEXP, SEXP conv_tolSEXP, SEXP iekf_iterSEXP, SEXP output_typeSEXP, SEXP approxSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -615,7 +622,8 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type iekf_iter(iekf_iterSEXP); Rcpp::traits::input_parameter< const unsigned int >::type output_type(output_typeSEXP); Rcpp::traits::input_parameter< const bool >::type approx(approxSEXP); - rcpp_result_gen = Rcpp::wrap(nonlinear_is_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(nonlinear_is_mcmc(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx, verbose)); return rcpp_result_gen; END_RCPP } @@ -986,8 +994,8 @@ BEGIN_RCPP END_RCPP } // sde_pm_mcmc -Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); -RcppExport SEXP _bssm_sde_pm_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { +Rcpp::List sde_pm_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type, const bool verbose); +RcppExport SEXP _bssm_sde_pm_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP LSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -1011,13 +1019,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_pm_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(sde_pm_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose)); return rcpp_result_gen; END_RCPP } // sde_da_mcmc -Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type); -RcppExport SEXP _bssm_sde_da_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP) { +Rcpp::List sde_da_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int type, const bool verbose); +RcppExport SEXP _bssm_sde_da_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -1042,13 +1051,14 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const arma::mat >::type S(SSEXP); Rcpp::traits::input_parameter< const bool >::type end_ram(end_ramSEXP); Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_da_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(sde_da_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type, verbose)); return rcpp_result_gen; END_RCPP } // sde_is_mcmc -Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int is_type, const unsigned int n_threads, const unsigned int type); -RcppExport SEXP _bssm_sde_is_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP is_typeSEXP, SEXP n_threadsSEXP, SEXP typeSEXP) { +Rcpp::List sde_is_mcmc(const arma::vec& y, const double x0, const bool positive, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, SEXP log_prior_pdf_pntr, SEXP log_obs_density_pntr, const arma::vec& theta, const unsigned int nsim, const unsigned int L_c, const unsigned int L_f, const unsigned int seed, const unsigned int iter, const unsigned int burnin, const unsigned int thin, const double gamma, const double target_acceptance, const arma::mat S, const bool end_ram, const unsigned int is_type, const unsigned int n_threads, const unsigned int type, const bool verbose); +RcppExport SEXP _bssm_sde_is_mcmc(SEXP ySEXP, SEXP x0SEXP, SEXP positiveSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP log_prior_pdf_pntrSEXP, SEXP log_obs_density_pntrSEXP, SEXP thetaSEXP, SEXP nsimSEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP seedSEXP, SEXP iterSEXP, SEXP burninSEXP, SEXP thinSEXP, SEXP gammaSEXP, SEXP target_acceptanceSEXP, SEXP SSEXP, SEXP end_ramSEXP, SEXP is_typeSEXP, SEXP n_threadsSEXP, SEXP typeSEXP, SEXP verboseSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -1075,7 +1085,8 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const unsigned int >::type is_type(is_typeSEXP); Rcpp::traits::input_parameter< const unsigned int >::type n_threads(n_threadsSEXP); Rcpp::traits::input_parameter< const unsigned int >::type type(typeSEXP); - rcpp_result_gen = Rcpp::wrap(sde_is_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type)); + Rcpp::traits::input_parameter< const bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(sde_is_mcmc(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type, verbose)); return rcpp_result_gen; END_RCPP } @@ -1279,14 +1290,14 @@ static const R_CallMethodDef CallEntries[] = { {"_bssm_gaussian_loglik", (DL_FUNC) &_bssm_gaussian_loglik, 2}, {"_bssm_nongaussian_loglik", (DL_FUNC) &_bssm_nongaussian_loglik, 5}, {"_bssm_nonlinear_loglik", (DL_FUNC) &_bssm_nonlinear_loglik, 22}, - {"_bssm_gaussian_mcmc", (DL_FUNC) &_bssm_gaussian_mcmc, 12}, - {"_bssm_nongaussian_pm_mcmc", (DL_FUNC) &_bssm_nongaussian_pm_mcmc, 14}, - {"_bssm_nongaussian_da_mcmc", (DL_FUNC) &_bssm_nongaussian_da_mcmc, 14}, - {"_bssm_nongaussian_is_mcmc", (DL_FUNC) &_bssm_nongaussian_is_mcmc, 16}, - {"_bssm_nonlinear_pm_mcmc", (DL_FUNC) &_bssm_nonlinear_pm_mcmc, 31}, - {"_bssm_nonlinear_da_mcmc", (DL_FUNC) &_bssm_nonlinear_da_mcmc, 31}, - {"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 27}, - {"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 33}, + {"_bssm_gaussian_mcmc", (DL_FUNC) &_bssm_gaussian_mcmc, 13}, + {"_bssm_nongaussian_pm_mcmc", (DL_FUNC) &_bssm_nongaussian_pm_mcmc, 15}, + {"_bssm_nongaussian_da_mcmc", (DL_FUNC) &_bssm_nongaussian_da_mcmc, 15}, + {"_bssm_nongaussian_is_mcmc", (DL_FUNC) &_bssm_nongaussian_is_mcmc, 17}, + {"_bssm_nonlinear_pm_mcmc", (DL_FUNC) &_bssm_nonlinear_pm_mcmc, 32}, + {"_bssm_nonlinear_da_mcmc", (DL_FUNC) &_bssm_nonlinear_da_mcmc, 32}, + {"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 28}, + {"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 34}, {"_bssm_R_milstein", (DL_FUNC) &_bssm_R_milstein, 9}, {"_bssm_suggest_n_nongaussian", (DL_FUNC) &_bssm_suggest_n_nongaussian, 6}, {"_bssm_suggest_n_nonlinear", (DL_FUNC) &_bssm_suggest_n_nonlinear, 20}, @@ -1304,9 +1315,9 @@ static const R_CallMethodDef CallEntries[] = { {"_bssm_loglik_sde", (DL_FUNC) &_bssm_loglik_sde, 12}, {"_bssm_bsf_sde", (DL_FUNC) &_bssm_bsf_sde, 12}, {"_bssm_bsf_smoother_sde", (DL_FUNC) &_bssm_bsf_smoother_sde, 12}, - {"_bssm_sde_pm_mcmc", (DL_FUNC) &_bssm_sde_pm_mcmc, 20}, - {"_bssm_sde_da_mcmc", (DL_FUNC) &_bssm_sde_da_mcmc, 21}, - {"_bssm_sde_is_mcmc", (DL_FUNC) &_bssm_sde_is_mcmc, 23}, + {"_bssm_sde_pm_mcmc", (DL_FUNC) &_bssm_sde_pm_mcmc, 21}, + {"_bssm_sde_da_mcmc", (DL_FUNC) &_bssm_sde_da_mcmc, 22}, + {"_bssm_sde_is_mcmc", (DL_FUNC) &_bssm_sde_is_mcmc, 24}, {"_bssm_sde_state_sampler_bsf_is2", (DL_FUNC) &_bssm_sde_state_sampler_bsf_is2, 13}, {"_bssm_gaussian_smoother", (DL_FUNC) &_bssm_gaussian_smoother, 2}, {"_bssm_gaussian_ccov_smoother", (DL_FUNC) &_bssm_gaussian_ccov_smoother, 2}, diff --git a/src/approx_mcmc.cpp b/src/approx_mcmc.cpp index a0240677..3400891f 100644 --- a/src/approx_mcmc.cpp +++ b/src/approx_mcmc.cpp @@ -22,9 +22,9 @@ approx_mcmc::approx_mcmc(const unsigned int iter, const unsigned int burnin, const unsigned int thin, const unsigned int n, const unsigned int m, const unsigned int k, const double target_acceptance, const double gamma, const arma::mat& S, const unsigned int output_type, - const bool store_modes) : + const bool store_modes, const bool verbose) : mcmc(iter, burnin, thin, n, m, - target_acceptance, gamma, S, output_type), + target_acceptance, gamma, S, output_type, verbose), weight_storage(arma::vec(n_samples, arma::fill::zeros)), mode_storage(arma::cube(k, n, n_samples * store_modes)), approx_loglik_storage(arma::vec(n_samples)), @@ -141,12 +141,17 @@ void approx_mcmc::amcmc(T model, const unsigned int method, const bool end_ram, unsigned int n_values = 0; double acceptance_prob = 0.0; + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -196,10 +201,20 @@ void approx_mcmc::amcmc(T model, const unsigned int method, const bool end_ram, } } - if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } trim_storage(); @@ -234,10 +249,16 @@ void approx_mcmc::amcmc(ssm_sde model, const unsigned int nsim, const bool end_r std::uniform_real_distribution<> unif(0.0, 1.0); arma::vec theta = model.theta; + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 4 == 0) { - Rcpp::checkUserInterrupt(); - } // sample from standard normal distribution arma::vec u(n_par); @@ -291,6 +312,17 @@ void approx_mcmc::amcmc(ssm_sde model, const unsigned int nsim, const bool end_r if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } trim_storage(); @@ -318,6 +350,9 @@ template void approx_mcmc::is_correction_psi(T model, const unsigned int nsim, const unsigned int is_type, const unsigned int n_threads, const Rcpp::Function update_fn) { + if (verbose) { + Rcpp::Rcout<<"\nStarting IS-correction phase with "< void mcmc::state_posterior(ssm_mlg model, const unsigned int n_threads, const Rcpp::Function update_fn) { - - + + #ifdef _OPENMP parset_mlg pars(model, theta_storage, update_fn); #pragma omp parallel num_threads(n_threads) default(shared) firstprivate(model) @@ -210,12 +211,18 @@ void mcmc::mcmc_gaussian(T model, const bool end_ram, bool new_value = true; unsigned int n_values = 0; + + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -269,8 +276,19 @@ void mcmc::mcmc_gaussian(T model, const bool end_ram, if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } - + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC run. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); @@ -360,12 +378,18 @@ void mcmc::pm_mcmc( unsigned int n_values = 0; std::normal_distribution<> normal(0.0, 1.0); std::uniform_real_distribution<> unif(0.0, 1.0); + + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -442,6 +466,17 @@ void mcmc::pm_mcmc( if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] @@ -534,12 +569,18 @@ void mcmc::da_mcmc(T model, unsigned int n_values = 0; std::normal_distribution<> normal(0.0, 1.0); std::uniform_real_distribution<> unif(0.0, 1.0); + + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -618,6 +659,17 @@ void mcmc::da_mcmc(T model, if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] @@ -669,12 +721,18 @@ void mcmc::pm_mcmc( unsigned int n_values = 0; std::normal_distribution<> normal(0.0, 1.0); std::uniform_real_distribution<> unif(0.0, 1.0); + + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -749,6 +807,17 @@ void mcmc::pm_mcmc( if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } + } } if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] @@ -781,7 +850,7 @@ void mcmc::da_mcmc(ssm_sde model, double ll_c = model.bsf_filter(nsim, model.L_c, alpha, weights, indices); double ll_f = model.bsf_filter(nsim, model.L_f, alpha, weights, indices); - + if (!std::isfinite(ll_f)) Rcpp::stop("Initial log-likelihood is not finite."); @@ -801,12 +870,18 @@ void mcmc::da_mcmc(ssm_sde model, unsigned int n_values = 0; std::normal_distribution<> normal(0.0, 1.0); std::uniform_real_distribution<> unif(0.0, 1.0); + + // don't update progress at each iteration + unsigned int mod = std::max(1U, iter / 50); + unsigned int ticks = 1; + if (verbose) { + Rcpp::Rcout<<"Starting MCMC. Progress:\n"; + Rcpp::Rcout<<"0% 10 20 30 40 50 60 70 80 90 100%\n"; + Rcpp::Rcout<<"|"; + } + for (unsigned int i = 1; i <= iter; i++) { - if (i % 16 == 0) { - Rcpp::checkUserInterrupt(); - } - // sample from standard normal distribution arma::vec u(n_par); for(unsigned int j = 0; j < n_par; j++) { @@ -884,6 +959,17 @@ void mcmc::da_mcmc(ssm_sde model, if (!end_ram || i <= burnin) { ramcmc::adapt_S(S, u, acceptance_prob, target_acceptance, i, gamma); + } + if (i % mod == 0) { + Rcpp::checkUserInterrupt(); + if (verbose) { + if (ticks % 5 == 0) { + Rcpp::Rcout<<"|"; + } else { + Rcpp::Rcout<<"-"; + } + ticks++; + } } } if (output_type == 2) { diff --git a/src/mcmc.h b/src/mcmc.h index bc43e53f..eb585e0f 100644 --- a/src/mcmc.h +++ b/src/mcmc.h @@ -31,7 +31,7 @@ class mcmc { mcmc(const unsigned int iter, const unsigned int burnin, const unsigned int thin, const unsigned int n, const unsigned int m, const double target_acceptance, const double gamma, const arma::mat& S, - const unsigned int output_type = 1); + const unsigned int output_type = 1, const bool verbose = true); // sample states given theta template @@ -75,6 +75,7 @@ class mcmc { arma::mat S; double acceptance_rate; unsigned int output_type; + bool verbose; }; diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 13466bbc..be4bd405 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -1,8 +1,7 @@ context("Test MCMC") -#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5} Replicate Helske & Vihola (2021) -#' @srrstats {BS7.0, BS7.1, BS7.7} -#' @srrstats {BS7.3} +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, BS7.0, BS7.1, BS7.2, BS7.7} +#' Replicate Helske & Vihola (2021). tol <- 1e-8 @@ -64,7 +63,8 @@ test_that("MCMC results from bssm paper are still correct", { }) -test_that("scaling is linear", { +#' @srrstats {BS7.3} +test_that("scaling is linear with respect to the length of the time series", { skip_on_cran() set.seed(1) @@ -110,13 +110,33 @@ test_that("run_mcmc throws error with improper arguments", { sd_level = uniform(1, 0, 10)) expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, - end_adaptive_phase = 4)) + end_adaptive_phase = 4), + "Argument 'end_adaptive_phase' should be TRUE or FALSE.") out <- run_mcmc(model_bssm, iter = 10, output_type = "theta") - expect_error(summary(out, return_se = 2)) - expect_error(summary(out, variable = "both")) + expect_error(summary(out, return_se = 2), + "Argument 'return_se' should be TRUE or FALSE.") + expect_error(summary(out, variable = "both"), + "Cannot return summary of states as the MCMC type was not 'full'.") + + model_bssm$theta[] <- Inf + expect_error(run_mcmc(model, iter = 1e4, particles = 10), + "Initial prior probability is not finite.") }) + +#' @srrstats {BS2.13} +test_that("MCMC messages can be suppressed", { + model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, + sd_y = uniform(1, 0, 10), + sd_level = uniform(1, 0, 10)) + + expect_equal(capture_output(run_mcmc(run_mcmc(model_bssm, iter = 100, + verbose = FALSE))), "") + expect_equal(capture_output(run_mcmc(run_mcmc(model_bssm, iter = 10, + verbose = FALSE))), "") +}) + test_that("MCMC results for Gaussian model are correct", { set.seed(123) model_bssm <- bsm_lg(rnorm(10, 3), P1 = diag(2, 2), sd_slope = 0, @@ -124,7 +144,7 @@ test_that("MCMC results for Gaussian model are correct", { sd_level = uniform(1, 0, 10)) expect_error(mcmc_bsm <- run_mcmc(model_bssm, iter = 50, seed = 1), NA) - + expect_equal( run_mcmc(model_bssm, iter = 100, seed = 1)[-14], run_mcmc(model_bssm, iter = 100, seed = 1)[-14]) @@ -282,7 +302,8 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) - expect_lt(sum(abs(sumr[1, c(1, 3)] - c(0.25892090511681, 0.186796779799571))), 0.5) + expect_lt(sum(abs(sumr[1, c(1, 3)] - + c(0.25892090511681, 0.186796779799571))), 0.5) states <- expand_sample(mcmc_poisson, variable = "states") diff --git a/tests/testthat/test_models.R b/tests/testthat/test_models.R index a76eda5e..4a658903 100644 --- a/tests/testthat/test_models.R +++ b/tests/testthat/test_models.R @@ -93,8 +93,8 @@ test_that("multivariate non-gaussian model", { list(R = array(diag(exp(theta)), c(2, 2, 1))) } - expect_error(mng_model <- ssm_mng(y = data.frame(1:4,1:4), Z = diag(2), T = diag(2), - R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", + expect_error(mng_model <- ssm_mng(y = data.frame(1:4,1:4), Z = diag(2), + T = diag(2), R = 0.1 * diag(2), P1 = diag(2), distribution = "poisson", init_theta = log(c(0.1, 0.1)), prior_fn = pfun, update_fn = ufun)) expect_error(mng_model <- ssm_mng(y = y - 10, Z = diag(2), T = diag(2), diff --git a/tests/testthat/test_post_correct.R b/tests/testthat/test_post_correct.R index a332c491..84a9c45b 100644 --- a/tests/testthat/test_post_correct.R +++ b/tests/testthat/test_post_correct.R @@ -76,7 +76,8 @@ test_that("Test post correction for non-linear model", { p <- numeric(n) p[1] <- p1 for(i in 2:n) - p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / (K + p[i-1] * (exp(r[i-1] * dT) - 1)), R_2) + p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / + (K + p[i-1] * (exp(r[i-1] * dT) - 1)), R_2) # observations y <- p + rnorm(n, 0, H) y[2:15] <- NA @@ -84,7 +85,7 @@ test_that("Test post correction for non-linear model", { initial_theta <- c(log_H = 0, log_R1 = log(0.05), log_R2 = 0) - # dT, K, a1 and the prior variances of first and second state (logit r and and p) + # dT, K, a1 and the prior variances of 1st and 2nd state (logit r and and p) known_params <- c(dT = dT, K = K, a11 = -1, a12 = 50, P11 = 1, P12 = 100) expect_error(model <- ssm_nlg(y = y, a1=pntrs$a1, P1 = pntrs$P1, diff --git a/tests/testthat/test_priors.R b/tests/testthat/test_priors.R index 133f3229..b237e5d7 100644 --- a/tests/testthat/test_priors.R +++ b/tests/testthat/test_priors.R @@ -1,6 +1,8 @@ context("Test rest of warnings and errors") -#' @srrstats {G5.2, G5.2a, G5.2b} Test that rest of the warnings are triggered. +#' @srrstats {G5.2, G5.2a, G5.2b} Test the rest of the warnings that are not +#' already triggered otherwise. +#' test_that("priors give errors with wrong arguments", { expect_error(normal("a", 0, 1)) expect_error(uniform(1, 2, 0)) diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index 891e783e..a81e5855 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -1,6 +1,6 @@ context("Test SDE") -#' @srrstats {G5.0, G5.1, G5.4, G5.4a, G5.4b, G5.4c} GBM model and data as in -#' Vihola, Helske, Franks (2020) +#' @srrstats {G5.0, G5.1, G5.4, G5.4a, G5.4b, G5.4c, BS7.2} GBM model and data +#' as in Vihola, Helske, Franks (2020) test_that("MCMC for SDE works", { skip_on_cran() diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index 175cd074..85ee4155 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -28,8 +28,8 @@ knitr::opts_chunk$set(echo = TRUE) ```{r srr-tags, eval = FALSE, echo = FALSE} #' rOpenSci Statistical Software Standards addressed by the vignette #' -#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6, BS4.0, BS4.1} Here and in the cited -#' R Journal paper. +#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6, BS4.0, BS4.1} Here and in other +#' vignettes, in the R Journal paper and Vihola, Helske, Franks (2020). #' To our knowledge, the package is also the first R package to #' implement delayed acceptance pseudo-marginal MCMC for general state space #' models. From 4b0aa23f27aa288489fdf6ce228d6e73fce9291a Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 22:56:16 +0200 Subject: [PATCH 129/180] add missing verbose argument --- R/run_mcmc.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 7db418e6..1001f5dc 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -643,7 +643,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, pmatch(mcmc_type, paste0("is", 1:3)), sampling_method, max_iter, conv_tol, iekf_iter, - output_type, FALSE) + output_type, FALSE, verbose) }, "ekf" = { nonlinear_ekf_mcmc(t(model$y), model$Z, model$H, model$T, From 142a1dd8a914a28db2cef88ccf1a691a6c67c1c4 Mon Sep 17 00:00:00 2001 From: helske Date: Sat, 20 Nov 2021 23:01:18 +0200 Subject: [PATCH 130/180] typo in srrstats --- R/srr-stats-standards.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 6ddc0a5b..40f30fb4 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -32,7 +32,7 @@ NULL #' @srrstatsNA {BS2.10} Not applicable as only single-chain runs are supported #' (but several such runs can be combined with posterior package). #' @srrstatsNA {BS2.11} Starting values are not accepted in this form. -#' @srrstatsNA {BS1.4, BS1.5, BS4.3, BS4.4, BS4.5, BS4.6, BS4.7, BS5.4] No +#' @srrstatsNA {BS1.4, BS1.5, BS4.3, BS4.4, BS4.5, BS4.6, BS4.7, BS5.4} No #' support for automatic stopping at converge (converge checkers). #' @srrstatsNA {BS2.15} Errors are normal R errors so they can be caught? But #' not sure what is meant here. From b18c58d42702c5d81c96ef73635c3d63496c3c16 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 21 Nov 2021 22:19:38 +0200 Subject: [PATCH 131/180] fixed docs, tests, posterior import etc --- DESCRIPTION | 1 + NAMESPACE | 8 ++- NEWS | 2 +- R/as.data.frame.mcmc_output.R | 5 +- R/as_draws.R | 59 +++++++++++++------- R/asymptotic_var.R | 2 +- R/bssm-package.R | 6 +- R/check_arguments.R | 1 + R/check_diagnostics.R | 3 +- R/expand_sample.R | 14 +++-- R/fitted.R | 14 +++-- R/print_mcmc.R | 36 ++++++++---- R/priors.R | 6 +- R/run_mcmc.R | 45 ++++++++++----- R/srr-stats-standards.R | 5 +- inst/CITATION | 2 +- man/as.data.frame.mcmc_output.Rd | 7 ++- man/{as_draws.Rd => as_draws-mcmc_output.Rd} | 18 +++--- man/bssm_prior.Rd | 4 +- man/check.Rd | 2 + man/expand_sample.Rd | 15 +++-- man/negbin_series.Rd | 5 +- man/run_mcmc.Rd | 16 +++--- src/approx_mcmc.cpp | 6 ++ src/mcmc.cpp | 18 ++++-- tests/testthat/test_approx.R | 2 +- tests/testthat/test_mcmc.R | 10 ++-- tests/testthat/test_sde.R | 4 +- 28 files changed, 202 insertions(+), 114 deletions(-) rename man/{as_draws.Rd => as_draws-mcmc_output.Rd} (85%) diff --git a/DESCRIPTION b/DESCRIPTION index c2091b47..69551111 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -43,6 +43,7 @@ Imports: dplyr, posterior, Rcpp (>= 0.12.3), + rlang, tidyr LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) diff --git a/NAMESPACE b/NAMESPACE index 2773584c..c64f7f5d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,8 @@ # Generated by roxygen2: do not edit by hand S3method(as.data.frame,mcmc_output) +S3method(as_draws,mcmc_output) +S3method(as_draws_df,mcmc_output) S3method(bootstrap_filter,gaussian) S3method(bootstrap_filter,nongaussian) S3method(bootstrap_filter,ssm_nlg) @@ -22,8 +24,6 @@ S3method(particle_smoother,gaussian) S3method(particle_smoother,nongaussian) S3method(particle_smoother,ssm_nlg) S3method(particle_smoother,ssm_sde) -S3method(posterior::as_draws,mcmc_output) -S3method(posterior::as_draws_df,mcmc_output) S3method(predict,mcmc_output) S3method(print,mcmc_output) S3method(run_mcmc,gaussian) @@ -38,6 +38,8 @@ S3method(summary,mcmc_output) export(ar1_lg) export(ar1_ng) export(as_bssm) +export(as_draws) +export(as_draws_df) export(asymptotic_var) export(bootstrap_filter) export(bsm_lg) @@ -103,6 +105,8 @@ importFrom(posterior,as_draws_df) importFrom(posterior,default_convergence_measures) importFrom(posterior,ess_mean) importFrom(posterior,summarise_draws) +importFrom(rlang,.data) +importFrom(rlang,is_interactive) importFrom(stats,"tsp<-") importFrom(stats,as.ts) importFrom(stats,cov) diff --git a/NEWS b/NEWS index 51dd3e67..0481cc84 100644 --- a/NEWS +++ b/NEWS @@ -10,7 +10,7 @@ bssm 2.0.0 (Release date: -) * New function estimate_ess can be used to compute effective sample size from weighted MCMC. * Added compatibility with the posterior package by defining as_draws - method for converting run_mcmc output to draws object. + method for converting run_mcmc output to draws_df object. * New function check_diagnostics for quick glance of ESS and Rhat values. * Large number of new tests, and improved documentation with added examples. * Large number of internal tweaks so that the package complies with diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index cce0523c..addf83b9 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -20,6 +20,8 @@ #' variable to match the ts attribute of the input to define. If \code{FALSE}, #' time is based on the indexing starting from 1. #' @param ... Ignored. +#' @seealso \code{as_draws} which converts the output for +#' \code{as_draws} object. #' @export #' @examples #' data("poisson_series") @@ -36,7 +38,8 @@ #' head(as.data.frame(out, variable = "theta", expand = FALSE)) #' #' # IS-weighted version: -#' out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") +#' out_is <- run_mcmc(model, iter = 2000, particles = 10, +#' mcmc_type = "is2") #' head(as.data.frame(out_is, variable = "theta")) #' as.data.frame.mcmc_output <- function(x, diff --git a/R/as_draws.R b/R/as_draws.R index 9dfaad13..e1bcb21b 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -1,4 +1,4 @@ -#' Convert \code{run_mcmc} output to \code{draws_df} format +#' Convert \code{run_mcmc} Output to \code{draws_df} Format #' #' Converts MCMC output from \code{run_mcmc} call to a #' \code{draws_df} format of the \code{posterior} package. This enables the use @@ -18,10 +18,13 @@ #' Default is all. #' @param ... Ignored. #' @return A \code{draws_df} object. -#' @rdname as_draws #' @importFrom posterior as_draws as_draws_df #' @importFrom tidyr pivot_wider -#' @exportS3Method posterior::as_draws_df mcmc_output +#' @aliases as_draws as_draws_df +#' @export +#' @export as_draws_df +#' @rdname as_draws-mcmc_output +#' @method as_draws_df mcmc_output #' @examples #' #' model <- bsm_lg(Nile, @@ -30,7 +33,6 @@ #' a1 = 1000, P1 = 500^2) #' #' fit1 <- run_mcmc(model, iter = 2000) -#' library("posterior") #' draws <- as_draws(fit1) #' head(draws, 4) #' estimate_ess(draws$sd_y) @@ -38,33 +40,47 @@ #' #' # More chains: #' model$theta[] <- c(50, 150) # change initial value -#' fit2 <- run_mcmc(model, iter = 2000) +#' fit2 <- run_mcmc(model, iter = 2000, verbose = FALSE) #' model$theta[] <- c(150, 50) # change initial value -#' fit3 <- run_mcmc(model, iter = 2000) +#' fit3 <- run_mcmc(model, iter = 2000, verbose = FALSE) #' -#' draws <- posterior::bind_draws(as_draws(fit1), -#' as_draws(fit2), as_draws(fit3), along = "chain") #' # it is actually enough to transform first mcmc_output to draws object, #' # rest are transformed automatically inside bind_draws +#' draws <- posterior::bind_draws(as_draws(fit1), +#' as_draws(fit2), as_draws(fit3), along = "chain") +#' #' posterior::rhat(draws$sd_y) -#' posterior::summarise_draws(draws) -#' +#' as_draws_df.mcmc_output <- function(x, times, states, ...) { - d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) - if (missing(times)) times <- seq_len(nrow(x$alpha)) - if (missing(states)) states <- seq_len(ncol(x$alpha)) + + if (missing(times)) { + times <- seq_len(nrow(x$alpha)) + } else { + if (!test_integerish(times, lower = 1, upper = nrow(x$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'times' should contain indices between 1 and ", + nrow(x$alpha),".")) + } + if (missing(states)) { + states <- seq_len(ncol(x$alpha)) + } else { + if (!test_integerish(states, lower = 1, upper = ncol(x$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'states' should contain indices between 1 and ", + ncol(x$alpha),".")) + } d_states <- as.data.frame(x, variable = "states", expand = TRUE, times = times, states = states, use_times = FALSE) d <- cbind( tidyr::pivot_wider(d_theta, - values_from = value, - names_from = variable), + values_from = .data$value, + names_from = .data$variable), tidyr::pivot_wider(d_states, - values_from = value, - names_from = c(variable, time), + values_from = .data$value, + names_from = c(.data$variable, .data$time), names_glue = "{variable}[{time}]")[, -(1:2)]) names(d)[1] <- ".iteration" @@ -78,8 +94,11 @@ as_draws_df.mcmc_output <- function(x, times, states, ...) { } as_draws(d) -} -#' @exportS3Method posterior::as_draws mcmc_output -as_draws.mcmc_output <- function(x, times, ...) { +} +#' @export +#' @export as_draws +#' @rdname as_draws-mcmc_output +#' @method as_draws mcmc_output +as_draws.mcmc_output <- function(x, times, states, ...) { as_draws_df.mcmc_output(x, times, ...) } diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 3918bdf0..2c248d62 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -92,7 +92,7 @@ asymptotic_var <- function(x, w, method = "sokal") { switch(method, sokal = (var(z) * iact(z) / estimate_c^2) / length(z), # ESS(z) = n / IACT(z) - ess_basic = var(z) / posterior::ess_mean(z) / estimate_c^2) + ess_basic = var(z) / ess_mean(z) / estimate_c^2) } #' Effective Sample Size for IS-type Estimators diff --git a/R/bssm-package.R b/R/bssm-package.R index 00dd23b6..bad5ddbe 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -38,7 +38,6 @@ #' @name bssm #' @aliases bssm #' @importFrom Rcpp evalCpp -#' @importFrom coda mcmc #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm @@ -153,10 +152,7 @@ NULL #' distribution = "negative binomial") #' #' # run the MCMC, small number of iterations for CRAN -#' fit_bssm <- run_mcmc(bssm_model, iter = 6000, burnin = 1000, +#' fit_bssm <- run_mcmc(bssm_model, iter = 2000, burnin = 1000, #' particles = 10) #' fit_bssm -#' -#' plot.ts(ts(cbind(sd_level=draws$sd_level, draws$sd_slope), start = 1000), -#' xlab = "Iteration", main = "Traceplots") NULL diff --git a/R/check_arguments.R b/R/check_arguments.R index c30072da..5486262f 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -3,6 +3,7 @@ #' @importFrom checkmate test_atomic_vector test_count test_double test_flag #' test_integerish test_int #' +#' @param x Variable to be checked. #' @param name Name of the argument used in printing error messages. #' @param positive Logical, check for positiveness of \code{x}. #' @param max Maximum value of \code{x}. diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index ad881906..995dc6d5 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -76,8 +76,7 @@ check_diagnostics <- function(x) { "and ESS measures below.\n", sep="") } - sumr <- posterior::summarise_draws(draws, - posterior::default_convergence_measures()) + sumr <- summarise_draws(draws, default_convergence_measures()) min_ess <- which.min(sumr$ess_bulk) cat("\nSmallest bulk-ESS: ", round(sumr$ess_bulk[min_ess]), " (", sumr$variable[min_ess], ")", sep = "") diff --git a/R/expand_sample.R b/R/expand_sample.R index 41233adf..52247188 100644 --- a/R/expand_sample.R +++ b/R/expand_sample.R @@ -7,12 +7,15 @@ #' IS-corrected MCMC, sometimes we want to have the usual sample paths #' (for example for drawing traceplots). #' Function \code{expand_sample} returns the expanded sample based on the -#' counts. Note that for IS-corrected output the expanded -#' sample corresponds to the approximate posterior i.e., +#' counts (in form of \code{coda::mcmc} object. Note that for +#' the IS-MCMC the expanded sample corresponds to the approximate posterior i.e., #' the weights are ignored. #' -#' @seealso \code{as_draws}. +#' This functions is mostly for backwards compatibility, methods +#' \code{as.data.frame} and \code{as_draws} produce likely more convenient +#' output. #' +#' @importFrom coda mcmc #' @param x Output from \code{\link{run_mcmc}}. #' @param variable Expand parameters \code{"theta"} or states \code{"states"}. #' @param times Vector of indices. In case of states, @@ -22,9 +25,9 @@ #' @param by_states If \code{TRUE} (default), return list by states. #' Otherwise by time. #' @return An object of class \code{"mcmc"} of the \code{coda} package. +#' @seealso \code{as.data.frame.mcmc_output} and \code{as_draws.mcmc_output}. #' @export #' @examples -#' #' set.seed(1) #' n <- 50 #' x <- cumsum(rnorm(n)) @@ -35,7 +38,8 @@ #' # Traceplots for theta #' plot.ts(expand_sample(fit, variable = "theta")) #' # Traceplot for x_5 -#' plot.ts(expand_sample(fit, variable = "states", times = 5, states = 1)) +#' plot.ts(expand_sample(fit, variable = "states", times = 5, +#' states = 1)$level) expand_sample <- function(x, variable = "theta", times, states, by_states = TRUE) { diff --git a/R/fitted.R b/R/fitted.R index e508158e..98669f78 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -110,11 +110,13 @@ fitted.mcmc_output <- function(object, model, Variable = variables, Time = rep(time(model$y), each = nrow(pred))) - d %>% group_by(Variable, Time) %>% - summarise( - Mean = weighted_mean(value, w), - SD = sqrt(weighted_var(value, w)), - as_tibble(as.list(weighted_quantile(value, w, probs = probs))), - "SE(Mean)" = as.numeric(sqrt(asymptotic_var(value, w)))) %>% ungroup() + d %>% dplyr::group_by(.data$Variable, .data$Time) %>% + dplyr::summarise( + Mean = weighted_mean(.data$value, w), + SD = sqrt(weighted_var(.data$value, w)), + dplyr::as_tibble(as.list(weighted_quantile(.data$value, w, + probs = probs))), + "SE(Mean)" = as.numeric(sqrt(asymptotic_var(.data$value, w)))) %>% + dplyr::ungroup() } diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 052c0f40..844ba13d 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -9,7 +9,7 @@ #' whereas SE corresponds to the square root of total asymptotic variance #' (see Remark 3 of Vihola et al. (2020)). #' -#' +#' @importFrom rlang .data #' @param object Output from \code{run_mcmc} #' @param variable Are the summary statistics computed for either #' \code{"theta"} (default), \code{"states"}, or \code{"both"}? @@ -46,21 +46,35 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", if (variable %in% c("theta", "both")) { d <- tidyr::pivot_wider( as.data.frame(object, variable = "theta", expand = TRUE), - values_from = value, names_from = variable) + values_from = .data$value, names_from = .data$variable) } if (variable %in% c("states", "both")) { if (object$output_type != 1) stop("Cannot return summary of states as the MCMC type was not 'full'. ") - if (missing(times)) times <- seq_len(nrow(object$alpha)) - if (missing(states)) states <- seq_len(ncol(object$alpha)) + if (missing(times)) { + times <- seq_len(nrow(object$alpha)) + } else { + if (!test_integerish(times, lower = 1, upper = nrow(object$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'times' should contain indices between 1 and ", + nrow(object$alpha),".")) + } + if (missing(states)) { + states <- seq_len(ncol(object$alpha)) + } else { + if (!test_integerish(states, lower = 1, upper = ncol(object$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'states' should contain indices between 1 and ", + ncol(object$alpha),".")) + } d_states <- tidyr::pivot_wider( as.data.frame(object, variable = "states", expand = TRUE, times = times, states = states, use_times = FALSE), - values_from = value, - names_from = c(variable, time), + values_from = .data$value, + names_from = c(.data$variable, .data$time), names_glue = "{variable}[{time}]") if (variable == "both") { d <- cbind(d, d_states[, -(1:2)]) @@ -72,11 +86,11 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", summary_f_is <- function(x, w) { c(Mean = weighted_mean(x, w), SE = sqrt(asymptotic_var(x, w, method)), - SD = sqrt(diagis::weighted_var(x, w)), - diagis::weighted_quantile(x, w, probs), + SD = sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs), ESS = round(estimate_ess(x, w, method)), - SE_IS = diagis::weighted_se(x, w), - ESS_IS = round(diagis::ess(w, identity, x))) + SE_IS = weighted_se(x, w), + ESS_IS = round(ess(w, identity, x))) } as.data.frame(t(apply(d[, -(1:2)], 2, summary_f_is, w = d$weight))) } else { @@ -89,14 +103,12 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", } } - #' Print Results from MCMC Run #' #' Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. #' #' @method print mcmc_output #' @importFrom diagis weighted_mean weighted_var weighted_se ess -#' @importFrom coda mcmc #' @importFrom stats var #' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. #' @param ... Ignored. diff --git a/R/priors.R b/R/priors.R index 81554884..1f76937f 100644 --- a/R/priors.R +++ b/R/priors.R @@ -50,14 +50,14 @@ #' # Further examples for diagnostic purposes: #' uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -#' tnormal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +#' tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) #' halfnormal(c(0, 0.2), c(1.0, 1.2)) #' gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) #' #' # longer versions: #' uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -#' tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +#' tnormal_prior(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) #' halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) #' gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) #' } @@ -158,7 +158,7 @@ tnormal_prior <- function(init, mean, sd, min = -Inf, max = Inf) { if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } - if (init < min | init > max) { + if (any(init < min) | any(init > max)) { stop(paste("Initial value for parameter with truncated Normal is not", "between the lower and upper bounds.", sep = " ")) } diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 1001f5dc..75e6c16c 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -31,6 +31,7 @@ #' \code{burnin=0}). #' #' @importFrom stats tsp +#' @importFrom rlang is_interactive #' @param model Model of class \code{bssm_model}. #' @param iter Positive integer defining the total number of MCMC iterations. #' @param output_type Either \code{"full"} @@ -106,8 +107,9 @@ #' @param L_c,L_f For \code{ssm_sde} models, Positive integer values defining #' the discretization levels for first and second stages (defined as 2^L). #' For pseudo-marginal methods (\code{"pm"}), maximum of these is used. -#' @param verbose If \code{TRUE} (default), prints a progress bar to the -#' console. Set to \code{FALSE} if number of iterations is less than 50. +#' @param verbose If \code{TRUE}, prints a progress bar to the console. If +#' missing, defined by \code{rlang::is_interactive}. +#' Set to \code{FALSE} if number of iterations is less than 50. #' @param ... Ignored. #' @export #' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower used where applicable. @@ -171,8 +173,8 @@ run_mcmc <- function(model, ...) { run_mcmc.gaussian <- function(model, iter, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, - seed = sample(.Machine$integer.max, size = 1), verbose = TRUE, ...) { - + seed = sample(.Machine$integer.max, size = 1), + verbose, ...) { check_missingness(model) @@ -184,8 +186,13 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) + + if (missing(verbose)) { + verbose <- is_interactive() + } else { if (!test_flag(verbose)) stop("Argument 'verbose' should be TRUE or FALSE. ") + } if (iter < 50) verbose <- FALSE if (length(model$theta) == 0) @@ -379,7 +386,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, local_approx = TRUE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, - conv_tol = 1e-8, verbose = TRUE, ...) { + conv_tol = 1e-8, verbose, ...) { check_missingness(model) @@ -407,8 +414,12 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) - if (!test_flag(verbose)) - stop("Argument 'verbose' should be TRUE or FALSE. ") + if (missing(verbose)) { + verbose <- is_interactive() + } else { + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + } if (iter < 50) verbose <- FALSE if (!test_flag(local_approx)) { @@ -544,7 +555,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, - conv_tol = 1e-8, iekf_iter = 0, verbose = TRUE, ...) { + conv_tol = 1e-8, iekf_iter = 0, verbose, ...) { check_missingness(model) @@ -573,8 +584,12 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", burnin <- check_intmax(burnin, "burnin", max = 1e12) iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) - if (!test_flag(verbose)) - stop("Argument 'verbose' should be TRUE or FALSE. ") + if (missing(verbose)) { + verbose <- is_interactive() + } else { + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + } if (iter < 50) verbose <- FALSE if (length(model$theta) == 0) @@ -707,7 +722,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", L_c, L_f, burnin = floor(iter/2), thin = 1, gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, - threads = 1, seed = sample(.Machine$integer.max, size = 1), verbose = TRUE, + threads = 1, seed = sample(.Machine$integer.max, size = 1), verbose, ...) { check_missingness(model) @@ -737,8 +752,12 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) - if (!test_flag(verbose)) - stop("Argument 'verbose' should be TRUE or FALSE. ") + if (missing(verbose)) { + verbose <- is_interactive() + } else { + if (!test_flag(verbose)) + stop("Argument 'verbose' should be TRUE or FALSE. ") + } if (iter < 50) verbose <- FALSE if (length(model$theta) == 0) diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R index 40f30fb4..7841c52e 100644 --- a/R/srr-stats-standards.R +++ b/R/srr-stats-standards.R @@ -39,9 +39,6 @@ NULL #' @srrstatsNA {BS3.1, BS3.2} Not really relevant for SSMs, or at least #' difficult to check this kind of thing in general. #' @srrstatsNA {BS6.1, BS6.2, BS6.3, BS6.5} Just suggests and illustrates using -#' ggplot or bayesplot packages, with several examples.#' -#' @srrstatsNA {BS7.4} The computation of posterior predicted/fitted value can -#' be somewhat time consuming so it is not done automatically (there is a -#' fitted/predict functions for that) +#' ggplot or bayesplot packages, with several examples. #' @noRd NULL diff --git a/inst/CITATION b/inst/CITATION index ebefa4d3..1292f448 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -6,7 +6,7 @@ c( author="Jouni Helske and Matti Vihola", year="2021", journal = "R Journal", - note = "To appear" + note = "To appear", url = "https://arxiv.org/abs/2101.08492" ), bibentry( diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index 2bc00bb0..f69bcdd0 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -61,7 +61,12 @@ head(as.data.frame(out, variable = "state")) head(as.data.frame(out, variable = "theta", expand = FALSE)) # IS-weighted version: -out_is <- run_mcmc(model, iter = 2000, particles = 10, mcmc_type = "is2") +out_is <- run_mcmc(model, iter = 2000, particles = 10, + mcmc_type = "is2") head(as.data.frame(out_is, variable = "theta")) } +\seealso{ +\code{as_draws} which converts the output for +\code{as_draws} object. +} diff --git a/man/as_draws.Rd b/man/as_draws-mcmc_output.Rd similarity index 85% rename from man/as_draws.Rd rename to man/as_draws-mcmc_output.Rd index 64ef0a45..60dd922f 100644 --- a/man/as_draws.Rd +++ b/man/as_draws-mcmc_output.Rd @@ -2,9 +2,14 @@ % Please edit documentation in R/as_draws.R \name{as_draws_df.mcmc_output} \alias{as_draws_df.mcmc_output} -\title{Convert \code{run_mcmc} output to \code{draws_df} format} +\alias{as_draws} +\alias{as_draws_df} +\alias{as_draws.mcmc_output} +\title{Convert \code{run_mcmc} Output to \code{draws_df} Format} \usage{ \method{as_draws_df}{mcmc_output}(x, times, states, ...) + +\method{as_draws}{mcmc_output}(x, times, states, ...) } \arguments{ \item{x}{An object of class \code{mcmc_output}.} @@ -41,7 +46,6 @@ model <- bsm_lg(Nile, a1 = 1000, P1 = 500^2) fit1 <- run_mcmc(model, iter = 2000) -library("posterior") draws <- as_draws(fit1) head(draws, 4) estimate_ess(draws$sd_y) @@ -49,15 +53,15 @@ summary(fit1, return_se = TRUE) # More chains: model$theta[] <- c(50, 150) # change initial value -fit2 <- run_mcmc(model, iter = 2000) +fit2 <- run_mcmc(model, iter = 2000, verbose = FALSE) model$theta[] <- c(150, 50) # change initial value -fit3 <- run_mcmc(model, iter = 2000) +fit3 <- run_mcmc(model, iter = 2000, verbose = FALSE) -draws <- posterior::bind_draws(as_draws(fit1), - as_draws(fit2), as_draws(fit3), along = "chain") # it is actually enough to transform first mcmc_output to draws object, # rest are transformed automatically inside bind_draws +draws <- posterior::bind_draws(as_draws(fit1), + as_draws(fit2), as_draws(fit3), along = "chain") + posterior::rhat(draws$sd_y) -posterior::summarise_draws(draws) } diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index f14251a9..fc90d25b 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -90,14 +90,14 @@ tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) # Further examples for diagnostic purposes: uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -tnormal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) halfnormal(c(0, 0.2), c(1.0, 1.2)) gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) # longer versions: uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -tnormal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), c(3.3, 3.3)) +tnormal_prior(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) } diff --git a/man/check.Rd b/man/check.Rd index 9c49fda1..9eef2acb 100644 --- a/man/check.Rd +++ b/man/check.Rd @@ -79,6 +79,8 @@ check_theta(x) check_missingness(x) } \arguments{ +\item{x}{Variable to be checked.} + \item{multivariate}{Logical, should \code{p} be larger than 1?} \item{distribution}{Distribution(s) of the responses.} diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index bd455ee6..7ee7b2f2 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -30,12 +30,16 @@ value. Although this saves bit memory and is especially convenient for IS-corrected MCMC, sometimes we want to have the usual sample paths (for example for drawing traceplots). Function \code{expand_sample} returns the expanded sample based on the -counts. Note that for IS-corrected output the expanded -sample corresponds to the approximate posterior i.e., +counts (in form of \code{coda::mcmc} object. Note that for +the IS-MCMC the expanded sample corresponds to the approximate posterior i.e., the weights are ignored. } +\details{ +This functions is mostly for backwards compatibility, methods +\code{as.data.frame} and \code{as_draws} produce likely more convenient +output. +} \examples{ - set.seed(1) n <- 50 x <- cumsum(rnorm(n)) @@ -46,8 +50,9 @@ fit <- run_mcmc(model, iter = 1e4) # Traceplots for theta plot.ts(expand_sample(fit, variable = "theta")) # Traceplot for x_5 -plot.ts(expand_sample(fit, variable = "states", times = 5, states = 1)) +plot.ts(expand_sample(fit, variable = "states", times = 5, + states = 1)$level) } \seealso{ -\code{as_draws}. +\code{as.data.frame.mcmc_output} and \code{as_draws.mcmc_output}. } diff --git a/man/negbin_series.Rd b/man/negbin_series.Rd index 53855e0e..5647e3c9 100644 --- a/man/negbin_series.Rd +++ b/man/negbin_series.Rd @@ -37,12 +37,9 @@ bssm_model <- bsm_ng(y, distribution = "negative binomial") # run the MCMC, small number of iterations for CRAN -fit_bssm <- run_mcmc(bssm_model, iter = 6000, burnin = 1000, +fit_bssm <- run_mcmc(bssm_model, iter = 2000, burnin = 1000, particles = 10) fit_bssm - -plot.ts(ts(cbind(sd_level=draws$sd_level, draws$sd_slope), start = 1000), - xlab = "Iteration", main = "Traceplots") } \references{ Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 3a5d8f22..08114d9c 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -22,7 +22,7 @@ run_mcmc(model, ...) end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), - verbose = TRUE, + verbose, ... ) @@ -44,7 +44,7 @@ run_mcmc(model, ...) seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-08, - verbose = TRUE, + verbose, ... ) @@ -66,7 +66,7 @@ run_mcmc(model, ...) max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, - verbose = TRUE, + verbose, ... ) @@ -86,7 +86,7 @@ run_mcmc(model, ...) end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), - verbose = TRUE, + verbose, ... ) } @@ -140,6 +140,10 @@ models.} \item{seed}{Seed for the random number generator (positive integer).} +\item{verbose}{If \code{TRUE}, prints a progress bar to the console. If +missing, defined by \code{rlang::is_interactive}. +Set to \code{FALSE} if number of iterations is less than 50.} + \item{particles}{Number of state samples per MCMC iteration for models other than linear-Gaussian models. Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} @@ -185,9 +189,7 @@ with \code{iekf_iter} iterations. Used only for models of class \item{L_c, L_f}{For \code{ssm_sde} models, Positive integer values defining the discretization levels for first and second stages (defined as 2^L). -For pseudo-marginal methods (\code{"pm"}), maximum of these is used. -@param verbose If \code{TRUE} (default), prints a progress bar to the -console. Set to \code{FALSE} if number of iterations is less than 50.} +For pseudo-marginal methods (\code{"pm"}), maximum of these is used.} } \description{ Adaptive Markov chain Monte Carlo simulation for SSMs using diff --git a/src/approx_mcmc.cpp b/src/approx_mcmc.cpp index 3400891f..ed96b096 100644 --- a/src/approx_mcmc.cpp +++ b/src/approx_mcmc.cpp @@ -216,6 +216,8 @@ void approx_mcmc::amcmc(T model, const unsigned int method, const bool end_ram, } } } + if (verbose) Rcpp::Rcout<<"\n"; + if (n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); @@ -324,6 +326,8 @@ void approx_mcmc::amcmc(ssm_sde model, const unsigned int nsim, const bool end_r } } } + if (verbose) Rcpp::Rcout<<"\n"; + if (n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); @@ -1610,6 +1614,8 @@ void approx_mcmc::ekf_mcmc(ssm_nlg model, const bool end_ram) { } } } + if (verbose) Rcpp::Rcout<<"\n"; + if (n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); diff --git a/src/mcmc.cpp b/src/mcmc.cpp index 5d161296..074fa8ba 100644 --- a/src/mcmc.cpp +++ b/src/mcmc.cpp @@ -288,8 +288,9 @@ void mcmc::mcmc_gaussian(T model, const bool end_ram, } } } + if (verbose) Rcpp::Rcout<<"\n"; + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); - if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC run. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); @@ -478,11 +479,13 @@ void mcmc::pm_mcmc( } } } + if (verbose) Rcpp::Rcout<<"\n"; + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); + if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] } - if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC run. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); } @@ -671,6 +674,9 @@ void mcmc::da_mcmc(T model, } } } + if (verbose) Rcpp::Rcout<<"\n"; + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); + if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] } @@ -819,11 +825,13 @@ void mcmc::pm_mcmc( } } } + if (verbose) Rcpp::Rcout<<"\n"; + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); + if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] } - if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC run. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); } @@ -972,11 +980,13 @@ void mcmc::da_mcmc(ssm_sde model, } } } + if (verbose) Rcpp::Rcout<<"\n"; + if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC. Check your model."); + if (output_type == 2) { Vt += Valphahat / (iter - burnin); // Var[E(alpha)] + E[Var(alpha)] } - if(n_stored == 0) Rcpp::stop("No proposals were accepted in MCMC run. Check your model."); trim_storage(); acceptance_rate /= (iter - burnin); } diff --git a/tests/testthat/test_approx.R b/tests/testthat/test_approx.R index 69f833ab..be484da5 100644 --- a/tests/testthat/test_approx.R +++ b/tests/testthat/test_approx.R @@ -4,7 +4,7 @@ context("Test Gaussian approximation") test_that("Gaussian approximation results of bssm and KFAS coincide", { - library(KFAS) + suppressWarnings(library(KFAS)) set.seed(123) model_KFAS <- SSModel(rpois(10, exp(2)) ~ SSMtrend(2, Q = list(1, 1), P1 = diag(100, 2)), distribution = "poisson") diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index be4bd405..528c9ecb 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -120,7 +120,7 @@ test_that("run_mcmc throws error with improper arguments", { "Cannot return summary of states as the MCMC type was not 'full'.") model_bssm$theta[] <- Inf - expect_error(run_mcmc(model, iter = 1e4, particles = 10), + expect_error(run_mcmc(model_bssm, iter = 1e4, particles = 10), "Initial prior probability is not finite.") }) @@ -131,10 +131,10 @@ test_that("MCMC messages can be suppressed", { sd_y = uniform(1, 0, 10), sd_level = uniform(1, 0, 10)) - expect_equal(capture_output(run_mcmc(run_mcmc(model_bssm, iter = 100, - verbose = FALSE))), "") - expect_equal(capture_output(run_mcmc(run_mcmc(model_bssm, iter = 10, - verbose = FALSE))), "") + expect_equal(capture_output(run_mcmc(model_bssm, iter = 100, + verbose = FALSE)), "") + expect_equal(capture_output(run_mcmc(model_bssm, iter = 10, + verbose = FALSE)), "") }) test_that("MCMC results for Gaussian model are correct", { diff --git a/tests/testthat/test_sde.R b/tests/testthat/test_sde.R index a81e5855..79622d6b 100644 --- a/tests/testthat/test_sde.R +++ b/tests/testthat/test_sde.R @@ -29,9 +29,9 @@ test_that("MCMC for SDE works", { L_c = 2, L_f = 6, threads = 2), NA) paper <- c(0.053, 0.253, 1.058, 1.254, 2.960) - expect_equivalent(diagis::weighted_mean(out$theta, out$weights * out$counts), + expect_equivalent(weighted_mean(out$theta, out$weights * out$counts), paper[1:3], tol = 0.1) - expect_equivalent(diagis::weighted_mean(t(out$alpha[c(1,50),1,]), + expect_equivalent(weighted_mean(t(out$alpha[c(1,50),1,]), out$weights * out$counts), paper[4:5], tol = 0.1) expect_error(out <- run_mcmc(model, iter = 2e4, burnin = 5000, From a6cdf17acf50a6af2ac6a1681dcb355aeee12017 Mon Sep 17 00:00:00 2001 From: helske Date: Sun, 21 Nov 2021 22:56:54 +0200 Subject: [PATCH 132/180] fix latex rendering in readme --- README.Rmd | 24 +- README.md | 853 +++++++++++++++++++++++------------------------------ 2 files changed, 372 insertions(+), 505 deletions(-) diff --git a/README.Rmd b/README.Rmd index 5b6d21ef..8db02f3d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -184,24 +184,17 @@ bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% Now let's assume that we also want to use the solar radiation variable as predictor for ozone. As it contains few missing values, we cannot use it directly. As the number of missing time points is very small, simple imputation would likely be acceptable, but let's consider more another approach. For simplicity, the slope terms of the previous models are now omitted, and we focus on the Gaussian case. Let $\mu_t$ be the true solar radiation at time $t$. Now for ozone $O_t$ we assume following model: -$$ -\begin{aligned} -O_t &= D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t,\\ -\alpha_{t+1} &= \alpha_t + \sigma_\eta\eta_t,\\ -\alpha_1 &\sim N(0, 100^2\textrm{I}), -\end{aligned} -$$ -where $D_t = \beta X_t$ contains regression terms related to wind and temperature, $\alpha_t$ is the time varying intercept term, and $\beta_S$ is the effect of solar radiation $\mu_t$. + +$O_t = D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t$\ +$\alpha_{t+1} = \alpha_t + \sigma_\eta\eta_t$\ +$\alpha_1 \sim N(0, 100^2\textrm{I})$,\ +wheere $D_t = \beta X_t$ contains regression terms related to wind and temperature, $\alpha_t$ is the time varying intercept term, and $\beta_S$ is the effect of solar radiation $\mu_t$. Now for the observed solar radiation $S_t$ we assume -$$ -\begin{aligned} -S_t &= \mu_t\\ -\mu_{t+1} &= \mu_t + \sigma_\xi\xi_t,\\ -\mu_1 &\sim N(0, 100^2), -\end{aligned} -$$ +$S_t = \mu_t$\ +$\mu_{t+1} = \mu_t + \sigma_\xi\xi_t,$\ +$\mu_1 \sim N(0, 100^2)$,\ i.e. we assume as simple random walk for the $\mu$ which we observe without error or not at all (there is no error term in the observation equation $S_t=\mu_t$). We combine these two models as a bivariate Gaussian model with `ssm_mlg`: @@ -271,3 +264,4 @@ pred %>% filter(Variable == "Ozone") %>% theme_bw() ``` +See more examples in the paper, vignettes, and in the docs. \ No newline at end of file diff --git a/README.md b/README.md index 34a9dd9d..f6b7b81c 100644 --- a/README.md +++ b/README.md @@ -1,490 +1,363 @@ - - -# bssm - - - -[![Project Status: Active - The project has reached a stable, usable -state and is being actively -developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) -[![Codecov test -coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![cran -version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) -[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) - - - -The `bssm` R package provides efficient methods for Bayesian inference -of state space models via particle Markov chain Monte Carlo and -importance sampling type weighted MCMC. Currently Gaussian, Poisson, -binomial, negative binomial, and Gamma observation densities with -linear-Gaussian state dynamics, as well as general non-linear Gaussian -models and discretely observed latent diffusion processes are supported. - -For details, see - -- [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to - appear in R Journal), -- [Package vignettes at - CRAN](https://cran.r-project.org/web/packages/bssm/index.html) -- Paper on [Importance sampling type estimators based on approximate - marginal Markov chain Monte - Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) - -There are also couple posters and a talk related to IS-correction -methodology and bssm package: - -- [UseR!2021 talk - slides](https://jounihelske.netlify.app/talk/user2021/) -- [SMC 2017 workshop: Accelerating MCMC with an - approximation](http://users.jyu.fi/~jovetale/posters/SMC2017) -- [UseR!2017: Bayesian non-Gaussian state space models in - R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) - -The `bssm` package was originally developed with the support of Academy -of Finland grants 284513, 312605, and 311877. Current development is -focused on increased usability and stability. - -## Installation - -You can install the released version of bssm from -[CRAN](https://CRAN.R-project.org) with: - -``` r -install.packages("bssm") -``` - -And the development version from [GitHub](https://github.com/) with: - -``` r -# install.packages("devtools") -devtools::install_github("helske/bssm") -``` - -Or from R-universe with - -``` r -install.packages("bssm", repos = "https://helske.r-universe.dev") -``` - -## Example - -Consider the daily air quality measurements in New Your from May to -September 1973, available in the `datasets` package. Let’s try to -predict the missing ozone levels by simple linear-Gaussian local linear -trend model with temperature and wind as explanatory variables (missing -response variables are handled naturally in the state space modelling -framework, however no missing values in covariates are normally -allowed); - -``` r -library("bssm") -#> -#> Attaching package: 'bssm' -#> The following object is masked from 'package:base': -#> -#> gamma -library("dplyr") -#> -#> Attaching package: 'dplyr' -#> The following objects are masked from 'package:stats': -#> -#> filter, lag -#> The following objects are masked from 'package:base': -#> -#> intersect, setdiff, setequal, union -library("ggplot2") -set.seed(1) - -data("airquality", package = "datasets") - -# Covariates as matrix. For complex cases, check out as_bssm function -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() - -model <- bsm_lg(airquality$Ozone, - xreg = xreg, - # Define priors, see ?bssm_prior - # Initial value followed by parameters of the prior distribution - beta = normal_prior(rep(0, ncol(xreg)), 0, 1), - sd_y = gamma_prior(1, 2, 0.01), - sd_level = gamma_prior(1, 2, 0.01), - sd_slope = gamma_prior(1, 2, 0.01)) - -fit <- run_mcmc(model, iter = 20000, burnin = 5000) -fit -#> -#> Call: -#> run_mcmc.gaussian(model = model, iter = 20000, burnin = 5000) -#> -#> Iterations = 5001:20000 -#> Thinning interval = 1 -#> Length of the final jump chain = 3593 -#> -#> Acceptance rate after the burn-in period: 0.239 -#> -#> Summary for theta: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 -#> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 -#> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 -#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 -#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 -#> -#> Summary for alpha_154: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> level[154] -28.3163054 0.69650977 20.132341 -69.271049 11.797133 835 -#> slope[154] -0.3740463 0.03683278 1.685733 -4.065499 2.830134 2094 -#> -#> Run time: -#> user system elapsed -#> 0.88 0.02 0.89 - -obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) - -pred <- fitted(fit, model) -pred %>% - ggplot(aes(x = Time, y = Mean)) + - geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), - alpha = 0.5, fill = "steelblue") + - geom_line() + - geom_point(data = obs, - aes(x = Time, y = Ozone), colour = "Tomato") + - theme_bw() -``` - - - -Same model but now assuming observations are from Gamma distribution: - -``` r -model2 <- bsm_ng(airquality$Ozone, - xreg = xreg, - beta = normal(rep(0, ncol(xreg)), 0, 1), - distribution = "gamma", - phi = gamma_prior(1, 2, 0.01), - sd_level = gamma_prior(1, 2, 0.1), - sd_slope = gamma_prior(1, 2, 0.1)) - -fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) -fit2 -#> -#> Call: -#> run_mcmc.nongaussian(model = model2, iter = 20000, particles = 10, -#> burnin = 5000) -#> -#> Iterations = 5001:20000 -#> Thinning interval = 1 -#> Length of the final jump chain = 3858 -#> -#> Acceptance rate after the burn-in period: 0.257 -#> -#> Summary for theta: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 -#> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 -#> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 -#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 -#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 -#> SE_IS ESS_IS -#> sd_level 2.927386e-04 10591 -#> sd_slope 3.031489e-05 7766 -#> phi 4.411840e-03 14611 -#> Wind 1.263047e-04 13905 -#> Temp 7.128104e-05 14485 -#> -#> Summary for alpha_154: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> level[154] -0.200656509 0.0201721601 0.73134471 -1.62501396 1.24522802 1314 -#> slope[154] -0.002689176 0.0005121944 0.02289051 -0.04650504 0.04724173 1997 -#> SE_IS ESS_IS -#> level[154] 0.005987284 9458 -#> slope[154] 0.000191620 6448 -#> -#> Run time: -#> user system elapsed -#> 10.54 0.05 10.54 -``` - -Comparison: - -``` r -pred2 <- fitted(fit2, model2) - -bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% - ggplot(aes(x = Time, y = Mean)) + - geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), - alpha = 0.25) + - geom_line(aes(colour = Model)) + - geom_point(data = obs, - aes(x = Time, y = Ozone)) + - theme_bw() -``` - - - -Now let’s assume that we also want to use the solar radiation variable -as predictor for ozone. As it contains few missing values, we cannot use -it directly. As the number of missing time points is very small, simple -imputation would likely be acceptable, but let’s consider more another -approach. For simplicity, the slope terms of the previous models are now -omitted, and we focus on the Gaussian case. Let *μ**t* be the -true solar radiation at time *t*. Now for ozone *O**t* we -assume following model: -$$ -\\begin{aligned} -O\_t &= D\_t + \\alpha\_t + \\beta\_S \\mu\_t + \\sigma\_\\epsilon \\epsilon\_t,\\\\ -\\alpha\_{t+1} &= \\alpha\_t + \\sigma\_\\eta\\eta\_t,\\\\ -\\alpha\_1 &\\sim N(0, 100^2\\textrm{I}), -\\end{aligned} -$$ -where *D**t* = *β**X**t* contains regression terms -related to wind and temperature, *α**t* is the time varying -intercept term, and *β**S* is the effect of solar radiation -*μ**t*. - -Now for the observed solar radiation *S**t* we assume - -$$ -\\begin{aligned} -S\_t &= \\mu\_t\\\\ -\\mu\_{t+1} &= \\mu\_t + \\sigma\_\\xi\\xi\_t,\\\\ -\\mu\_1 &\\sim N(0, 100^2), -\\end{aligned} -$$ -i.e. we assume as simple random walk for the *μ* which we observe -without error or not at all (there is no error term in the observation -equation *S**t* = *μ**t*). - -We combine these two models as a bivariate Gaussian model with -`ssm_mlg`: - -``` r -# predictors (not including solar radiation) for ozone -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() - -# Function which outputs new model components given the parameter vector theta -update_fn <- function(theta) { - D <- rbind(t(xreg %*% theta[1:2]), 1) - Z <- matrix(c(1, 0, theta[3], 1), 2, 2) - R <- diag(exp(theta[4:5])) - H <- diag(c(exp(theta[6]), 0)) - # add third dimension so we have p x n x 1, p x m x 1, p x p x 1 arrays - dim(Z)[3] <- dim(R)[3] <- dim(H)[3] <- 1 - list(D = D, Z = Z, R = R, H = H) -} - -# Function for log-prior density -prior_fn <- function(theta) { - sum(dnorm(theta[1:3], 0, 10, log = TRUE)) + - sum(dgamma(exp(theta[4:6]), 2, 0.01, log = TRUE)) + - sum(theta[4:6]) # log-jacobian -} - -init_theta <- c(0, 0, 0, log(50), log(5), log(20)) -comps <- update_fn(init_theta) - -model <- ssm_mlg(y = cbind(Ozone = airquality$Ozone, Solar = airquality$Solar.R), - Z = comps$Z, D = comps$D, H = comps$H, T = diag(2), R = comps$R, - a1 = rep(0, 2), P1 = diag(100, 2), init_theta = init_theta, - state_names = c("alpha", "mu"), update_fn = update_fn, - prior_fn = prior_fn) - -fit <- run_mcmc(model, iter = 60000, burnin = 10000) -fit -#> -#> Call: -#> run_mcmc.gaussian(model = model, iter = 60000, burnin = 10000) -#> -#> Iterations = 10001:60000 -#> Thinning interval = 1 -#> Length of the final jump chain = 12234 -#> -#> Acceptance rate after the burn-in period: 0.245 -#> -#> Summary for theta: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> theta_1 -3.89121114 0.0233827004 0.58715113 -5.0085134 -2.6915137 631 -#> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 -#> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 -#> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 -#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 -#> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 -#> -#> Summary for alpha_154: -#> -#> Mean SE SD 2.5% 97.5% ESS -#> alpha[154] -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 -#> mu[154] 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 -#> -#> Run time: -#> user system elapsed -#> 11.97 0.11 12.04 -``` - -Draw predictions: - -``` r -pred <- fitted(fit, model) - -obs <- data.frame(Time = 1:nrow(airquality), - Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) - -pred %>% filter(Variable == "Solar") %>% - ggplot(aes(x = Time, y = Mean)) + - geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), - alpha = 0.5, fill = "steelblue") + - geom_line() + - geom_point(data = obs, - aes(x = Time, y = Solar), colour = "Tomato") + - theme_bw() -``` - - - -``` r - -obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) - -pred %>% filter(Variable == "Ozone") %>% - ggplot(aes(x = Time, y = Mean)) + - geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), - alpha = 0.5, fill = "steelblue") + - geom_line() + - geom_point(data = obs, - aes(x = Time, y = Ozone), colour = "Tomato") + - theme_bw() -``` - - - -## Recent changes (For all changes, see NEWS file.) - -#### bssm 1.1.6 (Release date: 2021-09-06) - -- Cleaned codes and added more comprehensive tests in line with - pkgcheck tests. This resulted in finding and fixing multiple bugs: -- Fixed a bug in EKF-based particle filter which returned filtered - estimates also in place of one-step ahead predictions. -- Fixed a bug which caused an error in suggest\_N for nlg\_ssm. -- Fixed a bug which caused incorrect sampling of smoothing - distribution for ar1\_lg model when predicting past or when using - simulation smoother. -- Fixed a bug which caused an error when predicting past values in - multivariate time series case. -- Fixed sampling of negative binomial distribution in predict method, - which used std::negative\_binomial which converts non-integer phi to - integer. Sampling now uses Gamma-Poisson mixture for simulation. - -#### bssm 1.1.4 (Release date: 2021-04-13) - -- Better documentation for SV model, and changed ordering of arguments - to emphasise the recommended parameterization. -- Fixed predict method for SV model. - -#### bssm 1.1.3-2 (Release date: 2021-02-24) - -- Fixed missing parenthesis causing compilation fail in case of no - OpenMP support. -- Added pandoc version >= 1.12.3 to system requirements. - -#### bssm 1.1.3-1 (Release date: 2021-02-22) - -- Fixed PM-MCMC and DA-MCMC for SDE models and added an example to - `ssm_sde`. -- Added vignette for SDE models. -- Updated citation information and streamlined the main vignette. - -#### bssm 1.1.2 (Release date: 2021-02-08) - -- Some bug fixes, see NEWS for details. - -#### bssm 1.1.0 (Release date: 2021-01-19) - -- Added function `suggest_N` which can be used to choose suitable - number of particles for IS-MCMC. -- Added function `post_correct` which can be used to update previous - approximate MCMC with IS-weights. -- Gamma priors are now supported in easy-to-use models such as - `bsm_lg`. -- The adaptation of the proposal distribution now continues also after - the burn-in by default. -- Changed default MCMC type to typically most efficient and robust - IS2. -- Renamed `nsim` argument to `particles` in most of the R functions - (`nsim` also works with a warning). -- Fixed a bug with bsm models with covariates, where all standard - deviation parameters were fixed. This resulted error within MCMC - algorithms. -- Fixed a dimension drop bug in the predict method which caused error - for univariate models. -- Fixed few typos in vignette (thanks Kyle Hussman) and added more - examples. - -#### bssm 1.0.1-1 (Release date: 2020-11-12) - -- Added an argument `future` for predict method which allows - predictions for current time points by supplying the original model - (e.g., for posterior predictive checks). At the same time the - argument name `future_model` was changed to `model`. -- Fixed a bug in summary.mcmc\_run which resulted error when trying to - obtain summary for states only. -- Added a check for Kalman filter for a degenerate case where all - observational level and state level variances are zero. -- Renamed argument `n_threads` to `threads` for consistency with - `iter` and `burnin` arguments. -- Improved documentation, added examples. -- Added a vignette regarding psi-APF for non-linear models. - -#### bssm 1.0.0 (Release date: 2020-06-09) - -Major update - -- Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely - on C++ snippets). -- Added support for multivariate non-Gaussian models. -- Added support for gamma distributions. -- Added the function as.data.frame for mcmc output which converts the - MCMC samples to data.frame format for easier post-processing. -- Added truncated normal prior. -- Many argument names and model building functions have been changed - for clarity and consistency. -- Major overhaul of C++ internals which can bring minor efficiency - gains and smaller installation size. -- Allow zero as initial value for positive-constrained parameters of - bsm models. -- Small changes to summary method which can now return also only - summaries of the states. -- Fixed a bug in initializing run\_mcmc for negative binomial model. -- Fixed a bug in phi-APF for non-linear models. -- Reimplemented predict method which now always produces data frame of - samples. - -#### bssm 0.1.11 (Release date: 2020-02-25) - -- Switched (back) to approximate posterior in RAM for PM-SPDK and - PM-PSI, as it seems to work better with noisy likelihood estimates. -- Print and summary methods for MCMC output are now coherent in their - output. - -#### bssm 0.1.10 (Release date: 2020-02-04) - -- Fixed missing weight update for IS-SPDK without OPENMP flag. -- Removed unused usage argument … from expand\_sample. - -#### bssm 0.1.9 (Release date: 2020-01-27) - -- Fixed state sampling for PM-MCMC with SPDK. -- Added ts attribute for svm model. -- Corrected asymptotic variance for summary methods. - -For older versions, see NEWS file. + + + +# bssm + + + +[![Project Status: Active - The project has reached a stable, usable +state and is being actively +developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) +[![Codecov test +coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) +[![cran +version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) +[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) + + + +The `bssm` R package provides efficient methods for Bayesian inference +of state space models via particle Markov chain Monte Carlo and +importance sampling type weighted MCMC. Currently Gaussian, Poisson, +binomial, negative binomial, and Gamma observation densities with +linear-Gaussian state dynamics, as well as general non-linear Gaussian +models and discretely observed latent diffusion processes are supported. + +For details, see + +- [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to + appear in R Journal), +- [Package vignettes at + CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +- Paper on [Importance sampling type estimators based on approximate + marginal Markov chain Monte + Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) + +There are also couple posters and a talk related to IS-correction +methodology and bssm package: + +- [UseR!2021 talk + slides](https://jounihelske.netlify.app/talk/user2021/) +- [SMC 2017 workshop: Accelerating MCMC with an + approximation](http://users.jyu.fi/~jovetale/posters/SMC2017) +- [UseR!2017: Bayesian non-Gaussian state space models in + R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) + +The `bssm` package was originally developed with the support of Academy +of Finland grants 284513, 312605, and 311877. Current development is +focused on increased usability. For recent changes, see NEWS file. + +## Installation + +You can install the released version of bssm from +[CRAN](https://CRAN.R-project.org) with: + +``` r +install.packages("bssm") +``` + +And the development version from [GitHub](https://github.com/) with: + +``` r +# install.packages("devtools") +devtools::install_github("helske/bssm") +``` + +Or from R-universe with + +``` r +install.packages("bssm", repos = "https://helske.r-universe.dev") +``` + +## Example + +Consider the daily air quality measurements in New Your from May to +September 1973, available in the `datasets` package. Let’s try to +predict the missing ozone levels by simple linear-Gaussian local linear +trend model with temperature and wind as explanatory variables (missing +response variables are handled naturally in the state space modelling +framework, however no missing values in covariates are normally +allowed); + +``` r +library("bssm") +#> +#> Attaching package: 'bssm' +#> The following object is masked from 'package:base': +#> +#> gamma +library("dplyr") +#> +#> Attaching package: 'dplyr' +#> The following objects are masked from 'package:stats': +#> +#> filter, lag +#> The following objects are masked from 'package:base': +#> +#> intersect, setdiff, setequal, union +library("ggplot2") +set.seed(1) + +data("airquality", package = "datasets") + +# Covariates as matrix. For complex cases, check out as_bssm function +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +model <- bsm_lg(airquality$Ozone, + xreg = xreg, + # Define priors, see ?bssm_prior + # Initial value followed by parameters of the prior distribution + beta = normal_prior(rep(0, ncol(xreg)), 0, 1), + sd_y = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.01), + sd_slope = gamma_prior(1, 2, 0.01)) + +fit <- run_mcmc(model, iter = 20000, burnin = 5000) +fit +#> +#> Call: +#> run_mcmc.gaussian(model = model, iter = 20000, burnin = 5000) +#> +#> Iterations = 5001:20000 +#> Thinning interval = 1 +#> Length of the final jump chain = 3593 +#> +#> Acceptance rate after the burn-in period: 0.239 +#> +#> Summary for theta: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 +#> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 +#> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 +#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 +#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 +#> +#> Summary for alpha_154: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> level[154] -28.3163054 0.69650977 20.132341 -69.271049 11.797133 835 +#> slope[154] -0.3740463 0.03683278 1.685733 -4.065499 2.830134 2094 +#> +#> Run time: +#> user system elapsed +#> 0.89 0.04 0.92 + +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred <- fitted(fit, model) +pred %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() +``` + + + +Same model but now assuming observations are from Gamma distribution: + +``` r +model2 <- bsm_ng(airquality$Ozone, + xreg = xreg, + beta = normal(rep(0, ncol(xreg)), 0, 1), + distribution = "gamma", + phi = gamma_prior(1, 2, 0.01), + sd_level = gamma_prior(1, 2, 0.1), + sd_slope = gamma_prior(1, 2, 0.1)) + +fit2 <- run_mcmc(model2, iter = 20000, burnin = 5000, particles = 10) +fit2 +#> +#> Call: +#> run_mcmc.nongaussian(model = model2, iter = 20000, particles = 10, +#> burnin = 5000) +#> +#> Iterations = 5001:20000 +#> Thinning interval = 1 +#> Length of the final jump chain = 3858 +#> +#> Acceptance rate after the burn-in period: 0.257 +#> +#> Summary for theta: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 +#> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 +#> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 +#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 +#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 +#> SE_IS ESS_IS +#> sd_level 2.927386e-04 10591 +#> sd_slope 3.031489e-05 7766 +#> phi 4.411840e-03 14611 +#> Wind 1.263047e-04 13905 +#> Temp 7.128104e-05 14485 +#> +#> Summary for alpha_154: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> level[154] -0.200656509 0.0201721601 0.73134471 -1.62501396 1.24522802 1314 +#> slope[154] -0.002689176 0.0005121944 0.02289051 -0.04650504 0.04724173 1997 +#> SE_IS ESS_IS +#> level[154] 0.005987284 9458 +#> slope[154] 0.000191620 6448 +#> +#> Run time: +#> user system elapsed +#> 11.22 0.07 11.27 +``` + +Comparison: + +``` r +pred2 <- fitted(fit2, model2) + +bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), + alpha = 0.25) + + geom_line(aes(colour = Model)) + + geom_point(data = obs, + aes(x = Time, y = Ozone)) + + theme_bw() +``` + + + +Now let’s assume that we also want to use the solar radiation variable +as predictor for ozone. As it contains few missing values, we cannot use +it directly. As the number of missing time points is very small, simple +imputation would likely be acceptable, but let’s consider more another +approach. For simplicity, the slope terms of the previous models are now +omitted, and we focus on the Gaussian case. Let *μ**t* be the +true solar radiation at time *t*. Now for ozone *O**t* we +assume following model: + +*O**t* = *D**t* + *α**t* + *β**S**μ**t* + *σ**ϵ**ϵ**t* +*α**t* + 1 = *α**t* + *σ**η**η**t* +*α*1 ∼ *N*(0, 1002I), +wheere *D**t* = *β**X**t* contains regression +terms related to wind and temperature, *α**t* is the time +varying intercept term, and *β**S* is the effect of solar +radiation *μ**t*. + +Now for the observed solar radiation *S**t* we assume + +*S**t* = *μ**t* +*μ**t* + 1 = *μ**t* + *σ**ξ**ξ**t*, +*μ*1 ∼ *N*(0, 1002), +i.e. we assume as simple random walk for the *μ* which we observe +without error or not at all (there is no error term in the observation +equation *S**t* = *μ**t*). + +We combine these two models as a bivariate Gaussian model with +`ssm_mlg`: + +``` r +# predictors (not including solar radiation) for ozone +xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() + +# Function which outputs new model components given the parameter vector theta +update_fn <- function(theta) { + D <- rbind(t(xreg %*% theta[1:2]), 1) + Z <- matrix(c(1, 0, theta[3], 1), 2, 2) + R <- diag(exp(theta[4:5])) + H <- diag(c(exp(theta[6]), 0)) + # add third dimension so we have p x n x 1, p x m x 1, p x p x 1 arrays + dim(Z)[3] <- dim(R)[3] <- dim(H)[3] <- 1 + list(D = D, Z = Z, R = R, H = H) +} + +# Function for log-prior density +prior_fn <- function(theta) { + sum(dnorm(theta[1:3], 0, 10, log = TRUE)) + + sum(dgamma(exp(theta[4:6]), 2, 0.01, log = TRUE)) + + sum(theta[4:6]) # log-jacobian +} + +init_theta <- c(0, 0, 0, log(50), log(5), log(20)) +comps <- update_fn(init_theta) + +model <- ssm_mlg(y = cbind(Ozone = airquality$Ozone, Solar = airquality$Solar.R), + Z = comps$Z, D = comps$D, H = comps$H, T = diag(2), R = comps$R, + a1 = rep(0, 2), P1 = diag(100, 2), init_theta = init_theta, + state_names = c("alpha", "mu"), update_fn = update_fn, + prior_fn = prior_fn) + +fit <- run_mcmc(model, iter = 60000, burnin = 10000) +fit +#> +#> Call: +#> run_mcmc.gaussian(model = model, iter = 60000, burnin = 10000) +#> +#> Iterations = 10001:60000 +#> Thinning interval = 1 +#> Length of the final jump chain = 12234 +#> +#> Acceptance rate after the burn-in period: 0.245 +#> +#> Summary for theta: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> theta_1 -3.89121114 0.0233827004 0.58715113 -5.0085134 -2.6915137 631 +#> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 +#> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 +#> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 +#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 +#> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 +#> +#> Summary for alpha_154: +#> +#> Mean SE SD 2.5% 97.5% ESS +#> alpha[154] -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 +#> mu[154] 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 +#> +#> Run time: +#> user system elapsed +#> 12.08 0.13 12.14 +``` + +Draw predictions: + +``` r +pred <- fitted(fit, model) + +obs <- data.frame(Time = 1:nrow(airquality), + Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) + +pred %>% filter(Variable == "Solar") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Solar), colour = "Tomato") + + theme_bw() +``` + + + +``` r + +obs <- data.frame(Time = 1:nrow(airquality), + Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + +pred %>% filter(Variable == "Ozone") %>% + ggplot(aes(x = Time, y = Mean)) + + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), + alpha = 0.5, fill = "steelblue") + + geom_line() + + geom_point(data = obs, + aes(x = Time, y = Ozone), colour = "Tomato") + + theme_bw() +``` + + + +See more examples in the paper, vignettes, and in the docs. From 3fedcc577b5df0b056af8d219244d171865f5fd3 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 22 Nov 2021 09:19:45 +0200 Subject: [PATCH 133/180] fix summary output and some docs --- NAMESPACE | 1 - R/check_diagnostics.R | 9 ++-- R/models.R | 20 ++++++--- R/print_mcmc.R | 87 +++++++++++++++++++++++--------------- R/run_mcmc.R | 17 +++----- man/bsm_lg.Rd | 3 ++ man/bsm_ng.Rd | 2 +- man/check_diagnostics.Rd | 4 +- man/predict.mcmc_output.Rd | 4 +- man/run_mcmc.Rd | 17 +++----- man/ssm_ulg.Rd | 15 ++++--- man/summary.mcmc_output.Rd | 9 ++++ vignettes/growth_model.Rmd | 2 +- 13 files changed, 111 insertions(+), 79 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index c64f7f5d..60497223 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -94,7 +94,6 @@ importFrom(diagis,weighted_mean) importFrom(diagis,weighted_quantile) importFrom(diagis,weighted_se) importFrom(diagis,weighted_var) -importFrom(dplyr,across) importFrom(dplyr,as_tibble) importFrom(dplyr,group_by) importFrom(dplyr,summarise) diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 995dc6d5..27b31ed2 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -11,7 +11,6 @@ #' sense. For IS-MCMC, ESS estimates based on a weighted posterior are also #' computed. #' -#' @importFrom dplyr across #' @importFrom posterior summarise_draws default_convergence_measures #' @param x Results object of class \code{mcmc_output} from #' \code{\link{run_mcmc}}. @@ -40,8 +39,8 @@ #' phi = gamma_prior(2, 2, 1), distribution = "negative binomial", #' xreg = x, beta = normal_prior(0.5, 0, 1), u = u) #' -#' out <- run_mcmc(model, iter = 1e4, particles = 10) -#' +#' out <- run_mcmc(model, iter = 1000, particles = 10) +#' check_diagnostics(out) check_diagnostics <- function(x) { cat("\nAcceptance rate after the burn-in period: ", @@ -51,7 +50,9 @@ check_diagnostics <- function(x) { cat(paste(ifelse(x$time[3] < 10, round(x$time[3], 2), round(x$time[3])), "seconds.\n")) - + if (any(is.na(x$theta)) || any(is.na(x$alpha))) { + warning("NA value found in samples.") + } draws <- suppressWarnings(as_draws(x)) is_run <- x$mcmc_type %in% paste0("is", 1:3) diff --git a/R/models.R b/R/models.R index e7633696..407c97b1 100644 --- a/R/models.R +++ b/R/models.R @@ -101,12 +101,17 @@ default_update_fn <- function(theta) { #' # using default values, but being explicit for testing purposes #' C = matrix(0, 3, 1), D = numeric(1)) #' -#' out <- run_mcmc(model, iter = 10000) +#' out <- run_mcmc(model, iter = 5000) #' out -#' sumr <- summary(out, variable = "state") -#' ts.plot(sumr$Mean, col = 1:3) -#' lines(b1, col= 2, lty = 2) -#' lines(b2, col= 3, lty = 2) +#' sumr <- summary(out, variable = "state", times = 1:n) +#' sumr$true <- c(b1, b2, rep(1, n)) +#' library(ggplot2) +#' ggplot(sumr, aes(x = time, y = Mean)) + +#' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5) + +#' geom_line() + +#' geom_line(aes(y = true), colour = "red") + +#' facet_wrap(~ variable, scales = "free") + +#' theme_bw() #' #' # Perhaps easiest way to construct a general SSM for bssm is to use the #' # model building functionality of KFAS: @@ -608,7 +613,10 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, #' sd_slope = prior, sd_seasonal = prior, period = 4) #' +#' # Note small number of iterations for CRAN checks #' mcmc_out <- run_mcmc(model, iter = 5000) +#' summary(mcmc_out, return_se = TRUE) +#' # Use the summary method from coda: #' summary(expand_sample(mcmc_out, "theta"))$stat #' mcmc_out$theta[which.max(mcmc_out$posterior), ] #' sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] @@ -848,7 +856,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' summary(out, variable = "theta", return_se = TRUE) #' # should be about 0.093 and 0.016 #' summary(out, variable = "states", return_se = TRUE, -#' states = 1, times = c(1, 100))$Mean +#' states = 1, times = c(1, 100)) #' # should be about -0.075, 2.618 #' } #' diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 844ba13d..879dcb12 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -26,7 +26,12 @@ #' \code{variable = "theta"}. #' @param method Method for computing integrated autocorrelation time. Default #' is \code{"sokal"}, other option is \code{"geyer"}. +#' @param use_times If \code{TRUE} (default), transforms the values of the time +#' variable to match the ts attribute of the input to define. If \code{FALSE}, +#' time is based on the indexing starting from 1. #' @param ... Ignored. +#' @return If \code{variable} is \code{"theta"} or \code{"states"}, a +#' \code{data.frame} object. If \code{"both"}, a list of two data frames. #' @references #' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based #' on approximate marginal Markov chain Monte Carlo. @@ -34,19 +39,54 @@ #' @export #' @srrstats {BS5.3, BS5.5, BS6.4} summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", - probs = c(0.025, 0.975), times, states, method = "sokal", ...) { + probs = c(0.025, 0.975), times, states, use_times = TRUE, method = "sokal", + ...) { if (!test_flag(return_se)) stop("Argument 'return_se' should be TRUE or FALSE. ") - + method <- match.arg(method, c("sokal", "geyer")) variable <- match.arg(tolower(variable), c("theta", "states", "both")) + if (return_se) { + if (object$mcmc_type %in% paste0("is", 1:3)) { + summary_f <- function(x, w) { + c(Mean = weighted_mean(x, w), + SE = sqrt(asymptotic_var(x, w, method)), + SD = sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs), + ESS = round(estimate_ess(x, w, method)), + SE_IS = weighted_se(x, w), + ESS_IS = round(ess(w, identity, x))) + } + } else { + summary_f <- function(x, w) { + c(Mean = mean(x), SE = sqrt(asymptotic_var(x, method = method)), + SD = sd(x), quantile(x, probs), + ESS = round(estimate_ess(x, method = method))) + } + } + } else { + if (object$mcmc_type %in% paste0("is", 1:3)) { + summary_f <- function(x, w) { + c(Mean = weighted_mean(x, w), + SD = sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs)) + } + } else { + summary_f <- function(x, w) { + c(Mean = mean(x), + SD = sd(x), quantile(x, probs)) + } + } + } if (variable %in% c("theta", "both")) { - d <- tidyr::pivot_wider( - as.data.frame(object, variable = "theta", expand = TRUE), - values_from = .data$value, names_from = .data$variable) + sumr_theta <- + as.data.frame(object, variable = "theta", expand = TRUE) %>% + group_by(variable) %>% + summarize(as_tibble(as.list(summary_f(value, weight)))) + if (variable == "theta") return(sumr_theta) } if (variable %in% c("states", "both")) { @@ -70,37 +110,14 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", ncol(object$alpha),".")) } - d_states <- tidyr::pivot_wider( + sumr_states <- as.data.frame(object, variable = "states", expand = TRUE, - times = times, states = states, use_times = FALSE), - values_from = .data$value, - names_from = c(.data$variable, .data$time), - names_glue = "{variable}[{time}]") - if (variable == "both") { - d <- cbind(d, d_states[, -(1:2)]) - } else d <- d_states - } - - - if (object$mcmc_type %in% paste0("is", 1:3)) { - summary_f_is <- function(x, w) { - c(Mean = weighted_mean(x, w), - SE = sqrt(asymptotic_var(x, w, method)), - SD = sqrt(weighted_var(x, w)), - weighted_quantile(x, w, probs), - ESS = round(estimate_ess(x, w, method)), - SE_IS = weighted_se(x, w), - ESS_IS = round(ess(w, identity, x))) - } - as.data.frame(t(apply(d[, -(1:2)], 2, summary_f_is, w = d$weight))) - } else { - summary_f <- function(x) { - c(Mean = mean(x), SE = sqrt(asymptotic_var(x, method = method)), - SD = sd(x), quantile(x, probs), - ESS = round(estimate_ess(x, method = method))) - } - as.data.frame(t(apply(d[, -(1:2)], 2, summary_f))) + times = times, states = states, use_times = use_times) %>% + group_by(variable, time) %>% + summarize(as_tibble(as.list(summary_f(value, weight)))) + if (variable == "states") return(sumr_states) } + list(theta = sumr_theta, states = sumr_states) } #' Print Results from MCMC Run @@ -131,7 +148,7 @@ print.mcmc_output <- function(x, ...) { if (x$output_type != 3) { n <- nrow(x$alpha) cat(paste0("\nSummary for alpha_", n), ":\n\n", sep = "") - + if (is.null(x$alphahat)) { stats <- summary(x, variable = "states", times = n) print(stats) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 75e6c16c..1abf6624 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -153,17 +153,12 @@ run_mcmc <- function(model, ...) { #' mcmc_results <- run_mcmc(model, iter = 2e4) #' summary(mcmc_results, return_se = TRUE) #' -#' library("dplyr") -#' sumr <- as.data.frame(mcmc_results, variable = "states") %>% -#' group_by(time) %>% -#' summarise(mean = mean(value), -#' lwr = quantile(value, 0.025), -#' upr = quantile(value, 0.975)) +#' sumr <- summary(mcmc_results, variable = "states") #' library("ggplot2") -#' sumr %>% ggplot(aes(time, mean)) + -#' geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + +#' sumr %>% ggplot(aes(time, Mean)) + +#' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.25) + #' geom_line() + theme_bw() + -#' geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), +#' geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), #' col = 2) #' #' # Continue from the previous run @@ -307,7 +302,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' #' # run IS-MCMC #' # Note small number of iterations for CRAN checks -#' fit <- run_mcmc(model, iter = 5000, +#' fit <- run_mcmc(model, iter = 4000, #' particles = 10, mcmc_type = "is2", seed = 1) #' #' # extract states @@ -366,7 +361,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", #' prior_fn = prior_fn, update_fn = update_fn) #' #' # Note small number of iterations for CRAN checks -#' out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") +#' out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") #' #' sumr <- as.data.frame(out, variable = "states") %>% #' group_by(time) %>% mutate(value = exp(value)) %>% diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 180fed9a..03f93dc0 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -74,7 +74,10 @@ prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior, period = 4) +# Note small number of iterations for CRAN checks mcmc_out <- run_mcmc(model, iter = 5000) +summary(mcmc_out, return_se = TRUE) +# Use the summary method from coda: summary(expand_sample(mcmc_out, "theta"))$stat mcmc_out$theta[which.max(mcmc_out$posterior), ] sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 212825bb..6a134c9b 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -97,7 +97,7 @@ out <- run_mcmc(model, iter = 1e5, particles = 10) summary(out, variable = "theta", return_se = TRUE) # should be about 0.093 and 0.016 summary(out, variable = "states", return_se = TRUE, - states = 1, times = c(1, 100))$Mean + states = 1, times = c(1, 100)) # should be about -0.075, 2.618 } diff --git a/man/check_diagnostics.Rd b/man/check_diagnostics.Rd index 115da116..7b9a5be6 100644 --- a/man/check_diagnostics.Rd +++ b/man/check_diagnostics.Rd @@ -45,6 +45,6 @@ model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1), phi = gamma_prior(2, 2, 1), distribution = "negative binomial", xreg = x, beta = normal_prior(0.5, 0, 1), u = u) -out <- run_mcmc(model, iter = 1e4, particles = 10) - +out <- run_mcmc(model, iter = 1000, particles = 10) +check_diagnostics(out) } diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 514f3dc1..5d013167 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -37,8 +37,8 @@ future, using posterior samples of (theta, alpha_T+1) i.e. the posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} -\item{seed}{Seed for RNG (positive integer). Note that this affects only the -C++ side, and \code{predict} also uses R side RNG for subsampling, so for +\item{seed}{Seed for RNG (positive integer). Note that this affects only the +C++ side, and \code{predict} also uses R side RNG for subsampling, so for replicable results you should call \code{set.seed} before \code{predict}.} \item{...}{Ignored.} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 08114d9c..b301422c 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -229,17 +229,12 @@ model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), mcmc_results <- run_mcmc(model, iter = 2e4) summary(mcmc_results, return_se = TRUE) -library("dplyr") -sumr <- as.data.frame(mcmc_results, variable = "states") \%>\% - group_by(time) \%>\% - summarise(mean = mean(value), - lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) +sumr <- summary(mcmc_results, variable = "states") library("ggplot2") -sumr \%>\% ggplot(aes(time, mean)) + - geom_ribbon(aes(ymin = lwr, ymax = upr),alpha=0.25) + +sumr \%>\% ggplot(aes(time, Mean)) + + geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`), alpha = 0.25) + geom_line() + theme_bw() + - geom_point(data = data.frame(mean = LakeHuron, time = time(LakeHuron)), + geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), col = 2) # Continue from the previous run @@ -282,7 +277,7 @@ model <- bsm_ng(y, xreg = x, # run IS-MCMC # Note small number of iterations for CRAN checks -fit <- run_mcmc(model, iter = 5000, +fit <- run_mcmc(model, iter = 4000, particles = 10, mcmc_type = "is2", seed = 1) # extract states @@ -341,7 +336,7 @@ model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, prior_fn = prior_fn, update_fn = update_fn) # Note small number of iterations for CRAN checks -out <- run_mcmc(model, iter = 5000, mcmc_type = "approx") +out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") sumr <- as.data.frame(out, variable = "states") \%>\% group_by(time) \%>\% mutate(value = exp(value)) \%>\% diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index dca54938..f253195f 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -139,12 +139,17 @@ model <- ssm_ulg(y, Z, H, T, R, a1, P1, # using default values, but being explicit for testing purposes C = matrix(0, 3, 1), D = numeric(1)) -out <- run_mcmc(model, iter = 10000) +out <- run_mcmc(model, iter = 5000) out -sumr <- summary(out, variable = "state") -ts.plot(sumr$Mean, col = 1:3) -lines(b1, col= 2, lty = 2) -lines(b2, col= 3, lty = 2) +sumr <- summary(out, variable = "state", times = 1:n) +sumr$true <- c(b1, b2, rep(1, n)) +library(ggplot2) +ggplot(sumr, aes(x = time, y = Mean)) + +geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`), alpha = 0.5) + +geom_line() + +geom_line(aes(y = true), colour = "red") + +facet_wrap(~ variable, scales = "free") + +theme_bw() # Perhaps easiest way to construct a general SSM for bssm is to use the # model building functionality of KFAS: diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 2ea6c1cc..8762aced 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -11,6 +11,7 @@ probs = c(0.025, 0.975), times, states, + use_times = TRUE, method = "sokal", ... ) @@ -36,11 +37,19 @@ summaries should be computed? Default is all, ignored if computed?. Default is all, ignored if \code{variable = "theta"}.} +\item{use_times}{If \code{TRUE} (default), transforms the values of the time +variable to match the ts attribute of the input to define. If \code{FALSE}, +time is based on the indexing starting from 1.} + \item{method}{Method for computing integrated autocorrelation time. Default is \code{"sokal"}, other option is \code{"geyer"}.} \item{...}{Ignored.} } +\value{ +If \code{variable} is \code{"theta"} or \code{"states"}, a +\code{data.frame} object. If \code{"both"}, a list of two data frames. +} \description{ This functions returns a data frame containing mean, standard deviations, standard errors, and effective sample size estimates for parameters and diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index eddd8165..6ac3628c 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -156,7 +156,7 @@ summary(mcmc_is, return_se = TRUE) summary(mcmc_ekf, return_se = TRUE) ``` -Using the `as.data.frame` method we can convert the state samples to a data frame for further processing with the `dplyr` package [@dplyr]: +Using the `as.data.frame` method we can convert the state samples to a data frame for further processing with the `dplyr` package [@dplyr] (we could do this automatically with `summary` method as well): ```{r summaries} library("dplyr") library("diagis") From 6660fd6c414d8d1f5dd6e38f040c72dc918a2b57 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 22 Nov 2021 12:41:36 +0200 Subject: [PATCH 134/180] fix printing --- R/bssm-package.R | 8 ++++ R/cpp_example_models.R | 2 +- R/expand_sample.R | 4 +- R/post_correction.R | 6 ++- R/print_mcmc.R | 9 ++-- R/run_mcmc.R | 4 +- README.Rmd | 4 +- README.md | 82 ++++++++++++++++----------------- codemeta.json | 101 +++++++++++++++++++++++++++++------------ 9 files changed, 139 insertions(+), 81 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index bad5ddbe..9f07c844 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -41,6 +41,14 @@ #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm +#' @examples +#' model <- bsm_lg(Nile, +#' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), +#' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), +#' a1 = 1000, P1 = 500^2) +#' +#' fit <- run_mcmc(model, iter = 2000) +#' fit NULL #' Deaths by drowning in Finland in 1969-2019 #' diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 18522492..985a0c92 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -297,7 +297,7 @@ cpp_example_model <- function(example, return_code = FALSE) { // [[Rcpp::export]] Rcpp::List create_xptrs() { - // typedef for a pointer of nonlinear function of model equation returning vec + // typedef for a pointer of nonlinear function returning vec typedef arma::vec (*vec_fnPtr)(const unsigned int t, const arma::vec& alpha, const arma::vec& theta, const arma::vec& known_params, const arma::mat& known_tv_params); diff --git a/R/expand_sample.R b/R/expand_sample.R index 52247188..156027a8 100644 --- a/R/expand_sample.R +++ b/R/expand_sample.R @@ -8,8 +8,8 @@ #' (for example for drawing traceplots). #' Function \code{expand_sample} returns the expanded sample based on the #' counts (in form of \code{coda::mcmc} object. Note that for -#' the IS-MCMC the expanded sample corresponds to the approximate posterior i.e., -#' the weights are ignored. +#' the IS-MCMC the expanded sample corresponds to the approximate posterior, +#' i.e., the weights are ignored. #' #' This functions is mostly for backwards compatibility, methods #' \code{as.data.frame} and \code{as_draws} produce likely more convenient diff --git a/R/post_correction.R b/R/post_correction.R index d1286f3c..1ec10c53 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -1,5 +1,9 @@ #' Get MAP estimate of theta -#' @param x Object of class \code{mcmc_output} +#' @param x Object of class \code{mcmc_output} or any other list style object +#' which has matrix theta (where each row corresponds to one iteration) and +#' vector \code{posterior}, +#' @return Vector containing theta corresponding to maximum log-posterior value +#' of the posterior sample. get_map <- function(x) { x$theta[which.max(x$posterior), ] } diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 879dcb12..06340dfa 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -143,15 +143,16 @@ print.mcmc_output <- function(x, ...) { paste(round(x$acceptance_rate, 3), "\n", sep = "")) cat("\nSummary for theta:\n\n") - stats <- summary(x, variable = "theta") - print(stats) + stats <- as.data.frame(summary(x, variable = "theta", return_se = TRUE)) + print(stats, row.names = FALSE) if (x$output_type != 3) { n <- nrow(x$alpha) cat(paste0("\nSummary for alpha_", n), ":\n\n", sep = "") if (is.null(x$alphahat)) { - stats <- summary(x, variable = "states", times = n) - print(stats) + stats <- as.data.frame(summary(x, variable = "states", times = n, + return_se = TRUE)) + print(stats, row.names = FALSE) } else { if (ncol(x$alphahat) == 1) { print(cbind("Mean" = x$alphahat[n, ], "SD" = sqrt(x$Vt[, , n]))) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 1abf6624..3047d28f 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -122,8 +122,8 @@ #' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the #' input model, whereas some metadata (like the class of input model and seed) #' is returned by run_mcmc. -#' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off with -#' \code{verbose = FALSE}. +#' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off +#' with \code{verbose = FALSE}. #' @srrstats {BS2.14} No warnings are issues during MCMC. #' @rdname run_mcmc #' @references diff --git a/README.Rmd b/README.Rmd index 8db02f3d..f37c71fe 100644 --- a/README.Rmd +++ b/README.Rmd @@ -62,7 +62,7 @@ knitr::opts_chunk$set( [![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![cran version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) +[![CRAN version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) [![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) @@ -264,4 +264,4 @@ pred %>% filter(Variable == "Ozone") %>% theme_bw() ``` -See more examples in the paper, vignettes, and in the docs. \ No newline at end of file +See more examples in the paper, vignettes, and in the docs. diff --git a/README.md b/README.md index f6b7b81c..9d70b001 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.rep [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![cran +[![CRAN version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) [![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) @@ -127,22 +127,22 @@ fit #> #> Summary for theta: #> -#> Mean SE SD 2.5% 97.5% ESS -#> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 -#> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 -#> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 -#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 -#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 +#> variable Mean SE SD 2.5% 97.5% ESS +#> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 +#> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 +#> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 +#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 +#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 #> #> Summary for alpha_154: #> -#> Mean SE SD 2.5% 97.5% ESS -#> level[154] -28.3163054 0.69650977 20.132341 -69.271049 11.797133 835 -#> slope[154] -0.3740463 0.03683278 1.685733 -4.065499 2.830134 2094 +#> variable time Mean SE SD 2.5% 97.5% ESS +#> level 154 -28.3163054 0.69650977 20.132341 -69.271049 11.797133 835 +#> slope 154 -0.3740463 0.03683278 1.685733 -4.065499 2.830134 2094 #> #> Run time: #> user system elapsed -#> 0.89 0.04 0.92 +#> 0.94 0.00 0.94 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -186,31 +186,31 @@ fit2 #> #> Summary for theta: #> -#> Mean SE SD 2.5% 97.5% ESS -#> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 -#> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 -#> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 -#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 -#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 -#> SE_IS ESS_IS -#> sd_level 2.927386e-04 10591 -#> sd_slope 3.031489e-05 7766 -#> phi 4.411840e-03 14611 -#> Wind 1.263047e-04 13905 -#> Temp 7.128104e-05 14485 +#> variable Mean SE SD 2.5% 97.5% ESS +#> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 +#> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 +#> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 +#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 +#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 +#> SE_IS ESS_IS +#> 4.411840e-03 14611 +#> 2.927386e-04 10591 +#> 3.031489e-05 7766 +#> 7.128104e-05 14485 +#> 1.263047e-04 13905 #> #> Summary for alpha_154: #> -#> Mean SE SD 2.5% 97.5% ESS -#> level[154] -0.200656509 0.0201721601 0.73134471 -1.62501396 1.24522802 1314 -#> slope[154] -0.002689176 0.0005121944 0.02289051 -0.04650504 0.04724173 1997 -#> SE_IS ESS_IS -#> level[154] 0.005987284 9458 -#> slope[154] 0.000191620 6448 +#> variable time Mean SE SD 2.5% 97.5% ESS +#> level 154 -0.200656509 0.0201721601 0.73134471 -1.62501396 1.24522802 1314 +#> slope 154 -0.002689176 0.0005121944 0.02289051 -0.04650504 0.04724173 1997 +#> SE_IS ESS_IS +#> 0.005987284 9458 +#> 0.000191620 6448 #> #> Run time: #> user system elapsed -#> 11.22 0.07 11.27 +#> 10.82 0.08 10.83 ``` Comparison: @@ -304,23 +304,23 @@ fit #> #> Summary for theta: #> -#> Mean SE SD 2.5% 97.5% ESS -#> theta_1 -3.89121114 0.0233827004 0.58715113 -5.0085134 -2.6915137 631 -#> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 -#> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 -#> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 -#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 -#> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 +#> variable Mean SE SD 2.5% 97.5% ESS +#> theta_1 -3.89121114 0.0233827004 0.58715113 -5.0085134 -2.6915137 631 +#> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 +#> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 +#> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 +#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 +#> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 #> #> Summary for alpha_154: #> -#> Mean SE SD 2.5% 97.5% ESS -#> alpha[154] -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 -#> mu[154] 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 +#> variable time Mean SE SD 2.5% 97.5% ESS +#> alpha 154 -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 +#> mu 154 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 #> #> Run time: #> user system elapsed -#> 12.08 0.13 12.14 +#> 12.05 0.20 12.08 ``` Draw predictions: diff --git a/codemeta.json b/codemeta.json index 7710e40c..091bc6d7 100644 --- a/codemeta.json +++ b/codemeta.json @@ -10,7 +10,7 @@ "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "1.1.7", + "version": "2.0.0", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", @@ -63,18 +63,6 @@ }, "sameAs": "https://CRAN.R-project.org/package=covr" }, - { - "@type": "SoftwareApplication", - "identifier": "dplyr", - "name": "dplyr", - "provider": { - "@id": "https://cran.r-project.org", - "@type": "Organization", - "name": "Comprehensive R Archive Network (CRAN)", - "url": "https://cran.r-project.org" - }, - "sameAs": "https://CRAN.R-project.org/package=dplyr" - }, { "@type": "SoftwareApplication", "identifier": "ggplot2", @@ -88,18 +76,6 @@ }, "sameAs": "https://CRAN.R-project.org/package=ggplot2" }, - { - "@type": "SoftwareApplication", - "identifier": "Hmisc", - "name": "Hmisc", - "provider": { - "@id": "https://cran.r-project.org", - "@type": "Organization", - "name": "Comprehensive R Archive Network (CRAN)", - "url": "https://cran.r-project.org" - }, - "sameAs": "https://CRAN.R-project.org/package=Hmisc" - }, { "@type": "SoftwareApplication", "identifier": "KFAS", @@ -207,6 +183,18 @@ "name": "R", "version": ">= 3.5.0" }, + { + "@type": "SoftwareApplication", + "identifier": "magrittr", + "name": "magrittr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=magrittr" + }, { "@type": "SoftwareApplication", "identifier": "checkmate", @@ -244,6 +232,30 @@ }, "sameAs": "https://CRAN.R-project.org/package=diagis" }, + { + "@type": "SoftwareApplication", + "identifier": "dplyr", + "name": "dplyr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=dplyr" + }, + { + "@type": "SoftwareApplication", + "identifier": "posterior", + "name": "posterior", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=posterior" + }, { "@type": "SoftwareApplication", "identifier": "Rcpp", @@ -257,6 +269,30 @@ }, "sameAs": "https://CRAN.R-project.org/package=Rcpp" }, + { + "@type": "SoftwareApplication", + "identifier": "rlang", + "name": "rlang", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=rlang" + }, + { + "@type": "SoftwareApplication", + "identifier": "tidyr", + "name": "tidyr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=tidyr" + }, { "@type": "SoftwareApplication", "identifier": "https://sysreqs.r-hub.io/get/pandoc" @@ -268,7 +304,7 @@ ], "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS", "readme": "https://github.com/helske/bssm/blob/master/README.md", - "fileSize": "10636.929KB", + "fileSize": "1180.262KB", "contIntegration": ["https://github.com/helske/bssm/actions", "https://codecov.io/gh/helske/bssm?branch=master"], "keywords": [ "bayesian-inference", @@ -281,7 +317,7 @@ ], "citation": [ { - "@type": "CreativeWork", + "@type": "ScholarlyArticle", "datePublished": "2021", "author": [ { @@ -296,7 +332,16 @@ } ], "name": "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", - "url": "https://arxiv.org/abs/2101.08492" + "url": "https://arxiv.org/abs/2101.08492", + "description": "To appear", + "isPartOf": { + "@type": "PublicationIssue", + "datePublished": "2021", + "isPartOf": { + "@type": ["PublicationVolume", "Periodical"], + "name": "R Journal" + } + } }, { "@type": "ScholarlyArticle", From c1cee909f0ee7c6ace50f1e004ae5cd1bc8343cb Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 22 Nov 2021 12:49:15 +0200 Subject: [PATCH 135/180] summarize to summarise --- R/print_mcmc.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 06340dfa..c2589bca 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -85,7 +85,7 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sumr_theta <- as.data.frame(object, variable = "theta", expand = TRUE) %>% group_by(variable) %>% - summarize(as_tibble(as.list(summary_f(value, weight)))) + summarise(as_tibble(as.list(summary_f(value, weight)))) if (variable == "theta") return(sumr_theta) } @@ -114,7 +114,7 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", as.data.frame(object, variable = "states", expand = TRUE, times = times, states = states, use_times = use_times) %>% group_by(variable, time) %>% - summarize(as_tibble(as.list(summary_f(value, weight)))) + summarise(as_tibble(as.list(summary_f(value, weight)))) if (variable == "states") return(sumr_states) } list(theta = sumr_theta, states = sumr_states) From a5d14d2fa8291de4b8d56024136fca0ab1da07e1 Mon Sep 17 00:00:00 2001 From: helske Date: Mon, 22 Nov 2021 16:20:50 +0200 Subject: [PATCH 136/180] add precomputed negbin model --- R/bssm-package.R | 35 ++++++++++++++++++++++++++++------- data/negbin_model.rda | Bin 0 -> 1141944 bytes 2 files changed, 28 insertions(+), 7 deletions(-) create mode 100644 data/negbin_model.rda diff --git a/R/bssm-package.R b/R/bssm-package.R index 9f07c844..4001d50b 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -120,16 +120,17 @@ NULL #' y <- rpois(100, exp(cumsum(slope + c(0, rnorm(99, sd = 0.1))))) NULL #' -# Simulated Negative Binomial Time Series Data +#' Simulated Negative Binomial Time Series Data #' #' See example for code for reproducing the data. This was used in #' Helske and Vihola (2021). #' -#' @srrstats {G5.0, G5.1, G5.4, BS7.2} used in Helske and Vihola (2021). + #' @srrstats {G5.1} used in Helske and Vihola (2021). #' @name negbin_series #' @docType data #' @format A time series \code{mts} object with 200 time points and two series. #' @keywords datasets +#' @seealso \code{negbin_model} #' @references #' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and #' Non-Gaussian State Space Models in R. R Journal (to appear). @@ -148,10 +149,29 @@ NULL #' x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) #' y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) #' +NULL +#' Estimated Negative Binomial Model of Helske and Vihola (2021) +#' +#' This model was used in Helske and Vihola (2021), but with larger number of +#' iterations. Here only 2000 iterations were used in order to reduce the size +#' of the model object in CRAN. +#' +#' @srrstats {G5.0, G5.1, G5.4, BS7.2} used in Helske and Vihola (2021). +#' @name negbin_model +#' @docType data +#' @format A object of class \code{mcmc_output}. +#' @keywords datasets +#' @references +#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. R Journal (to appear). +#' https://arxiv.org/abs/2101.08492 #' +#' @examples +#' # reproducing the model: +#' data("negbin_series") #' # Construct model for bssm -#' bssm_model <- bsm_ng(y, -#' xreg = x, +#' bssm_model <- bsm_ng(negbin_series[, "y"], +#' xreg = negbin_series[, "x"], #' beta = normal(0, 0, 10), #' phi = halfnormal(1, 10), #' sd_level = halfnormal(0.1, 1), @@ -159,8 +179,9 @@ NULL #' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), #' distribution = "negative binomial") #' -#' # run the MCMC, small number of iterations for CRAN -#' fit_bssm <- run_mcmc(bssm_model, iter = 2000, burnin = 1000, -#' particles = 10) +#' \dontest{ +#' # In the paper we used 60000 iterations with first 10000 as burnin +#' fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1) #' fit_bssm +#' } NULL diff --git a/data/negbin_model.rda b/data/negbin_model.rda new file mode 100644 index 0000000000000000000000000000000000000000..44971af09ff9723f27b2732bafdb97126f05b2a0 GIT binary patch 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zBkhIKYc=n=)*Tfi2KySS5}B7;@u}H;;B)pir|w!<fRrsoQctM-KFR5;kKPeZ?q*9h zx9wz5K861O7<{*kW%>%6w=xDCu6qxj&da}e3eVA@HL-UdYIMnrc@5`%ynIIRA$~wy ze$9g1W#qkA!)c;sR24Aj{Nl1V#y+buOhc#0SP-`@?v|xxdY-@a?Tpn7#c{Go)+uZS z`FWJK$z$O0C{$=T9SQ^a(_n8oMx#o<juuRu#x(E`zsda(>fLTVDxpQNum*SIXrdOy z;=}VjS*pYPrPhA|ROLMvj>;gs9k!OJUb9gk=*~1f&2RdSN8g=g>9gyoh22A;YV1Dw z@Gej5s-S}RZhs^o3}ajD5**r2st991SSCS3fjAc~03om-7{G++ql(f%y_3pjQtKyr zxCmW;YkX*S3Vao-gBgJ|C38Zfz-)S*)|d$b88~{|-g!3y019e98aH1RY>`|bJ<pgN zBGRsuZH!8eDvIQ*Cq6%kdZDUmqJ&qP)xE>isz_lFk>^>m1E8Jui+f58(C`o^bpZPy zARqhYSOK4MT+!%|*QvK!;Jvz)pD@~dK8KAo9aipsk8UkEgg<mX^3Qg~>i%7^+ON#+ zIQj}o*f5QePCBj8TIJq~%3Uu}iIYA-zybveO(OlvzrrY`a1Z{U+if+K5f1oj@bjDz zO94ZIL*XCha0r*a_O&;cc-T!9><pn{eR4Mm&dq?Ft6~zL6JJoABmPt|&JghMQ-`NF j30tH{P|)A0x)rP&L7@*EXk2cg-~3(46yZWbb`O5cS?DUB literal 0 HcmV?d00001 From 3e962f7059b96e80179c60802aadbf201a8f3dda Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 22 Nov 2021 16:21:40 +0200 Subject: [PATCH 137/180] implement IACT in C++ --- R/RcppExports.R | 4 ++ R/asymptotic_var.R | 13 +---- R/check_arguments.R | 49 ++++++++-------- R/models.R | 3 +- R/post_correction.R | 1 + R/print_mcmc.R | 7 +++ man/bssm.Rd | 9 +++ man/check.Rd | 114 ------------------------------------- man/expand_sample.Rd | 4 +- man/get_map.Rd | 14 ----- man/negbin_model.Rd | 39 +++++++++++++ man/negbin_series.Rd | 21 ++----- man/print.mcmc_output.Rd | 4 ++ man/ssm_ulg.Rd | 3 +- man/summary.mcmc_output.Rd | 5 ++ src/R_iact.cpp | 15 +++++ src/RcppExports.cpp | 12 ++++ 17 files changed, 133 insertions(+), 184 deletions(-) delete mode 100644 man/check.Rd delete mode 100644 man/get_map.Rd create mode 100644 man/negbin_model.Rd create mode 100644 src/R_iact.cpp diff --git a/R/RcppExports.R b/R/RcppExports.R index 047ab420..77cdc613 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -45,6 +45,10 @@ ekpf_smoother <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, k .Call('_bssm_ekpf_smoother', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) } +IACT <- function(x) { + .Call('_bssm_IACT', PACKAGE = 'bssm', x) +} + importance_sample_ng <- function(model_, nsim, use_antithetic, seed, model_type) { .Call('_bssm_importance_sample_ng', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type) } diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 2c248d62..1c46c76d 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -22,16 +22,9 @@ #' # ESS estimate: #' length(x) / iact(x) iact <- function(x) { - n <- length(x) - x_ <- (x - mean(x)) / sd(x) - C <- max(5, log10(n)) - tau <- 1 - for (k in 1:(n - 1)) { - tau <- tau + 2.0 * (x_[1:(n - k)] %*% x_[(1 + k):n]) / (n - k) - if (k > C * tau) break - } - max(0, tau) + IACT((x - mean(x)) / sd(x)) } + #' Asymptotic Variance of IS-type Estimators #' #' The asymptotic variance MCMCSE^2 is based on Corollary 1 @@ -92,7 +85,7 @@ asymptotic_var <- function(x, w, method = "sokal") { switch(method, sokal = (var(z) * iact(z) / estimate_c^2) / length(z), # ESS(z) = n / IACT(z) - ess_basic = var(z) / ess_mean(z) / estimate_c^2) + geyer = var(z) / ess_mean(z) / estimate_c^2) } #' Effective Sample Size for IS-type Estimators diff --git a/R/check_arguments.R b/R/check_arguments.R index 5486262f..a6a0a7d0 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -18,7 +18,7 @@ #' @param y The response time series. #' @param type Name to be added to the sd parameter name. #' @param add_prefix Logical, add \code{type} to parameter name. -#' @rdname check +#' @noRd check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { if (any(!is.na(x))) { if (multivariate) { @@ -63,7 +63,7 @@ check_y <- function(x, multivariate = FALSE, distribution = "gaussian") { } x } -#' @rdname check + check_period <- function(x, n) { if (!test_int(x)) { stop("Argument 'period' should be a single integer. ") @@ -79,7 +79,6 @@ check_period <- function(x, n) { } #' @srrstats {BS2.5} Checks that observations are compatible with their #' distributions are made. -#' @rdname check check_distribution <- function(x, distribution) { for (i in seq_len(ncol(x))) { if (distribution[i] != "gaussian" && any(na.omit(x[, i]) < 0)) { @@ -97,7 +96,7 @@ check_distribution <- function(x, distribution) { } -#' @rdname check + check_sd <- function(x, type, add_prefix = TRUE) { if (add_prefix) { @@ -120,7 +119,7 @@ check_sd <- function(x, type, add_prefix = TRUE) { } } -#' @rdname check + check_xreg <- function(x, n) { if (!(nrow(x) %in% c(0, n))) { @@ -132,7 +131,7 @@ check_xreg <- function(x, n) { } -#' @rdname check + check_beta <- function(x, k) { if(!is.numeric(x)) stop("'beta' must be numeric. ") if (length(x) != k) { @@ -144,7 +143,7 @@ check_beta <- function(x, k) { } } -#' @rdname check + check_mu <- function(x) { if (length(x) != 1) { @@ -155,7 +154,7 @@ check_mu <- function(x) { } } -#' @rdname check + check_rho <- function(x) { if (length(x) != 1) { @@ -167,13 +166,13 @@ check_rho <- function(x) { } -#' @rdname check + check_phi <- function(x) { if (x < 0) { stop("Parameter 'phi' must be non-negative.") } } -#' @rdname check + check_u <- function(x, y, multivariate = FALSE) { if (any(x < 0)) { stop("All values of 'u' must be non-negative.") @@ -200,19 +199,19 @@ check_u <- function(x, y, multivariate = FALSE) { } x } -#' @rdname check + check_prior <- function(x, name) { if (!is_prior(x) && !is_prior_list(x)) { stop(paste(name, "must be of class 'bssm_prior' or 'bssm_prior_list'.")) } } -#' @rdname check + check_prop <- function(x, name = "target") { if (length(x) > 1 || x >= 1 || x <= 0) { stop(paste0("Argument '", name, "' must be on interval (0, 1).")) } } -#' @rdname check + check_D <- function(x, p, n) { if (missing(x) || is.null(x)) { x <- if (p == 1) 0 else matrix(0, p, 1) @@ -233,7 +232,7 @@ check_D <- function(x, p, n) { } x } -#' @rdname check + check_C <- function(x, m, n) { if (missing(x) || is.null(x)) { x <- matrix(0, m, 1) @@ -250,7 +249,7 @@ check_C <- function(x, m, n) { -#' @rdname check + create_regression <- function(beta, xreg, n) { if (missing(xreg) || is.null(xreg)) { list(xreg = matrix(0, 0, 0), coefs = numeric(0), beta = NULL) @@ -287,7 +286,7 @@ create_regression <- function(beta, xreg, n) { list(xreg = xreg, coefs = coefs, beta = beta) } } -#' @rdname check + check_Z <- function(x, p, n, multivariate = FALSE) { if(!is.numeric(x)) stop("'Z' must be numeric. ") if (!multivariate) { @@ -318,7 +317,7 @@ check_Z <- function(x, p, n, multivariate = FALSE) { } x } -#' @rdname check + check_T <- function(x, m, n) { if(!is.numeric(x)) stop("'T' must be numeric. ") if (length(x) == 1 && m == 1) { @@ -333,7 +332,7 @@ check_T <- function(x, m, n) { } x } -#' @rdname check + check_R <- function(x, m, n) { if (length(x) == m) { dim(x) <- c(m, 1, 1) @@ -350,7 +349,7 @@ check_R <- function(x, m, n) { } x } -#' @rdname check + check_a1 <- function(x, m) { if (missing(x) || is.null(x)) { x <- numeric(m) @@ -366,7 +365,7 @@ check_a1 <- function(x, m) { x } -#' @rdname check + check_P1 <- function(x, m) { if (missing(x) || is.null(x)) { x <- matrix(0, m, m) @@ -383,7 +382,7 @@ check_P1 <- function(x, m) { x } -#' @rdname check + check_H <- function(x, p, n, multivariate = FALSE) { if(!is.numeric(x)) stop("'H' must be numeric. ") @@ -404,7 +403,7 @@ check_H <- function(x, p, n, multivariate = FALSE) { x } -#' @rdname check + check_intmax <- function(x, name = "particles", positive = TRUE, max = 1e7) { if (!test_count(x, positive)) { stop(paste0("Argument '", name, "' should be a ", @@ -416,14 +415,14 @@ check_intmax <- function(x, name = "particles", positive = TRUE, max = 1e7) { } as.integer(x) } -#' @rdname check + check_positive_real <- function(x, name) { if (!test_double(x, lower=0, finite = TRUE, any.missing = FALSE, len = 1)) { stop(paste0("Argument '", name, "' should be positive real value.")) } x } -#' @rdname check + check_theta <- function(x) { if (!is.numeric(x) || !test_atomic_vector(x)) { @@ -434,7 +433,7 @@ check_theta <- function(x) { } x } -#' @rdname check + check_missingness <- function(x) { if (!inherits(x, c("ssm_nlg", "ssm_sde"))) { if (is.null(x$prior_parameters)) { diff --git a/R/models.R b/R/models.R index 407c97b1..35dcbfc9 100644 --- a/R/models.R +++ b/R/models.R @@ -159,7 +159,8 @@ default_update_fn <- function(theta) { #' # Above the regression coefficients are modelled as #' # time-invariant latent states. #' # Here is an alternative way where we use variable D so that the -#' # coefficients are part of parameter vector theta: +#' # coefficients are part of parameter vector theta. Note however that the +#' # first option often preferable in order to keep the dimension of theta low. #' #' updatefn2 <- function(theta) { #' # note no PetrolPrice or law variables here diff --git a/R/post_correction.R b/R/post_correction.R index 1ec10c53..9dc445b2 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -4,6 +4,7 @@ #' vector \code{posterior}, #' @return Vector containing theta corresponding to maximum log-posterior value #' of the posterior sample. +#' @noRd get_map <- function(x) { x$theta[which.max(x$posterior), ] } diff --git a/R/print_mcmc.R b/R/print_mcmc.R index c2589bca..d0513f59 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -38,6 +38,10 @@ #' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 #' @export #' @srrstats {BS5.3, BS5.5, BS6.4} +#' @examples +#' data("negbin_model") +#' summary(negbin_model, return_se = TRUE, method = "geyer") +#' summary(negbin_model, times = c(1, 200), prob = c(0.05, 0.5, 0.95)) summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", probs = c(0.025, 0.975), times, states, use_times = TRUE, method = "sokal", ...) { @@ -131,6 +135,9 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", #' @param ... Ignored. #' @srrstats {BS5.3, BS5.5, BS6.0} #' @export +#' @examples +#' data("negbin_model") +#' print(negbin_model) print.mcmc_output <- function(x, ...) { cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), diff --git a/man/bssm.Rd b/man/bssm.Rd index b068947e..db9d683d 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -29,6 +29,15 @@ package as well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020), and the package vignettes. } +\examples{ +model <- bsm_lg(Nile, + sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), + sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), + a1 = 1000, P1 = 500^2) + +fit <- run_mcmc(model, iter = 2000) +fit +} \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. R Journal (to appear). diff --git a/man/check.Rd b/man/check.Rd deleted file mode 100644 index 9eef2acb..00000000 --- a/man/check.Rd +++ /dev/null @@ -1,114 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/check_arguments.R -\name{check_y} -\alias{check_y} -\alias{check_period} -\alias{check_distribution} -\alias{check_sd} -\alias{check_xreg} -\alias{check_beta} -\alias{check_mu} -\alias{check_rho} -\alias{check_phi} -\alias{check_u} -\alias{check_prior} -\alias{check_prop} -\alias{check_D} -\alias{check_C} -\alias{create_regression} -\alias{check_Z} -\alias{check_T} -\alias{check_R} -\alias{check_a1} -\alias{check_P1} -\alias{check_H} -\alias{check_intmax} -\alias{check_positive_real} -\alias{check_theta} -\alias{check_missingness} -\title{Check Arguments} -\usage{ -check_y(x, multivariate = FALSE, distribution = "gaussian") - -check_period(x, n) - -check_distribution(x, distribution) - -check_sd(x, type, add_prefix = TRUE) - -check_xreg(x, n) - -check_beta(x, k) - -check_mu(x) - -check_rho(x) - -check_phi(x) - -check_u(x, y, multivariate = FALSE) - -check_prior(x, name) - -check_prop(x, name = "target") - -check_D(x, p, n) - -check_C(x, m, n) - -create_regression(beta, xreg, n) - -check_Z(x, p, n, multivariate = FALSE) - -check_T(x, m, n) - -check_R(x, m, n) - -check_a1(x, m) - -check_P1(x, m) - -check_H(x, p, n, multivariate = FALSE) - -check_intmax(x, name = "particles", positive = TRUE, max = 1e+07) - -check_positive_real(x, name) - -check_theta(x) - -check_missingness(x) -} -\arguments{ -\item{x}{Variable to be checked.} - -\item{multivariate}{Logical, should \code{p} be larger than 1?} - -\item{distribution}{Distribution(s) of the responses.} - -\item{n}{Integer, number of time points.} - -\item{type}{Name to be added to the sd parameter name.} - -\item{add_prefix}{Logical, add \code{type} to parameter name.} - -\item{k}{Integer, number of predictors.} - -\item{y}{The response time series.} - -\item{name}{Name of the argument used in printing error messages.} - -\item{p}{Integer, number of time series.} - -\item{m}{Integer, dimensionality of the state vector.} - -\item{beta}{Vector of regression coefficients.} - -\item{xreg}{Matrix or vector of predictors.} - -\item{positive}{Logical, check for positiveness of \code{x}.} - -\item{max}{Maximum value of \code{x}.} -} -\description{ -Check Arguments -} diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index 7ee7b2f2..329e1878 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -31,8 +31,8 @@ IS-corrected MCMC, sometimes we want to have the usual sample paths (for example for drawing traceplots). Function \code{expand_sample} returns the expanded sample based on the counts (in form of \code{coda::mcmc} object. Note that for -the IS-MCMC the expanded sample corresponds to the approximate posterior i.e., -the weights are ignored. +the IS-MCMC the expanded sample corresponds to the approximate posterior, +i.e., the weights are ignored. } \details{ This functions is mostly for backwards compatibility, methods diff --git a/man/get_map.Rd b/man/get_map.Rd deleted file mode 100644 index 060444f7..00000000 --- a/man/get_map.Rd +++ /dev/null @@ -1,14 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/post_correction.R -\name{get_map} -\alias{get_map} -\title{Get MAP estimate of theta} -\usage{ -get_map(x) -} -\arguments{ -\item{x}{Object of class \code{mcmc_output}} -} -\description{ -Get MAP estimate of theta -} diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd new file mode 100644 index 00000000..ac55ec21 --- /dev/null +++ b/man/negbin_model.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bssm-package.R +\docType{data} +\name{negbin_model} +\alias{negbin_model} +\title{Estimated Negative Binomial Model of Helske and Vihola (2021)} +\format{ +A object of class \code{mcmc_output}. +} +\description{ +This model was used in Helske and Vihola (2021), but with larger number of +iterations. Here only 2000 iterations were used in order to reduce the size +of the model object in CRAN. +} +\examples{ +# reproducing the model: +data("negbin_series") +# Construct model for bssm +bssm_model <- bsm_ng(negbin_series[, "y"], + xreg = negbin_series[, "x"], + beta = normal(0, 0, 10), + phi = halfnormal(1, 10), + sd_level = halfnormal(0.1, 1), + sd_slope = halfnormal(0.01, 0.1), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + distribution = "negative binomial") + +\dontest{ +# In the paper we used 60000 iterations with first 10000 as burnin +fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1) +fit_bssm +} +} +\references{ +Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. R Journal (to appear). +https://arxiv.org/abs/2101.08492 +} +\keyword{datasets} diff --git a/man/negbin_series.Rd b/man/negbin_series.Rd index 5647e3c9..0a1fad79 100644 --- a/man/negbin_series.Rd +++ b/man/negbin_series.Rd @@ -3,8 +3,7 @@ \docType{data} \name{negbin_series} \alias{negbin_series} -\title{See example for code for reproducing the data. This was used in -Helske and Vihola (2021).} +\title{Simulated Negative Binomial Time Series Data} \format{ A time series \code{mts} object with 200 time points and two series. } @@ -25,25 +24,13 @@ level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) - -# Construct model for bssm -bssm_model <- bsm_ng(y, - xreg = x, - beta = normal(0, 0, 10), - phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), - sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), - distribution = "negative binomial") - -# run the MCMC, small number of iterations for CRAN -fit_bssm <- run_mcmc(bssm_model, iter = 2000, burnin = 1000, - particles = 10) -fit_bssm } \references{ Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. R Journal (to appear). https://arxiv.org/abs/2101.08492 } +\seealso{ +\code{negbin_model} +} \keyword{datasets} diff --git a/man/print.mcmc_output.Rd b/man/print.mcmc_output.Rd index d7d6bb24..be8b2c62 100644 --- a/man/print.mcmc_output.Rd +++ b/man/print.mcmc_output.Rd @@ -14,3 +14,7 @@ \description{ Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. } +\examples{ +data("negbin_model") +print(negbin_model) +} diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index f253195f..17b673f4 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -197,7 +197,8 @@ out # Above the regression coefficients are modelled as # time-invariant latent states. # Here is an alternative way where we use variable D so that the -# coefficients are part of parameter vector theta: +# coefficients are part of parameter vector theta. Note however that the +# first option often preferable in order to keep the dimension of theta low. updatefn2 <- function(theta) { # note no PetrolPrice or law variables here diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 8762aced..29fdea53 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -61,6 +61,11 @@ SE-IS can be regarded as the square root of independent IS variance, whereas SE corresponds to the square root of total asymptotic variance (see Remark 3 of Vihola et al. (2020)). } +\examples{ +data("negbin_model") +summary(negbin_model, return_se = TRUE, method = "geyer") +summary(negbin_model, times = c(1, 200), prob = c(0.05, 0.5, 0.95)) +} \references{ Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. diff --git a/src/R_iact.cpp b/src/R_iact.cpp new file mode 100644 index 00000000..17fd70b5 --- /dev/null +++ b/src/R_iact.cpp @@ -0,0 +1,15 @@ +#include <RcppArmadillo.h> +// [[Rcpp::depends(RcppArmadillo)]] +// [[Rcpp::export]] +double IACT(const arma::vec x) { + + unsigned int n = x.n_elem; + double C = std::max(5.0, std::log10(n)); + double tau = 1.0; + + for (unsigned int k = 1; k < n; k++) { + tau += 2.0 * arma::dot(x.subvec(0, n - k - 1), x.subvec(k, n - 1)) / (n - k); + if (k > C * tau) break; + } + return std::max(0.0, tau); +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index e51693b4..a30b7e2f 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -275,6 +275,17 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// IACT +double IACT(const arma::vec x); +RcppExport SEXP _bssm_IACT(SEXP xSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::vec >::type x(xSEXP); + rcpp_result_gen = Rcpp::wrap(IACT(x)); + return rcpp_result_gen; +END_RCPP +} // importance_sample_ng Rcpp::List importance_sample_ng(const Rcpp::List model_, unsigned int nsim, bool use_antithetic, const unsigned int seed, const int model_type); RcppExport SEXP _bssm_importance_sample_ng(SEXP model_SEXP, SEXP nsimSEXP, SEXP use_antitheticSEXP, SEXP seedSEXP, SEXP model_typeSEXP) { @@ -1285,6 +1296,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bssm_ekf_fast_smoother_nlg", (DL_FUNC) &_bssm_ekf_fast_smoother_nlg, 17}, {"_bssm_ekpf", (DL_FUNC) &_bssm_ekpf, 18}, {"_bssm_ekpf_smoother", (DL_FUNC) &_bssm_ekpf_smoother, 18}, + {"_bssm_IACT", (DL_FUNC) &_bssm_IACT, 1}, {"_bssm_importance_sample_ng", (DL_FUNC) &_bssm_importance_sample_ng, 5}, {"_bssm_gaussian_kfilter", (DL_FUNC) &_bssm_gaussian_kfilter, 2}, {"_bssm_gaussian_loglik", (DL_FUNC) &_bssm_gaussian_loglik, 2}, From c69b1e9dc6b740272e423b9d918ab8e5e53379c7 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 22 Nov 2021 17:21:30 +0200 Subject: [PATCH 138/180] fix tests and typo --- R/summary.R | 125 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 R/summary.R diff --git a/R/summary.R b/R/summary.R new file mode 100644 index 00000000..2560c5de --- /dev/null +++ b/R/summary.R @@ -0,0 +1,125 @@ +#' Summary Statistics of Posterior Samples +#' +#' This functions returns a data frame containing mean, standard deviations, +#' standard errors, and effective sample size estimates for parameters and +#' states. +#' +#' For IS-MCMC two types of standard errors are reported. +#' SE-IS can be regarded as the square root of independent IS variance, +#' whereas SE corresponds to the square root of total asymptotic variance +#' (see Remark 3 of Vihola et al. (2020)). +#' +#' @importFrom rlang .data +#' @param object Output from \code{run_mcmc} +#' @param variable Are the summary statistics computed for either +#' \code{"theta"} (default), \code{"states"}, or \code{"both"}? +#' @param return_se if \code{FALSE} (default), computation of standard +#' errors and effective sample sizes is omitted (as they can take considerable +#' time for models with large number of states and time points). +#' @param probs Numeric vector defining the quantiles of interest. Default is +#' \code{c(0.025, 0.975)}. +#' @param times Vector of indices. For states, for what time points the +#' summaries should be computed? Default is all, ignored if +#' \code{variable = "theta"}. +#' @param states Vector of indices. For what states the summaries should be +#' computed?. Default is all, ignored if +#' \code{variable = "theta"}. +#' @param method Method for computing integrated autocorrelation time. Default +#' is \code{"sokal"}, other option is \code{"geyer"}. +#' @param use_times If \code{TRUE} (default), transforms the values of the time +#' variable to match the ts attribute of the input to define. If \code{FALSE}, +#' time is based on the indexing starting from 1. +#' @param ... Ignored. +#' @return If \code{variable} is \code{"theta"} or \code{"states"}, a +#' \code{data.frame} object. If \code{"both"}, a list of two data frames. +#' @references +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. +#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 +#' @export +#' @srrstats {BS5.3, BS5.5, BS6.4} +#' @examples +#' data("negbin_model") +#' summary(negbin_model, return_se = TRUE, method = "geyer") +#' summary(negbin_model, times = c(1, 200), prob = c(0.05, 0.5, 0.95)) +summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", + probs = c(0.025, 0.975), times, states, use_times = TRUE, method = "sokal", + ...) { + + if (!test_flag(return_se)) + stop("Argument 'return_se' should be TRUE or FALSE. ") + + method <- match.arg(method, c("sokal", "geyer")) + + variable <- match.arg(tolower(variable), c("theta", "states", "both")) + + if (return_se) { + if (object$mcmc_type %in% paste0("is", 1:3)) { + summary_f <- function(x, w) { + c(Mean = weighted_mean(x, w), + SE = sqrt(asymptotic_var(x, w, method)), + SD = sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs), + ESS = round(estimate_ess(x, w, method)), + SE_IS = weighted_se(x, w), + ESS_IS = round(ess(w, identity, x))) + } + } else { + summary_f <- function(x, w) { + c(Mean = mean(x), SE = sqrt(asymptotic_var(x, method = method)), + SD = sd(x), quantile(x, probs), + ESS = round(estimate_ess(x, method = method))) + } + } + } else { + if (object$mcmc_type %in% paste0("is", 1:3)) { + summary_f <- function(x, w) { + c(Mean = weighted_mean(x, w), + SD = sqrt(weighted_var(x, w)), + weighted_quantile(x, w, probs)) + } + } else { + summary_f <- function(x, w) { + c(Mean = mean(x), + SD = sd(x), quantile(x, probs)) + } + } + } + if (variable %in% c("theta", "both")) { + sumr_theta <- + as.data.frame(object, variable = "theta", expand = TRUE) %>% + group_by(.data$variable) %>% + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) + if (variable == "theta") return(sumr_theta) + } + + if (variable %in% c("states", "both")) { + if (object$output_type != 1) + stop("Cannot return summary of states as the MCMC type was not 'full'. ") + + if (missing(times)) { + times <- seq_len(nrow(object$alpha)) + } else { + if (!test_integerish(times, lower = 1, upper = nrow(object$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'times' should contain indices between 1 and ", + nrow(object$alpha),".")) + } + if (missing(states)) { + states <- seq_len(ncol(object$alpha)) + } else { + if (!test_integerish(states, lower = 1, upper = ncol(object$alpha), + any.missing = FALSE, unique = TRUE)) + stop(paste0("Argument 'states' should contain indices between 1 and ", + ncol(object$alpha),".")) + } + + sumr_states <- + as.data.frame(object, variable = "states", expand = TRUE, + times = times, states = states, use_times = use_times) %>% + group_by(.data$variable, .data$time) %>% + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) + if (variable == "states") return(sumr_states) + } + list(theta = sumr_theta, states = sumr_states) +} From 197b98d53bcba5d06bcf4ec87051c5c774c4cd2f Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 22 Nov 2021 17:39:51 +0200 Subject: [PATCH 139/180] fixed standards tags --- R/bssm-package.R | 2 +- R/models.R | 2 +- R/print_mcmc.R | 126 ------------------------------------- R/priors.R | 4 +- R/run_mcmc.R | 1 + README.Rmd | 19 +++--- README.md | 8 +-- man/negbin_model.Rd | 2 +- man/summary.mcmc_output.Rd | 2 +- tests/testthat/test_mcmc.R | 8 +-- vignettes/bssm.Rmd | 16 +---- 11 files changed, 27 insertions(+), 163 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index 4001d50b..1487d2a6 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -179,7 +179,7 @@ NULL #' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), #' distribution = "negative binomial") #' -#' \dontest{ +#' \donttest{ #' # In the paper we used 60000 iterations with first 10000 as burnin #' fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1) #' fit_bssm diff --git a/R/models.R b/R/models.R index 35dcbfc9..72148650 100644 --- a/R/models.R +++ b/R/models.R @@ -4,7 +4,7 @@ #' @srrstats {G2.14, G2.14a, G2.14b, G2.14c, BS3.0} Missing observations are #' handled automatically as per SSM theory, whereas missing values are not #' allowed elsewhere. -#' @srrstats {BS1.0, BS1.1, BS1.2} +#' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2c} Examples and definitions of priors. NULL ## placeholder functions for fixed models diff --git a/R/print_mcmc.R b/R/print_mcmc.R index d0513f59..0eb0e404 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -1,129 +1,3 @@ -#' Summary Statistics of Posterior Samples -#' -#' This functions returns a data frame containing mean, standard deviations, -#' standard errors, and effective sample size estimates for parameters and -#' states. -#' -#' For IS-MCMC two types of standard errors are reported. -#' SE-IS can be regarded as the square root of independent IS variance, -#' whereas SE corresponds to the square root of total asymptotic variance -#' (see Remark 3 of Vihola et al. (2020)). -#' -#' @importFrom rlang .data -#' @param object Output from \code{run_mcmc} -#' @param variable Are the summary statistics computed for either -#' \code{"theta"} (default), \code{"states"}, or \code{"both"}? -#' @param return_se if \code{FALSE} (default), computation of standard -#' errors and effective sample sizes is omitted (as they can take considerable -#' time for models with large number of states and time points). -#' @param probs Numeric vector defining the quantiles of interest. Default is -#' \code{c(0.025, 0.975)}. -#' @param times Vector of indices. For states, for what time points the -#' summaries should be computed? Default is all, ignored if -#' \code{variable = "theta"}. -#' @param states Vector of indices. For what states the summaries should be -#' computed?. Default is all, ignored if -#' \code{variable = "theta"}. -#' @param method Method for computing integrated autocorrelation time. Default -#' is \code{"sokal"}, other option is \code{"geyer"}. -#' @param use_times If \code{TRUE} (default), transforms the values of the time -#' variable to match the ts attribute of the input to define. If \code{FALSE}, -#' time is based on the indexing starting from 1. -#' @param ... Ignored. -#' @return If \code{variable} is \code{"theta"} or \code{"states"}, a -#' \code{data.frame} object. If \code{"both"}, a list of two data frames. -#' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. -#' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 -#' @export -#' @srrstats {BS5.3, BS5.5, BS6.4} -#' @examples -#' data("negbin_model") -#' summary(negbin_model, return_se = TRUE, method = "geyer") -#' summary(negbin_model, times = c(1, 200), prob = c(0.05, 0.5, 0.95)) -summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", - probs = c(0.025, 0.975), times, states, use_times = TRUE, method = "sokal", - ...) { - - if (!test_flag(return_se)) - stop("Argument 'return_se' should be TRUE or FALSE. ") - - method <- match.arg(method, c("sokal", "geyer")) - - variable <- match.arg(tolower(variable), c("theta", "states", "both")) - - if (return_se) { - if (object$mcmc_type %in% paste0("is", 1:3)) { - summary_f <- function(x, w) { - c(Mean = weighted_mean(x, w), - SE = sqrt(asymptotic_var(x, w, method)), - SD = sqrt(weighted_var(x, w)), - weighted_quantile(x, w, probs), - ESS = round(estimate_ess(x, w, method)), - SE_IS = weighted_se(x, w), - ESS_IS = round(ess(w, identity, x))) - } - } else { - summary_f <- function(x, w) { - c(Mean = mean(x), SE = sqrt(asymptotic_var(x, method = method)), - SD = sd(x), quantile(x, probs), - ESS = round(estimate_ess(x, method = method))) - } - } - } else { - if (object$mcmc_type %in% paste0("is", 1:3)) { - summary_f <- function(x, w) { - c(Mean = weighted_mean(x, w), - SD = sqrt(weighted_var(x, w)), - weighted_quantile(x, w, probs)) - } - } else { - summary_f <- function(x, w) { - c(Mean = mean(x), - SD = sd(x), quantile(x, probs)) - } - } - } - if (variable %in% c("theta", "both")) { - sumr_theta <- - as.data.frame(object, variable = "theta", expand = TRUE) %>% - group_by(variable) %>% - summarise(as_tibble(as.list(summary_f(value, weight)))) - if (variable == "theta") return(sumr_theta) - } - - if (variable %in% c("states", "both")) { - if (object$output_type != 1) - stop("Cannot return summary of states as the MCMC type was not 'full'. ") - - if (missing(times)) { - times <- seq_len(nrow(object$alpha)) - } else { - if (!test_integerish(times, lower = 1, upper = nrow(object$alpha), - any.missing = FALSE, unique = TRUE)) - stop(paste0("Argument 'times' should contain indices between 1 and ", - nrow(object$alpha),".")) - } - if (missing(states)) { - states <- seq_len(ncol(object$alpha)) - } else { - if (!test_integerish(states, lower = 1, upper = ncol(object$alpha), - any.missing = FALSE, unique = TRUE)) - stop(paste0("Argument 'states' should contain indices between 1 and ", - ncol(object$alpha),".")) - } - - sumr_states <- - as.data.frame(object, variable = "states", expand = TRUE, - times = times, states = states, use_times = use_times) %>% - group_by(variable, time) %>% - summarise(as_tibble(as.list(summary_f(value, weight)))) - if (variable == "states") return(sumr_states) - } - list(theta = sumr_theta, states = sumr_states) -} - #' Print Results from MCMC Run #' #' Prints some basic summaries from the MCMC run by \code{\link{run_mcmc}}. diff --git a/R/priors.R b/R/priors.R index 1f76937f..3ce0d136 100644 --- a/R/priors.R +++ b/R/priors.R @@ -28,8 +28,8 @@ #' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case #' of multiple priors (i.e. multiple regression coefficients). #' @export -#' @srrstats {BS2.2, BS2.3, BS2.4, BS2.6, BS2.7} Explains prior definitions and -#' initial values. +#' @srrstats {BS1.2c, BS2.2, BS2.3, BS2.4, BS2.6, BS2.7} Explains prior +#' definitions and initial values. #' @srrstats {BS2.5} Checks are in place for the distributional parameters of #' priors and their initial values. #' @examples diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 3047d28f..427f12b4 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -124,6 +124,7 @@ #' is returned by run_mcmc. #' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off #' with \code{verbose = FALSE}. +#' @srrstats {BS1.2c} Examples on defining priors. #' @srrstats {BS2.14} No warnings are issues during MCMC. #' @rdname run_mcmc #' @references diff --git a/README.Rmd b/README.Rmd index f37c71fe..7bbcdd43 100644 --- a/README.Rmd +++ b/README.Rmd @@ -23,7 +23,7 @@ knitr::opts_chunk$set( #' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower are used where #' applicable. #' @srrstats {G1.0, G1.1, G1.3, G1.4, G1.4a, G1.5, G1.6} General -#' documentation, addressed by the paper vignette and the corresponding R +#' documentation, addressed by the vignettes and the corresponding R #' Journal paper. #' @srrstats {G2.4, G2.4a, G2.4b, G2.4c, G2.6} Explicit conversions are used #' where necessary. @@ -35,11 +35,15 @@ knitr::opts_chunk$set( #' #' @srrstats {G3.0} No floating point equality comparisons are made. #' -#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} The -#' algorithms work correctly as per Vihola, Helske, Franks (2020) -#' (all simulations were implemented with the bssm package) and Helske -#' and Vihola (2021). Full replication of the results would take days/weeks -#' (but see also bsm_ng, negbin_series and several testthat tests). +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, G5.6, G5.6a, G5.6b, G5.7} and +#' @srrstats {BS4.0, BS4.1} The algorithms work as defined per Vihola, Helske, +#' Franks (2020) (all simulations were implemented with the bssm package) and +#' Helske and Vihola (2021). Full replication of the results would take +#' days/weeks (but see also bsm_ng, negbin_series and several testthat tests). +#' This is the first R package to implement delayed acceptance pseudo-marginal +#' MCMC for general state space models. +#' Note that the IS-MCMC method is also available in \code{walker} package for +#' limited class of models (subset of the models supported by \code{bssm}). #' #' @srrstats {G5.8, G5.8a, G5.8b, G5.8c, G5.8d} Tested with autotest and the #' testthat tests. @@ -53,7 +57,6 @@ knitr::opts_chunk$set( #' @srrstats {BS7.4, BS7.4a} The scales do not matter (in terms of runtime) #' in random walk Metropolis nor in particle filters, as long as numerical #' issues are not encountered - ``` # bssm @@ -128,7 +131,7 @@ xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, - # Define priors, see ?bssm_prior + # Define priors for hyperparameters (i.e. not the states), see ?bssm_prior # Initial value followed by parameters of the prior distribution beta = normal_prior(rep(0, ncol(xreg)), 0, 1), sd_y = gamma_prior(1, 2, 0.01), diff --git a/README.md b/README.md index 9d70b001..ef485f80 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,7 @@ xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, - # Define priors, see ?bssm_prior + # Define priors for hyperparameters (i.e. not the states), see ?bssm_prior # Initial value followed by parameters of the prior distribution beta = normal_prior(rep(0, ncol(xreg)), 0, 1), sd_y = gamma_prior(1, 2, 0.01), @@ -142,7 +142,7 @@ fit #> #> Run time: #> user system elapsed -#> 0.94 0.00 0.94 +#> 1.02 0.02 1.02 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -210,7 +210,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 10.82 0.08 10.83 +#> 14.47 0.19 14.45 ``` Comparison: @@ -320,7 +320,7 @@ fit #> #> Run time: #> user system elapsed -#> 12.05 0.20 12.08 +#> 16.63 0.20 16.65 ``` Draw predictions: diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd index ac55ec21..e20412c0 100644 --- a/man/negbin_model.Rd +++ b/man/negbin_model.Rd @@ -25,7 +25,7 @@ bssm_model <- bsm_ng(negbin_series[, "y"], a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), distribution = "negative binomial") -\dontest{ +\donttest{ # In the paper we used 60000 iterations with first 10000 as burnin fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1) fit_bssm diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index 29fdea53..d21eddb0 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/print_mcmc.R +% Please edit documentation in R/summary.R \name{summary.mcmc_output} \alias{summary.mcmc_output} \title{Summary Statistics of Posterior Samples} diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 528c9ecb..b7d16792 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -302,7 +302,7 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { sumr <- expect_error(summary(mcmc_poisson, variable = "both"), NA) - expect_lt(sum(abs(sumr[1, c(1, 3)] - + expect_lt(sum(abs(sumr$theta[1, 2:3] - c(0.25892090511681, 0.186796779799571))), 0.5) states <- expand_sample(mcmc_poisson, variable = "states") @@ -317,10 +317,10 @@ test_that("MCMC results with psi-APF for Poisson model are correct", { states = list(4))) - expect_equal(sumr$Mean[seq(2, nrow(sumr), by = 2)], + expect_equal(sumr$states$Mean[sumr$states$variable == "level"], as.numeric(colMeans(states$level))) - expect_error(posterior::as_draws(mcmc_poisson), NA) + expect_error(as_draws(mcmc_poisson), NA) expect_error(d <- as.data.frame(mcmc_poisson, variable = "state"), NA) x <- dplyr::pull(dplyr::summarise( dplyr::group_by( @@ -374,7 +374,7 @@ test_that("MCMC using SPDK for Gamma model works", { expect_true(is.finite(sum(mcmc_gamma$alpha))) expect_lt(sum(abs(summary(mcmc_gamma)$Mean - - c(0.542149368711246, 12.353642743311))), 2) + c(12.353642743311, 0.542149368711246))), 2) }) diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index 85ee4155..91141a20 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -25,23 +25,9 @@ if (!requireNamespace("rmarkdown") || knitr::opts_chunk$set(echo = TRUE) ``` -```{r srr-tags, eval = FALSE, echo = FALSE} -#' rOpenSci Statistical Software Standards addressed by the vignette -#' -#' @srrstats {G1.0, G1.1, G1.3, G1.5, G1.6, BS4.0, BS4.1} Here and in other -#' vignettes, in the R Journal paper and Vihola, Helske, Franks (2020). -#' To our knowledge, the package is also the first R package to -#' implement delayed acceptance pseudo-marginal MCMC for general state space -#' models. -#' Note that the IS-MCMC method is also available in \code{walker} package for -#' limited class of models (subset of the models supported by \code{bssm}). -``` - - This is a short vignette illustrating the `bssm` package. For more detailed exposition, please see the corresponding R Journal paper: -Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R". Accepted to R Journal. [ArXiv preprint]( -https://arxiv.org/abs/2101.08492). +Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R". Accepted to R Journal. [ArXiv preprint](https://arxiv.org/abs/2101.08492). # Introduction From 9717ca4227c9e8d4a22445df06c3c36308c6e32c Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 22 Nov 2021 21:37:32 +0200 Subject: [PATCH 140/180] test for numeric in IACT --- R/asymptotic_var.R | 44 ++++++++++++++++++++++++++++++++++---------- README.Rmd | 11 ++++++----- 2 files changed, 40 insertions(+), 15 deletions(-) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 1c46c76d..532d1a8a 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -22,6 +22,10 @@ #' # ESS estimate: #' length(x) / iact(x) iact <- function(x) { + + if (!test_numeric(x)) + stop("Argument 'x' should be a numeric vector. ") + IACT((x - mean(x)) / sd(x)) } @@ -59,6 +63,7 @@ iact <- function(x) { #' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. #' https://doi.org/10.1214/20-BA1221 #' @export +#' @importFrom checkmate test_numeric #' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) @@ -72,12 +77,21 @@ iact <- function(x) { #' asymptotic_var(x, w, method = "geyer") #' asymptotic_var <- function(x, w, method = "sokal") { + method <- match.arg(method, c("sokal", "geyer")) - if (missing(w)) w <- rep(1, length(x)) - if(any(w < 0) | any(!is.finite(w))) - stop("Nonfinite or negative weights in 'w'.") - if (!any(w > 0)) { - stop("No positive weights in 'w'.") + if (!test_numeric(x)) + stop("Argument 'x' should be a numeric vector. ") + + if (missing(w)) { + w <- rep(1, length(x)) + } else { + if (!test_numeric(w)) + stop("Argument 'w' should be a numeric vector. ") + if(any(w < 0) | any(!is.finite(w))) + stop("Nonfinite or negative weights in 'w'.") + if (!any(w > 0)) { + stop("No positive weights in 'w'.") + } } estimate_c <- mean(w) estimate_mean <- weighted_mean(x, w) @@ -130,12 +144,22 @@ asymptotic_var <- function(x, w, method = "sokal") { #' estimate_ess(x, w, method = "geyer") #' estimate_ess <- function(x, w, method = "sokal") { + method <- match.arg(method, c("sokal", "geyer")) - if (missing(w)) w <- rep(1, length(x)) - if(any(w < 0) | any(!is.finite(w))) - stop("Nonfinite or negative weights in 'w'.") - if (!any(w > 0)) { - stop("No positive weights in 'w'.") + + if (!test_numeric(x)) + stop("Argument 'x' should be a numeric vector. ") + + if (missing(w)) { + w <- rep(1, length(x)) + } else { + if (!test_numeric(w)) + stop("Argument 'w' should be a numeric vector. ") + if(any(w < 0) | any(!is.finite(w))) + stop("Nonfinite or negative weights in 'w'.") + if (!any(w > 0)) { + stop("No positive weights in 'w'.") + } } weighted_var(x, w) / asymptotic_var(x, w, method = method) } diff --git a/README.Rmd b/README.Rmd index 7bbcdd43..8c5c8d90 100644 --- a/README.Rmd +++ b/README.Rmd @@ -22,9 +22,14 @@ knitr::opts_chunk$set( #' #' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower are used where #' applicable. -#' @srrstats {G1.0, G1.1, G1.3, G1.4, G1.4a, G1.5, G1.6} General +#' @srrstats {G1.0, G1.3, G1.4, G1.4a, G1.5, G1.6} General #' documentation, addressed by the vignettes and the corresponding R #' Journal paper. +#' @srrstats {G1.1} This is the first R package to implement delayed acceptance +#' pseudo-marginal MCMC for general state space models, and the first to +#' implement the IS-MCMC by Vihola, Helske, and Franks (2020). The IS-MCMC +#' method is also available in code{walker} package for a limited class of +#' models (a subset of the models supported by \code{bssm}). #' @srrstats {G2.4, G2.4a, G2.4b, G2.4c, G2.6} Explicit conversions are used #' where necessary. #' @@ -40,10 +45,6 @@ knitr::opts_chunk$set( #' Franks (2020) (all simulations were implemented with the bssm package) and #' Helske and Vihola (2021). Full replication of the results would take #' days/weeks (but see also bsm_ng, negbin_series and several testthat tests). -#' This is the first R package to implement delayed acceptance pseudo-marginal -#' MCMC for general state space models. -#' Note that the IS-MCMC method is also available in \code{walker} package for -#' limited class of models (subset of the models supported by \code{bssm}). #' #' @srrstats {G5.8, G5.8a, G5.8b, G5.8c, G5.8d} Tested with autotest and the #' testthat tests. From 644aa29c3a425d3462e0de0f7f2d34c7d1f6f90a Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Tue, 23 Nov 2021 07:35:05 +0200 Subject: [PATCH 141/180] few doc fixes and missing import --- NAMESPACE | 1 + R/asymptotic_var.R | 19 ++++++++++++------- man/asymptotic_var.Rd | 11 ++++++++--- man/estimate_ess.Rd | 6 +++--- man/iact.Rd | 2 +- 5 files changed, 25 insertions(+), 14 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 60497223..11914d4f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -88,6 +88,7 @@ importFrom(checkmate,test_double) importFrom(checkmate,test_flag) importFrom(checkmate,test_int) importFrom(checkmate,test_integerish) +importFrom(checkmate,test_numeric) importFrom(coda,mcmc) importFrom(diagis,ess) importFrom(diagis,weighted_mean) diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 532d1a8a..6a3be8f3 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -4,7 +4,7 @@ #' Note that the estimator is not particularly good for very short series x #' (say < 100), but that is not very practical for MCMC applications anyway. #' -#' @param x A vector. +#' @param x A numeric vector. #' @references #' Sokal A. (1997) Monte Carlo Methods in Statistical Mechanics: Foundations #' and New Algorithms. @@ -39,9 +39,9 @@ iact <- function(x) { #' \code{posterior} package. #' #' @importFrom posterior ess_mean -#' @param x Vector of samples. -#' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is -#' assumed). +#' @param x A numeric vector of samples. +#' @param w A numeric vector of weights. If missing, set to 1 (i.e. no +#' weighting is assumed). #' @param method Method for computing IACT. Default is \code{"sokal"}, #' other option \code{"geyer"}. #' @references @@ -76,6 +76,11 @@ iact <- function(x) { #' asymptotic_var(x, w, method = "sokal") #' asymptotic_var(x, w, method = "geyer") #' +#' data("negbin_model") +#' # can be obtained directly with summary method +#' d <- suppressWarnings(as_draws(negbin_model)) +#' sqrt(asymptotic_var(d$sd_level, d$weight)) +#' asymptotic_var <- function(x, w, method = "sokal") { method <- match.arg(method, c("sokal", "geyer")) @@ -112,9 +117,9 @@ asymptotic_var <- function(x, w, method = "sokal") { #' using the identity ESS(x) = var(x) / MCMCSE^2 where var(x) is the #' posterior variance of x assuming independent samples. #' -#' @param x Vector of samples. -#' @param w Vector of weights. If missing, set to 1 (i.e. no weighting is -#' assumed). +#' @param x A numeric vector of samples. +#' @param w A numeric vector of weights. If missing, set to 1 (i.e. no +#' weighting is assumed). #' @param method Method for computing the ESS. Default is \code{"sokal"}, other #' option are \code{"geyer"} (see also \code{asymptotic_var}). #' @references diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd index 7ebd3f12..896a9a7f 100644 --- a/man/asymptotic_var.Rd +++ b/man/asymptotic_var.Rd @@ -7,10 +7,10 @@ asymptotic_var(x, w, method = "sokal") } \arguments{ -\item{x}{Vector of samples.} +\item{x}{A numeric vector of samples.} -\item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is -assumed).} +\item{w}{A numeric vector of weights. If missing, set to 1 (i.e. no +weighting is assumed).} \item{method}{Method for computing IACT. Default is \code{"sokal"}, other option \code{"geyer"}.} @@ -34,6 +34,11 @@ w <- rexp(n, 0.5 * exp(0.001 * x^2)) asymptotic_var(x, w, method = "sokal") asymptotic_var(x, w, method = "geyer") +data("negbin_model") +# can be obtained directly with summary method +d <- suppressWarnings(as_draws(negbin_model)) +sqrt(asymptotic_var(d$sd_level, d$weight)) + } \references{ Vihola M, Helske J, Franks J. (2020). Importance sampling type estimators diff --git a/man/estimate_ess.Rd b/man/estimate_ess.Rd index 3e3a0d35..99303889 100644 --- a/man/estimate_ess.Rd +++ b/man/estimate_ess.Rd @@ -7,10 +7,10 @@ estimate_ess(x, w, method = "sokal") } \arguments{ -\item{x}{Vector of samples.} +\item{x}{A numeric vector of samples.} -\item{w}{Vector of weights. If missing, set to 1 (i.e. no weighting is -assumed).} +\item{w}{A numeric vector of weights. If missing, set to 1 (i.e. no +weighting is assumed).} \item{method}{Method for computing the ESS. Default is \code{"sokal"}, other option are \code{"geyer"} (see also \code{asymptotic_var}).} diff --git a/man/iact.Rd b/man/iact.Rd index 98978e4e..4a559250 100644 --- a/man/iact.Rd +++ b/man/iact.Rd @@ -7,7 +7,7 @@ iact(x) } \arguments{ -\item{x}{A vector.} +\item{x}{A numeric vector.} } \description{ Estimates the integrated autocorrelation time (IACT) based on Sokal (1997). From 104030ba572782743c573651a0842a5c526cfb04 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 24 Nov 2021 18:07:49 +0200 Subject: [PATCH 142/180] fix docs etc based on autotest --- NAMESPACE | 16 +-- R/approx.R | 2 +- R/as.data.frame.mcmc_output.R | 4 +- R/as_bssm.R | 2 +- R/as_draws.R | 4 +- R/asymptotic_var.R | 17 +-- R/bootstrap_filter.R | 12 +- R/check_arguments.R | 23 ++-- R/cpp_example_models.R | 6 +- R/ekpf_filter.R | 2 +- R/expand_sample.R | 4 +- R/importance_sample.R | 3 +- R/kfilter.R | 6 +- R/loglik.R | 5 +- R/model_type.R | 2 +- R/models.R | 154 +++++++++++++----------- R/particle_smoother.R | 12 +- R/post_correction.R | 12 +- R/predict.R | 11 +- R/priors.R | 22 ++-- R/run_mcmc.R | 25 ++-- R/sim_smoother.R | 18 +-- R/smoother.R | 8 +- R/summary.R | 12 +- man/ar1_lg.Rd | 15 +-- man/ar1_ng.Rd | 17 +-- man/as.data.frame.mcmc_output.Rd | 4 +- man/as_bssm.Rd | 2 +- man/as_draws-mcmc_output.Rd | 4 +- man/asymptotic_var.Rd | 3 + man/bootstrap_filter.Rd | 4 +- man/bsm_lg.Rd | 59 ++++----- man/bsm_ng.Rd | 26 ++-- man/bssm_prior.Rd | 22 ++-- man/estimate_ess.Rd | 3 + man/expand_sample.Rd | 4 +- man/iact.Rd | 8 +- man/kfilter.Rd | 6 +- man/logLik_bssm.Rd | 9 +- man/particle_smoother.Rd | 4 +- man/predict.mcmc_output.Rd | 11 +- man/run_mcmc.Rd | 19 +-- man/sim_smoother.Rd | 4 +- man/smoother.Rd | 8 +- man/ssm_mlg.Rd | 8 +- man/ssm_mng.Rd | 16 +-- man/ssm_nlg.Rd | 8 +- man/ssm_sde.Rd | 2 +- man/ssm_ulg.Rd | 10 +- man/ssm_ung.Rd | 10 +- man/suggest_N.Rd | 2 +- man/summary.mcmc_output.Rd | 6 +- man/svm.Rd | 26 ++-- src/mcmc.h | 2 +- tests/testthat/test_as_data_frame.R | 2 +- tests/testthat/test_bootstrap_filter.R | 2 +- tests/testthat/test_mcmc.R | 9 +- tests/testthat/test_particle_smoother.R | 2 +- 58 files changed, 385 insertions(+), 334 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 11914d4f..11e92aa1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,36 +3,36 @@ S3method(as.data.frame,mcmc_output) S3method(as_draws,mcmc_output) S3method(as_draws_df,mcmc_output) -S3method(bootstrap_filter,gaussian) +S3method(bootstrap_filter,lineargaussian) S3method(bootstrap_filter,nongaussian) S3method(bootstrap_filter,ssm_nlg) S3method(bootstrap_filter,ssm_sde) S3method(ekpf_filter,ssm_nlg) -S3method(fast_smoother,gaussian) +S3method(fast_smoother,lineargaussian) S3method(fast_smoother,nongaussian) S3method(fitted,mcmc_output) S3method(gaussian_approx,nongaussian) S3method(gaussian_approx,ssm_nlg) S3method(importance_sample,nongaussian) -S3method(kfilter,gaussian) +S3method(kfilter,lineargaussian) S3method(kfilter,nongaussian) -S3method(logLik,gaussian) +S3method(logLik,lineargaussian) S3method(logLik,nongaussian) S3method(logLik,ssm_nlg) S3method(logLik,ssm_sde) -S3method(particle_smoother,gaussian) +S3method(particle_smoother,lineargaussian) S3method(particle_smoother,nongaussian) S3method(particle_smoother,ssm_nlg) S3method(particle_smoother,ssm_sde) S3method(predict,mcmc_output) S3method(print,mcmc_output) -S3method(run_mcmc,gaussian) +S3method(run_mcmc,lineargaussian) S3method(run_mcmc,nongaussian) S3method(run_mcmc,ssm_nlg) S3method(run_mcmc,ssm_sde) -S3method(sim_smoother,gaussian) +S3method(sim_smoother,lineargaussian) S3method(sim_smoother,nongaussian) -S3method(smoother,gaussian) +S3method(smoother,lineargaussian) S3method(smoother,nongaussian) S3method(summary,mcmc_output) export(ar1_lg) diff --git a/R/approx.R b/R/approx.R index f3ef6ab5..d140ca91 100644 --- a/R/approx.R +++ b/R/approx.R @@ -88,7 +88,7 @@ gaussian_approx.ssm_nlg <- function(model, max_iter = 100, model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") - model$iekf_iter <- check_intmax(iekf_iter, "iekf_iter") + model$iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) out <- gaussian_approx_model_nlg(t(model$y), model$Z, model$H, model$T, model$R, model$Z_gn, model$T_gn, model$a1, model$P1, diff --git a/R/as.data.frame.mcmc_output.R b/R/as.data.frame.mcmc_output.R index addf83b9..c99713bc 100644 --- a/R/as.data.frame.mcmc_output.R +++ b/R/as.data.frame.mcmc_output.R @@ -8,9 +8,9 @@ #' @param optional Ignored. #' @param variable Return samples of \code{"theta"} (default) or #' \code{"states"}? -#' @param times Vector of indices. In case of states, +#' @param times A vector of indices. In case of states, #' what time points to return? Default is all. -#' @param states Vector of indices. In case of states, +#' @param states A vector of indices. In case of states, #' what states to return? Default is all. #' @param expand Should the jump-chain be expanded? #' Defaults to \code{TRUE}. diff --git a/R/as_bssm.R b/R/as_bssm.R index 55d7949a..df39fe31 100644 --- a/R/as_bssm.R +++ b/R/as_bssm.R @@ -12,7 +12,7 @@ #' used to replace exact diffuse elements of the original model. #' @param ... Additional arguments to model building functions of \code{bssm} #' (such as prior and updating functions, C, and D). -#' @return Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +#' @return An object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or #' \code{ssm_mng}. #' @export #' @examples diff --git a/R/as_draws.R b/R/as_draws.R index e1bcb21b..ec1260da 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -12,9 +12,9 @@ #' i.e. those results correspond to approximate MCMC. #' #' @param x An object of class \code{mcmc_output}. -#' @param times Vector of indices defining which time points to return? +#' @param times A vector of indices defining which time points to return? #' Default is all. -#' @param states Vector of indices defining which states to return. +#' @param states A vector of indices defining which states to return. #' Default is all. #' @param ... Ignored. #' @return A \code{draws_df} object. diff --git a/R/asymptotic_var.R b/R/asymptotic_var.R index 6a3be8f3..58e64e6e 100644 --- a/R/asymptotic_var.R +++ b/R/asymptotic_var.R @@ -5,6 +5,7 @@ #' (say < 100), but that is not very practical for MCMC applications anyway. #' #' @param x A numeric vector. +#' @return A single numeric value of IACT estimate. #' @references #' Sokal A. (1997) Monte Carlo Methods in Statistical Mechanics: Foundations #' and New Algorithms. @@ -15,12 +16,11 @@ #' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) -#' n <- 1e4 +#' n <- 1000 #' x <- numeric(n) #' phi <- 0.8 #' for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) -#' # ESS estimate: -#' length(x) / iact(x) +#' iact(x) iact <- function(x) { if (!test_numeric(x)) @@ -39,11 +39,13 @@ iact <- function(x) { #' \code{posterior} package. #' #' @importFrom posterior ess_mean +#' @importFrom checkmate test_numeric #' @param x A numeric vector of samples. #' @param w A numeric vector of weights. If missing, set to 1 (i.e. no #' weighting is assumed). #' @param method Method for computing IACT. Default is \code{"sokal"}, #' other option \code{"geyer"}. +#' @return A single numeric value of asymptotic variance estimate. #' @references #' Vihola M, Helske J, Franks J. (2020). Importance sampling type estimators #' based on approximate marginal Markov chain Monte Carlo. @@ -63,7 +65,6 @@ iact <- function(x) { #' assessing convergence of MCMC. Bayesian analysis, 16(2):667-718. #' https://doi.org/10.1214/20-BA1221 #' @export -#' @importFrom checkmate test_numeric #' @srrstats {BS5.3, BS5.5} #' @examples #' set.seed(1) @@ -83,10 +84,9 @@ iact <- function(x) { #' asymptotic_var <- function(x, w, method = "sokal") { - method <- match.arg(method, c("sokal", "geyer")) - if (!test_numeric(x)) + method <- match.arg(tolower(method), c("sokal", "geyer")) + if (!test_numeric(x) & !is.null(class(x))) stop("Argument 'x' should be a numeric vector. ") - if (missing(w)) { w <- rep(1, length(x)) } else { @@ -137,6 +137,7 @@ asymptotic_var <- function(x, w, method = "sokal") { #' Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. #' @export #' @srrstats {BS5.3, BS5.5} +#' @return A single numeric value of effective sample size estimate. #' @examples #' set.seed(1) #' n <- 1e4 @@ -150,7 +151,7 @@ asymptotic_var <- function(x, w, method = "sokal") { #' estimate_ess <- function(x, w, method = "sokal") { - method <- match.arg(method, c("sokal", "geyer")) + method <- match.arg(tolower(method), c("sokal", "geyer")) if (!test_numeric(x)) stop("Argument 'x' should be a numeric vector. ") diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index 568afc62..f701b97c 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -19,7 +19,7 @@ bootstrap_filter <- function(model, particles, ...) { UseMethod("bootstrap_filter", model) } -#' @method bootstrap_filter gaussian +#' @method bootstrap_filter lineargaussian #' @rdname bootstrap_filter #' @export #' @examples @@ -32,7 +32,7 @@ bootstrap_filter <- function(model, particles, ...) { #' ts.plot(cbind(y, x, out$att), col = 1:3) #' ts.plot(cbind(kfilter(model)$att, out$att), col = 1:3) #' -bootstrap_filter.gaussian <- function(model, particles, +bootstrap_filter.lineargaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) { check_missingness(model) @@ -50,7 +50,7 @@ bootstrap_filter.gaussian <- function(model, particles, nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -94,7 +94,7 @@ bootstrap_filter.nongaussian <- function(model, particles, seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -133,7 +133,7 @@ bootstrap_filter.ssm_nlg <- function(model, particles, nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -177,7 +177,7 @@ bootstrap_filter.ssm_sde <- function(model, particles, L, particles <- check_intmax(particles, "particles") nsamples <- length(model$y) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } diff --git a/R/check_arguments.R b/R/check_arguments.R index a6a0a7d0..dd12ebba 100644 --- a/R/check_arguments.R +++ b/R/check_arguments.R @@ -7,13 +7,13 @@ #' @param name Name of the argument used in printing error messages. #' @param positive Logical, check for positiveness of \code{x}. #' @param max Maximum value of \code{x}. -#' @param p Integer, number of time series. -#' @param n Integer, number of time points. -#' @param m Integer, dimensionality of the state vector. -#' @param k Integer, number of predictors. +#' @param p An integer, number of time series. +#' @param n An integer, number of time points. +#' @param m An integer, dimensionality of the state vector. +#' @param k An integer, number of predictors. #' @param multivariate Logical, should \code{p} be larger than 1? -#' @param beta Vector of regression coefficients. -#' @param xreg Matrix or vector of predictors. +#' @param beta A vector of regression coefficients. +#' @param xreg A matrix or vector of predictors. #' @param distribution Distribution(s) of the responses. #' @param y The response time series. #' @param type Name to be added to the sd parameter name. @@ -75,7 +75,7 @@ check_period <- function(x, n) { stop("Period should be less than the number of time points.") } } - x + as.integer(x) } #' @srrstats {BS2.5} Checks that observations are compatible with their #' distributions are made. @@ -404,14 +404,15 @@ check_H <- function(x, p, n, multivariate = FALSE) { } -check_intmax <- function(x, name = "particles", positive = TRUE, max = 1e7) { - if (!test_count(x, positive)) { +check_intmax <- function(x, name = "particles", positive = TRUE, max = 1e5) { + # autotest complains without additional positivity test + if (!test_count(x, positive) | (positive & x <= 0)) { stop(paste0("Argument '", name, "' should be a ", ifelse(positive, "positive", "non-negative"), " integer. ")) } if (x > max) { - stop(paste0("I don't believe you want '", name, "' > ", max, - ". If you really do, file an issue at Github.")) + stop(paste0("You probably do not want '", name, "' > ", max, + ". If you really do, please file an issue at Github. ")) } as.integer(x) } diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 985a0c92..61cb8a87 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -14,11 +14,11 @@ #' cpp_example_model <- function(example, return_code = FALSE) { - example <- match.arg(example, c("nlg_linear_gaussian", "nlg_sin_exp", - "nlg_growth", "nlg_ar_exp", "sde_poisson_OU", "sde_gbm")) + example <- match.arg(tolower(example), c("nlg_linear_gaussian", "nlg_sin_exp", + "nlg_growth", "nlg_ar_exp", "sde_poisson_ou", "sde_gbm")) code <- switch(example, - "sde_poisson_OU" = { + "sde_poisson_ou" = { ' // A latent Ornstein-Uhlenbeck process with Poisson observations // dalpha_t = rho (nu - alpha_t) dt + sigma dB_t, t>=0 diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index eab96f71..1234127f 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -60,7 +60,7 @@ ekpf_filter.ssm_nlg <- function(model, particles, nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } diff --git a/R/expand_sample.R b/R/expand_sample.R index 156027a8..a5bdd0ac 100644 --- a/R/expand_sample.R +++ b/R/expand_sample.R @@ -18,9 +18,9 @@ #' @importFrom coda mcmc #' @param x Output from \code{\link{run_mcmc}}. #' @param variable Expand parameters \code{"theta"} or states \code{"states"}. -#' @param times Vector of indices. In case of states, +#' @param times A vector of indices. In case of states, #' what time points to expand? Default is all. -#' @param states Vector of indices. In case of states, +#' @param states A vector of indices. In case of states, #' what states to expand? Default is all. #' @param by_states If \code{TRUE} (default), return list by states. #' Otherwise by time. diff --git a/R/importance_sample.R b/R/importance_sample.R index ec94bc36..836355af 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -4,7 +4,6 @@ #' corresponding (scaled) importance weights. #' Probably mostly useful for comparing KFAS and bssm packages. #' -#' #' @inheritParams gaussian_approx #' @param model Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, #' \code{ssm_ung}, or \code{ssm_mng}. @@ -56,6 +55,8 @@ importance_sample.nongaussian <- function(model, nsim, use_antithetic = TRUE, "values, you might run out of memory.")) } seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) + if (!test_flag(use_antithetic)) + stop("Argument 'use_antithetic' should be TRUE or FALSE. ") model$distribution <- pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 diff --git a/R/kfilter.R b/R/kfilter.R index 073be2a8..ed28b8ff 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -7,7 +7,7 @@ #' For non-Gaussian models, the filtering is based on the approximate #' Gaussian model. #' -#' @param model Model of class \code{gaussian}, \code{nongaussian} or +#' @param model Model of class \code{lineargaussian}, \code{nongaussian} or #' \code{ssm_nlg}. #' @param ... Ignored. #' @return List containing the log-likelihood @@ -21,7 +21,7 @@ kfilter <- function(model, ...) { UseMethod("kfilter", model) } -#' @method kfilter gaussian +#' @method kfilter lineargaussian #' @rdname kfilter #' @export #' @examples @@ -29,7 +29,7 @@ kfilter <- function(model, ...) { #' y <- x + rnorm(20, sd = 0.1) #' model <- bsm_lg(y, sd_level = 1, sd_y = 0.1) #' ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3) -kfilter.gaussian <- function(model, ...) { +kfilter.lineargaussian <- function(model, ...) { check_missingness(model) diff --git a/R/loglik.R b/R/loglik.R index a415a12b..ab9bc9c0 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -15,8 +15,9 @@ #' For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle #' filter (or just EKF/IEKF approximation in the case of \code{particles = 0}). #' @importFrom stats logLik -#' @method logLik gaussian +#' @method logLik lineargaussian #' @rdname logLik_bssm +#' @return A numeric value. #' @seealso particle_smoother #' @export #' @references @@ -47,7 +48,7 @@ #' @examples #' model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) #' logLik(model) -logLik.gaussian <- function(object, ...) { +logLik.lineargaussian <- function(object, ...) { check_missingness(object) diff --git a/R/model_type.R b/R/model_type.R index 3140b170..4a82e039 100644 --- a/R/model_type.R +++ b/R/model_type.R @@ -1,5 +1,5 @@ model_type <- function(model) { - if (inherits(model, "gaussian")) { + if (inherits(model, "lineargaussian")) { switch(class(model)[1], "ssm_mlg" = 0L, "ssm_ulg" = 1L, diff --git a/R/models.R b/R/models.R index 72148650..5522072c 100644 --- a/R/models.R +++ b/R/models.R @@ -47,9 +47,9 @@ default_update_fn <- function(theta) { #' output. #' #' @inheritParams ssm_ung -#' @param H Vector of standard deviations. Either a scalar or a vector of +#' @param H A vector of standard deviations. Either a scalar or a vector of #' length n. -#' @param update_fn Function which returns list of updated model +#' @param update_fn A function which returns list of updated model #' components given input vector theta. This function should take only one #' vector argument which is used to create list with elements named as #' \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and @@ -58,7 +58,7 @@ default_update_fn <- function(theta) { #' the dimensions of input arguments can differ from the final dimensions. #' If any of these components is missing, it is assumed to be constant wrt. #' theta. -#' @return Object of class \code{ssm_ulg}. +#' @return An object of class \code{ssm_ulg}. #' @export #' @examples #' @@ -232,7 +232,7 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, xreg = matrix(0, 0, 0), beta = numeric(0)), - class = c("ssm_ulg", "gaussian", "bssm_model")) + class = c("ssm_ulg", "lineargaussian", "bssm_model")) } #' General univariate non-Gaussian state space model #' @@ -277,14 +277,14 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' a m x k matrix or a m x k x n array, or object which can be coerced to such. #' @param a1 Prior mean for the initial state as a vector of length m. #' @param P1 Prior covariance matrix for the initial state as m x m matrix. -#' @param state_names Names for the states. +#' @param state_names A character vector defining the names of the states. #' @param C Intercept terms \eqn{C_t} for the state equation, given as a #' m times 1 or m times n matrix. #' @param D Intercept terms \eqn{D_t} for the observations equation, given as a #' scalar or vector of length n. #' @param init_theta Initial values for the unknown hyperparameters theta #' (i.e. unknown variables excluding latent state variables). -#' @param update_fn Function which returns list of updated model +#' @param update_fn A function which returns list of updated model #' components given input vector theta. This function should take only one #' vector argument which is used to create list with elements named as #' \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and @@ -293,9 +293,9 @@ ssm_ulg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' theta. It's best to check the internal dimensions with #' \code{str(model_object)} as the dimensions of input arguments can differ #' from the final dimensions. -#' @param prior_fn Function which returns log of prior density +#' @param prior_fn A function which returns log of prior density #' given input vector theta. -#' @return Object of class \code{ssm_ung}. +#' @return An object of class \code{ssm_ung}. #' @export #' @examples #' @@ -399,7 +399,7 @@ ssm_ung <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, #' or a m x k x n array. #' @param D Intercept terms for observation equation, given as a p x n matrix. #' @param C Intercept terms for state equation, given as m x n matrix. -#' @return Object of class \code{ssm_mlg}. +#' @return An object of class \code{ssm_mlg}. #' @export #' @examples #' @@ -450,7 +450,8 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, structure(list(y = as.ts(y), Z = Z, H = H, T = T, R = R, a1 = a1, P1 = P1, D = D, C = C, update_fn = update_fn, prior_fn = prior_fn, theta = init_theta, - state_names = state_names), class = c("ssm_mlg", "gaussian", "bssm_model")) + state_names = state_names), class = c("ssm_mlg", "lineargaussian", + "bssm_model")) } #' General Non-Gaussian State Space Model @@ -481,7 +482,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' @param R Lower triangular matrix R the state equation. Either a m x k #' matrix or a #' m x k x n array. -#' @param distribution vector of distributions of the observed series. +#' @param distribution A vector of distributions of the observed series. #' Possible choices are #' \code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, #' \code{"gamma"}, and \code{"gaussian"}. @@ -489,13 +490,13 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' For negative binomial distribution this is the dispersion term, for #' gamma distribution this is the shape parameter, for Gaussian this is #' standard deviation, and for other distributions this is ignored. -#' @param u Matrix of positive constants for non-Gaussian models +#' @param u A matrix of positive constants for non-Gaussian models #' (of same dimensions as y). For Poisson, gamma, and negative binomial #' distribution, this corresponds to the offset term. For binomial, this is the -#' number of trials. +#' number of trials (and as such should be integer(ish)). #' @param D Intercept terms for observation equation, given as p x n matrix. #' @param C Intercept terms for state equation, given as m x n matrix. -#' @return Object of class \code{ssm_mng}. +#' @return An object of class \code{ssm_mng}. #' @export #' @examples #' @@ -533,7 +534,7 @@ ssm_mlg <- function(y, Z, H, T, R, a1 = NULL, P1 = NULL, #' # smoothing based on approximating gaussian model #' ts.plot(cbind(y, fast_smoother(model)), #' col = 1:3, lty = c(1, 1, 2)) -#' +#' ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, phi = 1, u, init_theta = numeric(0), D = NULL, C = NULL, state_names, update_fn = default_update_fn, prior_fn = default_prior_fn) { @@ -601,29 +602,18 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' #' @inheritParams bsm_ng #' @param sd_y Standard deviation of the noise of observation equation. -#' Should be an object of class \code{bssm_prior} or scalar. -#' @param D,C Intercept terms for observation and -#' state equations, given as a length n vector and m times n matrix -#' respectively (or scalar and m times 1 matrix). -#' @return Object of class \code{bsm_lg}. +#' Should be an object of class \code{bssm_prior} or scalar +#' value defining a known value such as 0. +#' @param D Intercept terms for observation equation, given as a length n +#' numeric vector or a scalar in case of time-invariant intercept. +#' @param C Intercept terms for state equation, given as a m times n matrix +#' or m times 1 matrix in case of time-invariant intercept. +#' @return An object of class \code{bsm_lg}. #' @export #' @examples #' -#' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) -#' # period here is redundant as frequency(UKgas) = 4 -#' model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, -#' sd_slope = prior, sd_seasonal = prior, period = 4) -#' -#' # Note small number of iterations for CRAN checks -#' mcmc_out <- run_mcmc(model, iter = 5000) -#' summary(mcmc_out, return_se = TRUE) -#' # Use the summary method from coda: -#' summary(expand_sample(mcmc_out, "theta"))$stat -#' mcmc_out$theta[which.max(mcmc_out$posterior), ] -#' sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] -#' #' set.seed(1) -#' n <- 10 +#' n <- 50 #' x <- rnorm(n) #' level <- numeric(n) #' level[1] <- rnorm(1) @@ -635,8 +625,22 @@ ssm_mng <- function(y, Z, T, R, a1 = NULL, P1 = NULL, distribution, #' #' ts.plot(cbind(fast_smoother(model), level), col = 1:2) #' +#' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +#' # period here is redundant as frequency(UKgas) = 4 +#' model_UKgas <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, +#' sd_slope = prior, sd_seasonal = prior, period = 4) +#' +#' # Note small number of iterations for CRAN checks +#' mcmc_out <- run_mcmc(model_UKgas, iter = 5000) +#' summary(mcmc_out, return_se = TRUE) +#' # Use the summary method from coda: +#' summary(expand_sample(mcmc_out, "theta"))$stat +#' mcmc_out$theta[which.max(mcmc_out$posterior), ] +#' sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] +#' + bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, - beta, xreg = NULL, period = frequency(y), a1 = NULL, P1 = NULL, D = NULL, + beta, xreg = NULL, period, a1 = NULL, P1 = NULL, D = NULL, C = NULL) { y <- check_y(y) @@ -690,7 +694,9 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, seasonal_names <- NULL seasonal <- FALSE sd_seasonal <- NULL + period <- 1L } else { + if (missing(period)) period <- frequency(y) period <- check_period(period, n) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") @@ -793,7 +799,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, fixed = as.integer(!notfixed), prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, - theta = theta), class = c("bsm_lg", "ssm_ulg", "gaussian", "bssm_model")) + theta = theta), class = c("bsm_lg", "ssm_ulg", "lineargaussian", + "bssm_model")) } #' Non-Gaussian Basic Structural (Time Series) Model @@ -802,7 +809,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' local trend component, a seasonal component, and regression component #' (or subset of these components). #' -#' @param y Vector or a \code{ts} object of observations. +#' @param y A vector or a \code{ts} object of observations. #' @param sd_level Standard deviation of the noise of level equation. #' Should be an object of class \code{bssm_prior} or scalar #' value defining a known value such as 0. @@ -814,7 +821,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' Should be an object of class \code{bssm_prior}, scalar #' value defining a known value such as 0, or missing, in which case the #' seasonal term is omitted from the model. -#' @param sd_noise Prior for the standard deviation of the additional noise +#' @param sd_noise A prior for the standard deviation of the additional noise #' term to be added to linear predictor, defined as an object of class #' \code{bssm_prior}. If missing, no additional noise term is used. #' @param distribution Distribution of the observed time series. Possible @@ -825,24 +832,26 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' distribution this is the shape parameter, and for other distributions this #' is ignored. Should an object of class \code{bssm_prior} or #' a positive scalar. -#' @param u Vector of positive constants for non-Gaussian models. For Poisson, +#' @param u A vector of positive constants for non-Gaussian models. For Poisson, #' gamma, and negative binomial distribution, this corresponds to the offset #' term. For binomial, this is the number of trials. -#' @param beta Prior for the regression coefficients. +#' @param beta A prior for the regression coefficients. #' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} #' (in case of multiple coefficients) or missing in case of no covariates. -#' @param xreg Matrix containing covariates with number of rows matching the -#' length of \code{y}. +#' @param xreg A matrix containing covariates with number of rows matching the +#' length of \code{y}. Can also be \code{ts}, \code{mts} or similar object +#' convertible to matrix. #' @param period Length of the seasonal pattern. -#' Default is \code{frequency(y)}. Must be a positive integer greater than 2 -#' and less than the length of the input time series. +#' Must be a positive value greater than 2 and less than the length of the +#' input time series. Default is \code{frequency(y)}, +#' which can also return non-integer value (in which case error is given). #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. -#' @param P1 Prior covariance for the initial states (level, slope, seasonals). +#' @param P1 Prior covariance matrix for the initial states (level, slope, seasonals). #' Default is diagonal matrix with 1000 on the diagonal. #' @param C Intercept terms for state equation, given as a m x n or m x 1 #' matrix. -#' @return Object of class \code{bsm_ng}. +#' @return An object of class \code{bsm_ng}. #' @export #' @examples #' # Same data as in Vihola, Helske, Franks (2020) @@ -867,7 +876,7 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' beta = normal(0, 0, 10), #' xreg = Seatbelts[, "law"], #' # default values, just for illustration -#' period = 12, +#' period = 12L, #' a1 = rep(0, 1 + 11), # level + period - 1 seasonal states #' P1 = diag(1, 12), #' C = matrix(0, 12, 1), @@ -915,10 +924,9 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' phi = gamma_prior(1, 5, 5)) #' bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, - distribution, phi, u, beta, xreg = NULL, period = frequency(y), + distribution, phi, u, beta, xreg = NULL, period, a1 = NULL, P1 = NULL, C = NULL) { - distribution <- match.arg(tolower(distribution), c("poisson", "binomial", "negative binomial", "gamma")) @@ -961,7 +969,9 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, seasonal_names <- NULL seasonal <- FALSE sd_seasonal <- NULL + period <- 1L } else { + if (missing(period)) period <- frequency(y) period <- check_period(period, n) if (is_prior(sd_seasonal)) { check_sd(sd_seasonal$init, "seasonal") @@ -1108,14 +1118,14 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' Constructs a simple stochastic volatility model with Gaussian errors and #' first order autoregressive signal. See the main vignette for details. #' -#' @param y Vector or a \code{\link{ts}} object of observations. -#' @param mu Prior for mu parameter of transition equation. +#' @param y A numeric vector or a \code{\link{ts}} object of observations. +#' @param mu A prior for mu parameter of transition equation. #' Should be an object of class \code{bssm_prior}. -#' @param rho prior for autoregressive coefficient. +#' @param rho A prior for autoregressive coefficient. #' Should be an object of class \code{bssm_prior}. -#' @param sd_ar Prior for the standard deviation of noise of the AR-process. +#' @param sd_ar A prior for the standard deviation of noise of the AR-process. #' Should be an object of class \code{bssm_prior}. -#' @param sigma Prior for sigma parameter of observation equation, internally +#' @param sigma A prior for sigma parameter of observation equation, internally #' denoted as phi. Should be an object of class \code{bssm_prior}. #' Ignored if \code{mu} is provided. Note that typically #' parametrization using mu is preferred due to better numerical properties and @@ -1123,18 +1133,18 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' Most notably the global approximation approach does not work with sigma #' parameterization as sigma is not a parameter of the resulting approximate #' model. -#' @return Object of class \code{svm}. +#' @return An object of class \code{svm}. #' @export #' @rdname svm #' @examples #' #' data("exchange") -#' exchange <- exchange[1:100] # faster CRAN check -#' model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), +#' y <- exchange[1:100] # for faster CRAN check +#' model <- svm(y, rho = uniform(0.98, -0.999, 0.999), #' sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) #' #' obj <- function(pars) { -#' -logLik(svm(exchange, +#' -logLik(svm(y, #' rho = uniform(pars[1], -0.999, 0.999), #' sd_ar = halfnormal(pars[2], 5), #' sigma = halfnormal(pars[3], 2)), particles = 0) @@ -1143,17 +1153,17 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' lower = c(-0.999, 1e-4, 1e-4), #' upper = c(0.999, 10, 10), method = "L-BFGS-B") #' pars <- opt$par -#' model <- svm(exchange, +#' model <- svm(y, #' rho = uniform(pars[1],-0.999,0.999), #' sd_ar = halfnormal(pars[2], 5), #' sigma = halfnormal(pars[3], 2)) #' #' # alternative parameterization -#' model2 <- svm(exchange, rho = uniform(0.98,-0.999, 0.999), +#' model2 <- svm(y, rho = uniform(0.98,-0.999, 0.999), #' sd_ar = halfnormal(0.15, 5), mu = normal(0, 0, 1)) #' #' obj2 <- function(pars) { -#' -logLik(svm(exchange, +#' -logLik(svm(y, #' rho = uniform(pars[1], -0.999, 0.999), #' sd_ar = halfnormal(pars[2], 5), #' mu = normal(pars[3], 0, 1)), particles = 0) @@ -1161,7 +1171,7 @@ bsm_ng <- function(y, sd_level, sd_slope, sd_seasonal, sd_noise, #' opt2 <- optim(c(0.98, 0.15, 0), obj2, lower = c(-0.999, 1e-4, -Inf), #' upper = c(0.999, 10, Inf), method = "L-BFGS-B") #' pars2 <- opt2$par -#' model2 <- svm(exchange, +#' model2 <- svm(y, #' rho = uniform(pars2[1],-0.999,0.999), #' sd_ar = halfnormal(pars2[2], 5), #' mu = normal(pars2[3], 0, 1)) @@ -1232,14 +1242,14 @@ svm <- function(y, mu, rho, sd_ar, sigma) { #' AR(1) process. #' #' @inheritParams bsm_ng -#' @param rho Prior for autoregressive coefficient. +#' @param rho A prior for autoregressive coefficient. #' Should be an object of class \code{bssm_prior}. #' @param mu A fixed value or a prior for the stationary mean of the latent #' AR(1) process. Should be an object of class \code{bssm_prior} or scalar #' value defining a fixed mean such as 0. -#' @param sigma Prior for the standard deviation of noise of the AR-process. +#' @param sigma A prior for the standard deviation of noise of the AR-process. #' Should be an object of class \code{bssm_prior} -#' @return Object of class \code{ar1_ng}. +#' @return An object of class \code{ar1_ng}. #' @export #' @rdname ar1_ng #' @examples @@ -1364,7 +1374,7 @@ ar1_ng <- function(y, rho, sigma, mu, distribution, phi, u, beta, #' #' @inheritParams ar1_ng #' @param sd_y A prior for the standard deviation of observation equation. -#' @return Object of class \code{ar1_lg}. +#' @return An object of class \code{ar1_lg}. #' @export #' @rdname ar1_lg #' @examples @@ -1455,7 +1465,7 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { prior_distributions = priors$prior_distribution, prior_parameters = priors$parameters, theta = theta, max_iter = 100, conv_tol = 1e-8), - class = c("ar1_lg", "ssm_ulg", "gaussian", "bssm_model")) + class = c("ar1_lg", "ssm_ulg", "lineargaussian", "bssm_model")) } #' @@ -1492,9 +1502,9 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' @param P1 Prior covariance matrix for the initial state as object of class #' \code{externalptr} #' @param theta Parameter vector passed to all model functions. -#' @param known_params Vector of known parameters passed to all model +#' @param known_params A vector of known parameters passed to all model #' functions. -#' @param known_tv_params Matrix of known parameters passed to all model +#' @param known_tv_params A matrix of known parameters passed to all model #' functions. #' @param n_states Number of states in the model (positive integer). #' @param n_etas Dimension of the noise term of the transition equation @@ -1506,8 +1516,8 @@ ar1_lg <- function(y, rho, sigma, mu, sd_y, beta, xreg = NULL) { #' the values of #' Z, H, T, and R vary with respect to time variable (given identical states). #' If used, this can speed up some computations. -#' @param state_names Vector containing names for the states. -#' @return Object of class \code{ssm_nlg}. +#' @param state_names A character vector containing names for the states. +#' @return An object of class \code{ssm_nlg}. #' @export #' @examples #' \donttest{ # Takes a while on CRAN @@ -1587,7 +1597,7 @@ ssm_nlg <- function(y, Z, H, T, R, Z_gn, T_gn, a1, P1, theta, #' @param x0 Fixed initial value for SDE at time 0. #' @param positive If \code{TRUE}, positivity constraint is #' forced by \code{abs} in Milstein scheme. -#' @return Object of class \code{ssm_sde}. +#' @return An object of class \code{ssm_sde}. #' @export #' @examples #' diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 55de7efc..1ae644a6 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -63,7 +63,7 @@ particle_smoother <- function(model, particles, ...) { UseMethod("particle_smoother", model) } -#' @method particle_smoother gaussian +#' @method particle_smoother lineargaussian #' @export #' @rdname particle_smoother #' @examples @@ -77,7 +77,7 @@ particle_smoother <- function(model, particles, ...) { #' use_antithetic = TRUE)) #' ts.plot(out$alphahat, rowMeans(out2), col = 1:2) #' -particle_smoother.gaussian <- function(model, particles, method = "psi", +particle_smoother.lineargaussian <- function(model, particles, method = "psi", seed = sample(.Machine$integer.max, size = 1), ...) { check_missingness(model) @@ -95,7 +95,7 @@ particle_smoother.gaussian <- function(model, particles, method = "psi", nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -150,7 +150,7 @@ particle_smoother.nongaussian <- function(model, particles, particles <- check_intmax(particles, "particles") nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -202,7 +202,7 @@ particle_smoother.ssm_nlg <- function(model, particles, nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } @@ -264,7 +264,7 @@ particle_smoother.ssm_sde <- function(model, particles, L, } particles <- check_intmax(particles, "particles") nsamples <- length(model$y) * particles - if (particles > 100 & nsamples > 1e12) { + if (particles > 100 & nsamples > 1e10) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } diff --git a/R/post_correction.R b/R/post_correction.R index 9dc445b2..00e37a7f 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -2,8 +2,8 @@ #' @param x Object of class \code{mcmc_output} or any other list style object #' which has matrix theta (where each row corresponds to one iteration) and #' vector \code{posterior}, -#' @return Vector containing theta corresponding to maximum log-posterior value -#' of the posterior sample. +#' @return A vector containing theta corresponding to maximum log-posterior +#' value of the posterior sample. #' @noRd get_map <- function(x) { x$theta[which.max(x$posterior), ] @@ -27,7 +27,7 @@ get_map <- function(x) { #' estimate from the (approximate) MCMC run. Can also be an output from #' \code{run_mcmc} which is then used to compute the MAP #' estimate of theta. -#' @param candidates Vector of positive integers containing the candidate +#' @param candidates A vector of positive integers containing the candidate #' number of particles to test. Default is \code{seq(10, 100, by = 10)}. #' @param replications Positive integer, how many replications should be used #' for computing the standard deviations? Default is 100. @@ -88,15 +88,15 @@ suggest_N <- function(model, theta, check_missingness(model) - replications <- check_intmax(replications, "replications") + replications <- check_intmax(replications, "replications", max = 1000) seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_integerish(candidates, lower = 1, any.missing = FALSE, min.len = 1)) { stop("Argument 'candidates' should be vector of positive integers. ") } - if (max(candidates) > 1e7) - stop(paste("I don't believe you want to use over 1e7 particles", + if (max(candidates) > 1e5) + stop(paste("I don't believe you want to use over 1e5 particles", "If you really do, please file an issue at Github.", sep = " ")) if (missing(theta) | (!is.vector(theta) & !inherits(theta, "mcmc_output"))) { diff --git a/R/predict.R b/R/predict.R index 433ad826..426d1643 100644 --- a/R/predict.R +++ b/R/predict.R @@ -27,7 +27,7 @@ #' C++ side, and \code{predict} also uses R side RNG for subsampling, so for #' replicable results you should call \code{set.seed} before \code{predict}. #' @param ... Ignored. -#' @return A \code{data.frame} consisting of samples from the predictive +#' @return A data.frame consisting of samples from the predictive #' posterior distribution. #' @method predict mcmc_output #' @aliases predict predict.mcmc_output @@ -45,7 +45,7 @@ #' start = tsp(model$y)[2] + 2 * deltat(model$y), #' frequency = frequency(model$y)) #' # use "state" for illustrative purposes, we could use type = "mean" directly -#' pred <- predict(mcmc_results, future_model, type = "state", +#' pred <- predict(mcmc_results, future_model = future_model, type = "state", #' nsim = 1000) #' #' library("dplyr") @@ -88,9 +88,9 @@ #' time = time(JohnsonJohnson))) #' #' # Posterior predictions for past observations: -#' yrep <- predict(mcmc_results, model, type = "response", +#' yrep <- predict(mcmc_results, future_model = model, type = "response", #' future = FALSE, nsim = 1000) -#' meanrep <- predict(mcmc_results, model, type = "mean", +#' meanrep <- predict(mcmc_results, future_model = model, type = "mean", #' future = FALSE, nsim = 1000) #' #' sumr_yrep <- yrep %>% @@ -108,7 +108,8 @@ #' mutate(interval = "Mean") #' #' rbind(sumr_meanrep, sumr_yrep) %>% -#' mutate(interval = factor(interval, levels = c("Observations", "Mean"))) %>% +#' mutate(interval = +#' factor(interval, levels = c("Observations", "Mean"))) %>% #' ggplot(aes(x = time, y = earnings)) + #' geom_ribbon(aes(ymin = lwr, ymax = upr, fill = interval), #' alpha = 0.75) + diff --git a/R/priors.R b/R/priors.R index 3ce0d136..75a618cf 100644 --- a/R/priors.R +++ b/R/priors.R @@ -37,8 +37,8 @@ #' uniform(init = 0.2, min = -1.0, max = 1.0) #' # two normal priors at once i.e. for coefficients beta: #' normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) -#' # Gamma prior -#' gamma(init = 0.1, shape = 2.5, rate = 1.1) +#' # Gamma prior (not run because autotest tests complain) +#' # gamma(init = 0.1, shape = 2.5, rate = 1.1) #' # Same as #' gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) #' # Half-normal @@ -46,21 +46,23 @@ #' # Truncated normal #' tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) #' -#' \dontshow{ +#' #' # Further examples for diagnostic purposes: #' uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) #' halfnormal(c(0, 0.2), c(1.0, 1.2)) -#' gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) +#' # not run because autotest bug +#' # gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) #' #' # longer versions: -#' uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -#' normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -#' tnormal_prior(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) -#' halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) -#' gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) -#' } +#' uniform_prior(init = c(0, 0.2), min = c(-1.0, 0.001), max = c(1.0, 1.2)) +#' normal_prior(init = c(0, 0.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2)) +#' tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), +#' min = c(1.2, 2), max = 3.3) +#' halfnormal_prior(init = c(0, 0.2), sd = c(1.0, 1.2)) +#' gamma_prior(init = c(0.1, 0.2), shape = c(1.2, 2), rate = c(3.3, 3.3)) +#' uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 427f12b4..5cef419b 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -33,17 +33,17 @@ #' @importFrom stats tsp #' @importFrom rlang is_interactive #' @param model Model of class \code{bssm_model}. -#' @param iter Positive integer defining the total number of MCMC iterations. +#' @param iter A positive integer defining the total number of MCMC iterations. #' @param output_type Either \code{"full"} #' (default, returns posterior samples from the posterior #' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of #' theta), or \code{"summary"} (return the mean and variance estimates of the #' states and posterior samples of theta). See details. -#' @param burnin Positive integer defining the length of the burn-in period +#' @param burnin A positive integer defining the length of the burn-in period #' which is disregarded from the results. Defaults to \code{iter / 2}. #' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the #' burn-in period in order to find good proposal distribution. -#' @param thin Positive integer defining the thinning rate. All MCMC algorithms +#' @param thin A positive integer defining the thinning rate. All MCMC algorithms #' in \code{bssm} use the jump chain representation (see refs), and the #' thinning is applied to these blocks. Defaults to 1. #' For IS-corrected methods, larger value can also be @@ -76,8 +76,8 @@ #' as a positive integer. #' Default is 100 (although typically only few iterations are needed). #' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' @param particles Number of state samples per MCMC iteration for models other -#' than linear-Gaussian models. +#' @param particles A positive integer defining the number of state samples per +#' MCMC iteration for models other than linear-Gaussian models. #' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. #' @param mcmc_type What type of MCMC algorithm should be used for models other #' than linear-Gaussian models? Possible choices are @@ -111,6 +111,7 @@ #' missing, defined by \code{rlang::is_interactive}. #' Set to \code{FALSE} if number of iterations is less than 50. #' @param ... Ignored. +#' @return An object of class \code{mcmc_output}. #' @export #' @srrstats {G2.3, G2.3a, G2.3b} match.arg and tolower used where applicable. #' @srrstats {BS1.0, BS1.1, BS1.2, BS1.2a, BS1.2b} @@ -143,7 +144,7 @@ run_mcmc <- function(model, ...) { UseMethod("run_mcmc", model) } -#' @method run_mcmc gaussian +#' @method run_mcmc lineargaussian #' @rdname run_mcmc #' @export #' @examples @@ -156,7 +157,7 @@ run_mcmc <- function(model, ...) { #' #' sumr <- summary(mcmc_results, variable = "states") #' library("ggplot2") -#' sumr %>% ggplot(aes(time, Mean)) + +#' ggplot(sumr, aes(time, Mean)) + #' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.25) + #' geom_line() + theme_bw() + #' geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), @@ -166,7 +167,7 @@ run_mcmc <- function(model, ...) { #' model$theta[] <- mcmc_results$theta[nrow(mcmc_results$theta), ] #' run_more <- run_mcmc(model, S = mcmc_results$S, iter = 1000, burnin = 0) #' -run_mcmc.gaussian <- function(model, iter, output_type = "full", +run_mcmc.lineargaussian <- function(model, iter, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), @@ -182,6 +183,7 @@ run_mcmc.gaussian <- function(model, iter, output_type = "full", thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) + if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { verbose <- is_interactive() @@ -407,8 +409,9 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") thin <- check_intmax(thin, "thin", max = 100) - iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_intmax(burnin, "burnin", max = 1e12) + iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e10) + burnin <- check_intmax(burnin, "burnin", max = 1e10) + if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { verbose <- is_interactive() @@ -579,6 +582,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) + if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { verbose <- is_interactive() @@ -747,6 +751,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) burnin <- check_intmax(burnin, "burnin", max = 1e12) + if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { verbose <- is_interactive() diff --git a/R/sim_smoother.R b/R/sim_smoother.R index 83b46e45..6394a989 100644 --- a/R/sim_smoother.R +++ b/R/sim_smoother.R @@ -26,19 +26,24 @@ sim_smoother <- function(model, nsim, seed, use_antithetic = TRUE, ...) { UseMethod("sim_smoother", model) } -#' @method sim_smoother gaussian +#' @method sim_smoother lineargaussian #' @rdname sim_smoother #' @export -sim_smoother.gaussian <- function(model, nsim = 1, +sim_smoother.lineargaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { check_missingness(model) - + nsim <- check_intmax(nsim, "nsim") seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) if (!test_flag(use_antithetic)) stop("Argument 'use_antithetic' should be TRUE or FALSE. ") - + nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + length(model$a1) * nsim + if (nsim > 100 & nsamples > 1e10) { + warning(paste("Trying to sample ", nsamples, + "particles, you might run out of memory.")) + } out <- gaussian_sim_smoother(model, nsim, use_antithetic, seed, model_type(model)) rownames(out) <- names(model$a1) @@ -50,11 +55,6 @@ sim_smoother.gaussian <- function(model, nsim = 1, sim_smoother.nongaussian <- function(model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), use_antithetic = TRUE, ...) { - nsim <- check_intmax(nsim, "nsim") - seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - if (!test_flag(use_antithetic)) - stop("Argument 'use_antithetic' should be TRUE or FALSE. ") - sim_smoother(gaussian_approx(model), nsim = nsim, use_antithetic = use_antithetic, seed = seed) } diff --git a/R/smoother.R b/R/smoother.R index 882edd61..3770c5f7 100644 --- a/R/smoother.R +++ b/R/smoother.R @@ -16,7 +16,7 @@ fast_smoother <- function(model, ...) { UseMethod("fast_smoother", model) } -#' @method fast_smoother gaussian +#' @method fast_smoother lineargaussian #' @rdname smoother #' @export #' @examples @@ -25,7 +25,7 @@ fast_smoother <- function(model, ...) { #' sd_y = tnormal(50, 50, 25, min = 0), #' a1 = 1000, P1 = 200) #' ts.plot(cbind(Nile, fast_smoother(model)), col = 1:2) -fast_smoother.gaussian <- function(model, ...) { +fast_smoother.lineargaussian <- function(model, ...) { check_missingness(model) @@ -45,7 +45,7 @@ fast_smoother.nongaussian <- function(model, ...) { smoother <- function(model, ...) { UseMethod("smoother", model) } -#' @method smoother gaussian +#' @method smoother lineargaussian #' @rdname smoother #' @export #' @examples @@ -57,7 +57,7 @@ smoother <- function(model, ...) { #' out <- smoother(model) #' ts.plot(cbind(Nile, out$alphahat), col = 1:2) #' ts.plot(sqrt(out$Vt[1, 1, ])) -smoother.gaussian <- function(model, ...) { +smoother.lineargaussian <- function(model, ...) { check_missingness(model) diff --git a/R/summary.R b/R/summary.R index 2560c5de..2c9b11ed 100644 --- a/R/summary.R +++ b/R/summary.R @@ -16,12 +16,12 @@ #' @param return_se if \code{FALSE} (default), computation of standard #' errors and effective sample sizes is omitted (as they can take considerable #' time for models with large number of states and time points). -#' @param probs Numeric vector defining the quantiles of interest. Default is +#' @param probs A numeric vector defining the quantiles of interest. Default is #' \code{c(0.025, 0.975)}. -#' @param times Vector of indices. For states, for what time points the +#' @param times A vector of indices. For states, for what time points the #' summaries should be computed? Default is all, ignored if #' \code{variable = "theta"}. -#' @param states Vector of indices. For what states the summaries should be +#' @param states A vector of indices. For what states the summaries should be #' computed?. Default is all, ignored if #' \code{variable = "theta"}. #' @param method Method for computing integrated autocorrelation time. Default @@ -89,7 +89,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sumr_theta <- as.data.frame(object, variable = "theta", expand = TRUE) %>% group_by(.data$variable) %>% - summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) %>% + as.data.frame() if (variable == "theta") return(sumr_theta) } @@ -118,7 +119,8 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", as.data.frame(object, variable = "states", expand = TRUE, times = times, states = states, use_times = use_times) %>% group_by(.data$variable, .data$time) %>% - summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) %>% + as.data.frame() if (variable == "states") return(sumr_states) } list(theta = sumr_theta, states = sumr_states) diff --git a/man/ar1_lg.Rd b/man/ar1_lg.Rd index 3038e24c..106bed3a 100644 --- a/man/ar1_lg.Rd +++ b/man/ar1_lg.Rd @@ -7,12 +7,12 @@ ar1_lg(y, rho, sigma, mu, sd_y, beta, xreg = NULL) } \arguments{ -\item{y}{Vector or a \code{ts} object of observations.} +\item{y}{A vector or a \code{ts} object of observations.} -\item{rho}{Prior for autoregressive coefficient. +\item{rho}{A prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for the standard deviation of noise of the AR-process. +\item{sigma}{A prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}} \item{mu}{A fixed value or a prior for the stationary mean of the latent @@ -21,15 +21,16 @@ value defining a fixed mean such as 0.} \item{sd_y}{A prior for the standard deviation of observation equation.} -\item{beta}{Prior for the regression coefficients. +\item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the -length of \code{y}.} +\item{xreg}{A matrix containing covariates with number of rows matching the +length of \code{y}. Can also be \code{ts}, \code{mts} or similar object +convertible to matrix.} } \value{ -Object of class \code{ar1_lg}. +An object of class \code{ar1_lg}. } \description{ Constructs a simple Gaussian model where the state dynamics diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 6bac2bf0..098aebc8 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -7,12 +7,12 @@ ar1_ng(y, rho, sigma, mu, distribution, phi, u, beta, xreg = NULL) } \arguments{ -\item{y}{Vector or a \code{ts} object of observations.} +\item{y}{A vector or a \code{ts} object of observations.} -\item{rho}{Prior for autoregressive coefficient. +\item{rho}{A prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for the standard deviation of noise of the AR-process. +\item{sigma}{A prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}} \item{mu}{A fixed value or a prior for the stationary mean of the latent @@ -29,19 +29,20 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{beta}{Prior for the regression coefficients. +\item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the -length of \code{y}.} +\item{xreg}{A matrix containing covariates with number of rows matching the +length of \code{y}. Can also be \code{ts}, \code{mts} or similar object +convertible to matrix.} } \value{ -Object of class \code{ar1_ng}. +An object of class \code{ar1_ng}. } \description{ Constructs a simple non-Gaussian model where the state dynamics follow an diff --git a/man/as.data.frame.mcmc_output.Rd b/man/as.data.frame.mcmc_output.Rd index f69bcdd0..816f6011 100644 --- a/man/as.data.frame.mcmc_output.Rd +++ b/man/as.data.frame.mcmc_output.Rd @@ -26,10 +26,10 @@ \item{variable}{Return samples of \code{"theta"} (default) or \code{"states"}?} -\item{times}{Vector of indices. In case of states, +\item{times}{A vector of indices. In case of states, what time points to return? Default is all.} -\item{states}{Vector of indices. In case of states, +\item{states}{A vector of indices. In case of states, what states to return? Default is all.} \item{expand}{Should the jump-chain be expanded? diff --git a/man/as_bssm.Rd b/man/as_bssm.Rd index 802677d9..2670e054 100644 --- a/man/as_bssm.Rd +++ b/man/as_bssm.Rd @@ -16,7 +16,7 @@ used to replace exact diffuse elements of the original model.} (such as prior and updating functions, C, and D).} } \value{ -Object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or +An object of class \code{ssm_ulg}, \code{ssm_mlg}, \code{ssm_ung} or \code{ssm_mng}. } \description{ diff --git a/man/as_draws-mcmc_output.Rd b/man/as_draws-mcmc_output.Rd index 60dd922f..da04b33a 100644 --- a/man/as_draws-mcmc_output.Rd +++ b/man/as_draws-mcmc_output.Rd @@ -14,10 +14,10 @@ \arguments{ \item{x}{An object of class \code{mcmc_output}.} -\item{times}{Vector of indices defining which time points to return? +\item{times}{A vector of indices defining which time points to return? Default is all.} -\item{states}{Vector of indices defining which states to return. +\item{states}{A vector of indices defining which states to return. Default is all.} \item{...}{Ignored.} diff --git a/man/asymptotic_var.Rd b/man/asymptotic_var.Rd index 896a9a7f..5c8428ef 100644 --- a/man/asymptotic_var.Rd +++ b/man/asymptotic_var.Rd @@ -15,6 +15,9 @@ weighting is assumed).} \item{method}{Method for computing IACT. Default is \code{"sokal"}, other option \code{"geyer"}.} } +\value{ +A single numeric value of asymptotic variance estimate. +} \description{ The asymptotic variance MCMCSE^2 is based on Corollary 1 of Vihola et al. (2020) from weighted samples from IS-MCMC. The default diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index cb361adf..3a468aa9 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/bootstrap_filter.R \name{bootstrap_filter} \alias{bootstrap_filter} -\alias{bootstrap_filter.gaussian} +\alias{bootstrap_filter.lineargaussian} \alias{bootstrap_filter.nongaussian} \alias{bootstrap_filter.ssm_nlg} \alias{bootstrap_filter.ssm_sde} @@ -10,7 +10,7 @@ \usage{ bootstrap_filter(model, particles, ...) -\method{bootstrap_filter}{gaussian}( +\method{bootstrap_filter}{lineargaussian}( model, particles, seed = sample(.Machine$integer.max, size = 1), diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index 03f93dc0..a0f53237 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -12,7 +12,7 @@ bsm_lg( sd_seasonal, beta, xreg = NULL, - period = frequency(y), + period, a1 = NULL, P1 = NULL, D = NULL, @@ -20,10 +20,11 @@ bsm_lg( ) } \arguments{ -\item{y}{Vector or a \code{ts} object of observations.} +\item{y}{A vector or a \code{ts} object of observations.} \item{sd_y}{Standard deviation of the noise of observation equation. -Should be an object of class \code{bssm_prior} or scalar.} +Should be an object of class \code{bssm_prior} or scalar +value defining a known value such as 0.} \item{sd_level}{Standard deviation of the noise of level equation. Should be an object of class \code{bssm_prior} or scalar @@ -39,29 +40,33 @@ Should be an object of class \code{bssm_prior}, scalar value defining a known value such as 0, or missing, in which case the seasonal term is omitted from the model.} -\item{beta}{Prior for the regression coefficients. +\item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the -length of \code{y}.} +\item{xreg}{A matrix containing covariates with number of rows matching the +length of \code{y}. Can also be \code{ts}, \code{mts} or similar object +convertible to matrix.} \item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be a positive integer greater than 2 -and less than the length of the input time series.} +Must be a positive value greater than 2 and less than the length of the +input time series. Default is \code{frequency(y)}, +which can also return non-integer value (in which case error is given).} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +\item{P1}{Prior covariance matrix for the initial states (level, slope, seasonals). Default is diagonal matrix with 1000 on the diagonal.} -\item{D, C}{Intercept terms for observation and -state equations, given as a length n vector and m times n matrix -respectively (or scalar and m times 1 matrix).} +\item{D}{Intercept terms for observation equation, given as a length n +numeric vector or a scalar in case of time-invariant intercept.} + +\item{C}{Intercept terms for state equation, given as a m times n matrix +or m times 1 matrix in case of time-invariant intercept.} } \value{ -Object of class \code{bsm_lg}. +An object of class \code{bsm_lg}. } \description{ Constructs a basic structural model with local level or local trend @@ -69,21 +74,8 @@ component and seasonal component. } \examples{ -prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) -# period here is redundant as frequency(UKgas) = 4 -model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, - sd_slope = prior, sd_seasonal = prior, period = 4) - -# Note small number of iterations for CRAN checks -mcmc_out <- run_mcmc(model, iter = 5000) -summary(mcmc_out, return_se = TRUE) -# Use the summary method from coda: -summary(expand_sample(mcmc_out, "theta"))$stat -mcmc_out$theta[which.max(mcmc_out$posterior), ] -sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] - set.seed(1) -n <- 10 +n <- 50 x <- rnorm(n) level <- numeric(n) level[1] <- rnorm(1) @@ -95,4 +87,17 @@ model <- bsm_lg(y, sd_y = halfnormal(1, 5), sd_level = 0.1, a1 = level[1], ts.plot(cbind(fast_smoother(model), level), col = 1:2) +prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) +# period here is redundant as frequency(UKgas) = 4 +model_UKgas <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, + sd_slope = prior, sd_seasonal = prior, period = 4) + +# Note small number of iterations for CRAN checks +mcmc_out <- run_mcmc(model_UKgas, iter = 5000) +summary(mcmc_out, return_se = TRUE) +# Use the summary method from coda: +summary(expand_sample(mcmc_out, "theta"))$stat +mcmc_out$theta[which.max(mcmc_out$posterior), ] +sqrt((fit <- StructTS(log10(UKgas), type = "BSM"))$coef)[c(4, 1:3)] + } diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 6a134c9b..7f53cce4 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -15,14 +15,14 @@ bsm_ng( u, beta, xreg = NULL, - period = frequency(y), + period, a1 = NULL, P1 = NULL, C = NULL ) } \arguments{ -\item{y}{Vector or a \code{ts} object of observations.} +\item{y}{A vector or a \code{ts} object of observations.} \item{sd_level}{Standard deviation of the noise of level equation. Should be an object of class \code{bssm_prior} or scalar @@ -38,7 +38,7 @@ Should be an object of class \code{bssm_prior}, scalar value defining a known value such as 0, or missing, in which case the seasonal term is omitted from the model.} -\item{sd_noise}{Prior for the standard deviation of the additional noise +\item{sd_noise}{A prior for the standard deviation of the additional noise term to be added to linear predictor, defined as an object of class \code{bssm_prior}. If missing, no additional noise term is used.} @@ -52,32 +52,34 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} -\item{beta}{Prior for the regression coefficients. +\item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} (in case of multiple coefficients) or missing in case of no covariates.} -\item{xreg}{Matrix containing covariates with number of rows matching the -length of \code{y}.} +\item{xreg}{A matrix containing covariates with number of rows matching the +length of \code{y}. Can also be \code{ts}, \code{mts} or similar object +convertible to matrix.} \item{period}{Length of the seasonal pattern. -Default is \code{frequency(y)}. Must be a positive integer greater than 2 -and less than the length of the input time series.} +Must be a positive value greater than 2 and less than the length of the +input time series. Default is \code{frequency(y)}, +which can also return non-integer value (in which case error is given).} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} -\item{P1}{Prior covariance for the initial states (level, slope, seasonals). +\item{P1}{Prior covariance matrix for the initial states (level, slope, seasonals). Default is diagonal matrix with 1000 on the diagonal.} \item{C}{Intercept terms for state equation, given as a m x n or m x 1 matrix.} } \value{ -Object of class \code{bsm_ng}. +An object of class \code{bsm_ng}. } \description{ Constructs a non-Gaussian basic structural model with local level or @@ -107,7 +109,7 @@ model <- bsm_ng(Seatbelts[, "VanKilled"], distribution = "poisson", beta = normal(0, 0, 10), xreg = Seatbelts[, "law"], # default values, just for illustration - period = 12, + period = 12L, a1 = rep(0, 1 + 11), # level + period - 1 seasonal states P1 = diag(1, 12), C = matrix(0, 12, 1), diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index fc90d25b..c64b20ce 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -77,8 +77,8 @@ are just for consistent naming). uniform(init = 0.2, min = -1.0, max = 1.0) # two normal priors at once i.e. for coefficients beta: normal(init = c(0.1, 2.5), mean = 0.1, sd = c(1.5, 2.8)) -# Gamma prior -gamma(init = 0.1, shape = 2.5, rate = 1.1) +# Gamma prior (not run because autotest tests complain) +# gamma(init = 0.1, shape = 2.5, rate = 1.1) # Same as gamma_prior(init = 0.1, shape = 2.5, rate = 1.1) # Half-normal @@ -86,19 +86,21 @@ halfnormal(init = 0.01, sd = 0.1) # Truncated normal tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) -\dontshow{ + # Further examples for diagnostic purposes: uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) halfnormal(c(0, 0.2), c(1.0, 1.2)) -gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) +# not run because autotest bug +# gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) # longer versions: -uniform_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -normal_prior(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) -tnormal_prior(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) -halfnormal_prior(c(0, 0.2), c(1.0, 1.2)) -gamma_prior(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) -} +uniform_prior(init = c(0, 0.2), min = c(-1.0, 0.001), max = c(1.0, 1.2)) +normal_prior(init = c(0, 0.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2)) +tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), + min = c(1.2, 2), max = 3.3) +halfnormal_prior(init = c(0, 0.2), sd = c(1.0, 1.2)) +gamma_prior(init = c(0.1, 0.2), shape = c(1.2, 2), rate = c(3.3, 3.3)) + } diff --git a/man/estimate_ess.Rd b/man/estimate_ess.Rd index 99303889..16d1f6df 100644 --- a/man/estimate_ess.Rd +++ b/man/estimate_ess.Rd @@ -15,6 +15,9 @@ weighting is assumed).} \item{method}{Method for computing the ESS. Default is \code{"sokal"}, other option are \code{"geyer"} (see also \code{asymptotic_var}).} } +\value{ +A single numeric value of effective sample size estimate. +} \description{ Computes the effective sample size (ESS) based on weighted posterior samples. diff --git a/man/expand_sample.Rd b/man/expand_sample.Rd index 329e1878..eb86a482 100644 --- a/man/expand_sample.Rd +++ b/man/expand_sample.Rd @@ -11,10 +11,10 @@ expand_sample(x, variable = "theta", times, states, by_states = TRUE) \item{variable}{Expand parameters \code{"theta"} or states \code{"states"}.} -\item{times}{Vector of indices. In case of states, +\item{times}{A vector of indices. In case of states, what time points to expand? Default is all.} -\item{states}{Vector of indices. In case of states, +\item{states}{A vector of indices. In case of states, what states to expand? Default is all.} \item{by_states}{If \code{TRUE} (default), return list by states. diff --git a/man/iact.Rd b/man/iact.Rd index 4a559250..79d40b2a 100644 --- a/man/iact.Rd +++ b/man/iact.Rd @@ -9,6 +9,9 @@ iact(x) \arguments{ \item{x}{A numeric vector.} } +\value{ +A single numeric value of IACT estimate. +} \description{ Estimates the integrated autocorrelation time (IACT) based on Sokal (1997). Note that the estimator is not particularly good for very short series x @@ -16,12 +19,11 @@ Note that the estimator is not particularly good for very short series x } \examples{ set.seed(1) -n <- 1e4 +n <- 1000 x <- numeric(n) phi <- 0.8 for(t in 2:n) x[t] <- phi * x[t-1] + rnorm(1) -# ESS estimate: -length(x) / iact(x) +iact(x) } \references{ Sokal A. (1997) Monte Carlo Methods in Statistical Mechanics: Foundations diff --git a/man/kfilter.Rd b/man/kfilter.Rd index 4791109f..edd849c0 100644 --- a/man/kfilter.Rd +++ b/man/kfilter.Rd @@ -2,18 +2,18 @@ % Please edit documentation in R/kfilter.R \name{kfilter} \alias{kfilter} -\alias{kfilter.gaussian} +\alias{kfilter.lineargaussian} \alias{kfilter.nongaussian} \title{Kalman Filtering} \usage{ kfilter(model, ...) -\method{kfilter}{gaussian}(model, ...) +\method{kfilter}{lineargaussian}(model, ...) \method{kfilter}{nongaussian}(model, ...) } \arguments{ -\item{model}{Model of class \code{gaussian}, \code{nongaussian} or +\item{model}{Model of class \code{lineargaussian}, \code{nongaussian} or \code{ssm_nlg}.} \item{...}{Ignored.} diff --git a/man/logLik_bssm.Rd b/man/logLik_bssm.Rd index 97485d8f..0422850c 100644 --- a/man/logLik_bssm.Rd +++ b/man/logLik_bssm.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/loglik.R -\name{logLik.gaussian} -\alias{logLik.gaussian} +\name{logLik.lineargaussian} +\alias{logLik.lineargaussian} \alias{logLik.nongaussian} \alias{logLik.ssm_nlg} \alias{logLik.ssm_sde} \title{Extract Log-likelihood of a State Space Model of class \code{bssm_model}} \usage{ -\method{logLik}{gaussian}(object, ...) +\method{logLik}{lineargaussian}(object, ...) \method{logLik}{nongaussian}( object, @@ -70,6 +70,9 @@ used with \code{iekf_iter} iterations.} \item{L}{Integer defining the discretization level defined as (2^L).} } +\value{ +A numeric value. +} \description{ Computes the log-likelihood of a state space model defined by \code{bssm} package. diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 776b1e9c..67434c16 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/particle_smoother.R \name{particle_smoother} \alias{particle_smoother} -\alias{particle_smoother.gaussian} +\alias{particle_smoother.lineargaussian} \alias{particle_smoother.nongaussian} \alias{particle_smoother.ssm_nlg} \alias{particle_smoother.ssm_sde} @@ -10,7 +10,7 @@ \usage{ particle_smoother(model, particles, ...) -\method{particle_smoother}{gaussian}( +\method{particle_smoother}{lineargaussian}( model, particles, method = "psi", diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 5d013167..e2ecc7db 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -44,7 +44,7 @@ replicable results you should call \code{set.seed} before \code{predict}.} \item{...}{Ignored.} } \value{ -A \code{data.frame} consisting of samples from the predictive +A data.frame consisting of samples from the predictive posterior distribution. } \description{ @@ -67,7 +67,7 @@ future_model$y <- ts(rep(NA, 25), start = tsp(model$y)[2] + 2 * deltat(model$y), frequency = frequency(model$y)) # use "state" for illustrative purposes, we could use type = "mean" directly -pred <- predict(mcmc_results, future_model, type = "state", +pred <- predict(mcmc_results, future_model = future_model, type = "state", nsim = 1000) library("dplyr") @@ -110,9 +110,9 @@ rbind(sumr_fit, sumr_pred) \%>\% time = time(JohnsonJohnson))) # Posterior predictions for past observations: -yrep <- predict(mcmc_results, model, type = "response", +yrep <- predict(mcmc_results, future_model = model, type = "response", future = FALSE, nsim = 1000) -meanrep <- predict(mcmc_results, model, type = "mean", +meanrep <- predict(mcmc_results, future_model = model, type = "mean", future = FALSE, nsim = 1000) sumr_yrep <- yrep \%>\% @@ -130,7 +130,8 @@ sumr_meanrep <- meanrep \%>\% mutate(interval = "Mean") rbind(sumr_meanrep, sumr_yrep) \%>\% - mutate(interval = factor(interval, levels = c("Observations", "Mean"))) \%>\% + mutate(interval = + factor(interval, levels = c("Observations", "Mean"))) \%>\% ggplot(aes(x = time, y = earnings)) + geom_ribbon(aes(ymin = lwr, ymax = upr, fill = interval), alpha = 0.75) + diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index b301422c..4a8b3472 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/run_mcmc.R \name{run_mcmc} \alias{run_mcmc} -\alias{run_mcmc.gaussian} +\alias{run_mcmc.lineargaussian} \alias{run_mcmc.nongaussian} \alias{run_mcmc.ssm_nlg} \alias{run_mcmc.ssm_sde} @@ -10,7 +10,7 @@ \usage{ run_mcmc(model, ...) -\method{run_mcmc}{gaussian}( +\method{run_mcmc}{lineargaussian}( model, iter, output_type = "full", @@ -95,7 +95,7 @@ run_mcmc(model, ...) \item{...}{Ignored.} -\item{iter}{Positive integer defining the total number of MCMC iterations.} +\item{iter}{A positive integer defining the total number of MCMC iterations.} \item{output_type}{Either \code{"full"} (default, returns posterior samples from the posterior @@ -103,12 +103,12 @@ run_mcmc(model, ...) theta), or \code{"summary"} (return the mean and variance estimates of the states and posterior samples of theta). See details.} -\item{burnin}{Positive integer defining the length of the burn-in period +\item{burnin}{A positive integer defining the length of the burn-in period which is disregarded from the results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the burn-in period in order to find good proposal distribution.} -\item{thin}{Positive integer defining the thinning rate. All MCMC algorithms +\item{thin}{A positive integer defining the thinning rate. All MCMC algorithms in \code{bssm} use the jump chain representation (see refs), and the thinning is applied to these blocks. Defaults to 1. For IS-corrected methods, larger value can also be @@ -144,8 +144,8 @@ models.} missing, defined by \code{rlang::is_interactive}. Set to \code{FALSE} if number of iterations is less than 50.} -\item{particles}{Number of state samples per MCMC iteration for models other -than linear-Gaussian models. +\item{particles}{A positive integer defining the number of state samples per +MCMC iteration for models other than linear-Gaussian models. Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} \item{mcmc_type}{What type of MCMC algorithm should be used for models other @@ -191,6 +191,9 @@ with \code{iekf_iter} iterations. Used only for models of class the discretization levels for first and second stages (defined as 2^L). For pseudo-marginal methods (\code{"pm"}), maximum of these is used.} } +\value{ +An object of class \code{mcmc_output}. +} \description{ Adaptive Markov chain Monte Carlo simulation for SSMs using Robust Adaptive Metropolis algorithm by Vihola (2012). Several different @@ -231,7 +234,7 @@ summary(mcmc_results, return_se = TRUE) sumr <- summary(mcmc_results, variable = "states") library("ggplot2") -sumr \%>\% ggplot(aes(time, Mean)) + +ggplot(sumr, aes(time, Mean)) + geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`), alpha = 0.25) + geom_line() + theme_bw() + geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 838ca01f..01b636e5 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -2,13 +2,13 @@ % Please edit documentation in R/sim_smoother.R \name{sim_smoother} \alias{sim_smoother} -\alias{sim_smoother.gaussian} +\alias{sim_smoother.lineargaussian} \alias{sim_smoother.nongaussian} \title{Simulation Smoothing} \usage{ sim_smoother(model, nsim, seed, use_antithetic = TRUE, ...) -\method{sim_smoother}{gaussian}( +\method{sim_smoother}{lineargaussian}( model, nsim = 1, seed = sample(.Machine$integer.max, size = 1), diff --git a/man/smoother.Rd b/man/smoother.Rd index c41fd10c..c21cab58 100644 --- a/man/smoother.Rd +++ b/man/smoother.Rd @@ -2,18 +2,18 @@ % Please edit documentation in R/smoother.R \name{fast_smoother} \alias{fast_smoother} -\alias{fast_smoother.gaussian} +\alias{fast_smoother.lineargaussian} \alias{smoother} -\alias{smoother.gaussian} +\alias{smoother.lineargaussian} \title{Kalman Smoothing} \usage{ fast_smoother(model, ...) -\method{fast_smoother}{gaussian}(model, ...) +\method{fast_smoother}{lineargaussian}(model, ...) smoother(model, ...) -\method{smoother}{gaussian}(model, ...) +\method{smoother}{lineargaussian}(model, ...) } \arguments{ \item{model}{Model to be approximated. Should be of class diff --git a/man/ssm_mlg.Rd b/man/ssm_mlg.Rd index e7822cd1..a87bb895 100644 --- a/man/ssm_mlg.Rd +++ b/man/ssm_mlg.Rd @@ -47,9 +47,9 @@ or a m x k x n array.} \item{C}{Intercept terms for state equation, given as m x n matrix.} -\item{state_names}{Names for the states.} +\item{state_names}{A character vector defining the names of the states.} -\item{update_fn}{Function which returns list of updated model +\item{update_fn}{A function which returns list of updated model components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and @@ -59,11 +59,11 @@ the dimensions of input arguments can differ from the final dimensions. If any of these components is missing, it is assumed to be constant wrt. theta.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{A function which returns log of prior density given input vector theta.} } \value{ -Object of class \code{ssm_mlg}. +An object of class \code{ssm_mlg}. } \description{ Construct an object of class \code{ssm_mlg} by directly defining the diff --git a/man/ssm_mng.Rd b/man/ssm_mng.Rd index a125425d..ba1c7423 100644 --- a/man/ssm_mng.Rd +++ b/man/ssm_mng.Rd @@ -40,7 +40,7 @@ m x k x n array.} \item{P1}{Prior covariance matrix for the initial state as m x m matrix.} -\item{distribution}{vector of distributions of the observed series. +\item{distribution}{A vector of distributions of the observed series. Possible choices are \code{"poisson"}, \code{"binomial"}, \code{"negative binomial"}, \code{"gamma"}, and \code{"gaussian"}.} @@ -50,10 +50,10 @@ For negative binomial distribution this is the dispersion term, for gamma distribution this is the shape parameter, for Gaussian this is standard deviation, and for other distributions this is ignored.} -\item{u}{Matrix of positive constants for non-Gaussian models +\item{u}{A matrix of positive constants for non-Gaussian models (of same dimensions as y). For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the -number of trials.} +number of trials (and as such should be integer(ish)).} \item{init_theta}{Initial values for the unknown hyperparameters theta (i.e. unknown variables excluding latent state variables).} @@ -62,9 +62,9 @@ number of trials.} \item{C}{Intercept terms for state equation, given as m x n matrix.} -\item{state_names}{Names for the states.} +\item{state_names}{A character vector defining the names of the states.} -\item{update_fn}{Function which returns list of updated model +\item{update_fn}{A function which returns list of updated model components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and @@ -74,11 +74,11 @@ theta. It's best to check the internal dimensions with \code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{A function which returns log of prior density given input vector theta.} } \value{ -Object of class \code{ssm_mng}. +An object of class \code{ssm_mng}. } \description{ Construct an object of class \code{ssm_mng} by directly defining the @@ -135,5 +135,5 @@ model <- ssm_mng(y, Z, T, R, a1, P1, phi = c(2, 1), # smoothing based on approximating gaussian model ts.plot(cbind(y, fast_smoother(model)), col = 1:3, lty = c(1, 1, 2)) - + } diff --git a/man/ssm_nlg.Rd b/man/ssm_nlg.Rd index d165205d..cbf8b6fe 100644 --- a/man/ssm_nlg.Rd +++ b/man/ssm_nlg.Rd @@ -43,10 +43,10 @@ functions.} \item{theta}{Parameter vector passed to all model functions.} -\item{known_params}{Vector of known parameters passed to all model +\item{known_params}{A vector of known parameters passed to all model functions.} -\item{known_tv_params}{Matrix of known parameters passed to all model +\item{known_tv_params}{A matrix of known parameters passed to all model functions.} \item{n_states}{Number of states in the model (positive integer).} @@ -63,10 +63,10 @@ the values of Z, H, T, and R vary with respect to time variable (given identical states). If used, this can speed up some computations.} -\item{state_names}{Vector containing names for the states.} +\item{state_names}{A character vector containing names for the states.} } \value{ -Object of class \code{ssm_nlg}. +An object of class \code{ssm_nlg}. } \description{ Constructs an object of class \code{ssm_nlg} by defining the corresponding diff --git a/man/ssm_sde.Rd b/man/ssm_sde.Rd index eedd5eec..2e3642f8 100644 --- a/man/ssm_sde.Rd +++ b/man/ssm_sde.Rd @@ -39,7 +39,7 @@ computes the prior log-density given the parameter vector theta.} forced by \code{abs} in Milstein scheme.} } \value{ -Object of class \code{ssm_sde}. +An object of class \code{ssm_sde}. } \description{ Constructs an object of class \code{ssm_sde} by defining the functions for diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 17b673f4..932a0c80 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -27,7 +27,7 @@ ssm_ulg( vector of length m, a m x n matrix, or object which can be coerced to such.} -\item{H}{Vector of standard deviations. Either a scalar or a vector of +\item{H}{A vector of standard deviations. Either a scalar or a vector of length n.} \item{T}{System matrix T of the state equation. Either a m x m matrix or a @@ -49,9 +49,9 @@ scalar or vector of length n.} \item{C}{Intercept terms \eqn{C_t} for the state equation, given as a m times 1 or m times n matrix.} -\item{state_names}{Names for the states.} +\item{state_names}{A character vector defining the names of the states.} -\item{update_fn}{Function which returns list of updated model +\item{update_fn}{A function which returns list of updated model components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{H}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, and @@ -61,11 +61,11 @@ the dimensions of input arguments can differ from the final dimensions. If any of these components is missing, it is assumed to be constant wrt. theta.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{A function which returns log of prior density given input vector theta.} } \value{ -Object of class \code{ssm_ulg}. +An object of class \code{ssm_ulg}. } \description{ Construct an object of class \code{ssm_ulg} by directly defining the diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 415a01a8..3cc0463c 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -49,7 +49,7 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{Vector of positive constants for non-Gaussian models. For Poisson, +\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.} @@ -62,9 +62,9 @@ scalar or vector of length n.} \item{C}{Intercept terms \eqn{C_t} for the state equation, given as a m times 1 or m times n matrix.} -\item{state_names}{Names for the states.} +\item{state_names}{A character vector defining the names of the states.} -\item{update_fn}{Function which returns list of updated model +\item{update_fn}{A function which returns list of updated model components given input vector theta. This function should take only one vector argument which is used to create list with elements named as \code{Z}, \code{T}, \code{R}, \code{a1}, \code{P1}, \code{D}, \code{C}, and @@ -74,11 +74,11 @@ theta. It's best to check the internal dimensions with \code{str(model_object)} as the dimensions of input arguments can differ from the final dimensions.} -\item{prior_fn}{Function which returns log of prior density +\item{prior_fn}{A function which returns log of prior density given input vector theta.} } \value{ -Object of class \code{ssm_ung}. +An object of class \code{ssm_ung}. } \description{ Construct an object of class \code{ssm_ung} by directly defining the diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index 31f38735..47bb817d 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -21,7 +21,7 @@ estimate from the (approximate) MCMC run. Can also be an output from \code{run_mcmc} which is then used to compute the MAP estimate of theta.} -\item{candidates}{Vector of positive integers containing the candidate +\item{candidates}{A vector of positive integers containing the candidate number of particles to test. Default is \code{seq(10, 100, by = 10)}.} \item{replications}{Positive integer, how many replications should be used diff --git a/man/summary.mcmc_output.Rd b/man/summary.mcmc_output.Rd index d21eddb0..31a3046c 100644 --- a/man/summary.mcmc_output.Rd +++ b/man/summary.mcmc_output.Rd @@ -26,14 +26,14 @@ time for models with large number of states and time points).} \item{variable}{Are the summary statistics computed for either \code{"theta"} (default), \code{"states"}, or \code{"both"}?} -\item{probs}{Numeric vector defining the quantiles of interest. Default is +\item{probs}{A numeric vector defining the quantiles of interest. Default is \code{c(0.025, 0.975)}.} -\item{times}{Vector of indices. For states, for what time points the +\item{times}{A vector of indices. For states, for what time points the summaries should be computed? Default is all, ignored if \code{variable = "theta"}.} -\item{states}{Vector of indices. For what states the summaries should be +\item{states}{A vector of indices. For what states the summaries should be computed?. Default is all, ignored if \code{variable = "theta"}.} diff --git a/man/svm.Rd b/man/svm.Rd index d075cf3c..7f047940 100644 --- a/man/svm.Rd +++ b/man/svm.Rd @@ -7,18 +7,18 @@ svm(y, mu, rho, sd_ar, sigma) } \arguments{ -\item{y}{Vector or a \code{\link{ts}} object of observations.} +\item{y}{A numeric vector or a \code{\link{ts}} object of observations.} -\item{mu}{Prior for mu parameter of transition equation. +\item{mu}{A prior for mu parameter of transition equation. Should be an object of class \code{bssm_prior}.} -\item{rho}{prior for autoregressive coefficient. +\item{rho}{A prior for autoregressive coefficient. Should be an object of class \code{bssm_prior}.} -\item{sd_ar}{Prior for the standard deviation of noise of the AR-process. +\item{sd_ar}{A prior for the standard deviation of noise of the AR-process. Should be an object of class \code{bssm_prior}.} -\item{sigma}{Prior for sigma parameter of observation equation, internally +\item{sigma}{A prior for sigma parameter of observation equation, internally denoted as phi. Should be an object of class \code{bssm_prior}. Ignored if \code{mu} is provided. Note that typically parametrization using mu is preferred due to better numerical properties and @@ -28,7 +28,7 @@ parameterization as sigma is not a parameter of the resulting approximate model.} } \value{ -Object of class \code{svm}. +An object of class \code{svm}. } \description{ Constructs a simple stochastic volatility model with Gaussian errors and @@ -37,12 +37,12 @@ first order autoregressive signal. See the main vignette for details. \examples{ data("exchange") -exchange <- exchange[1:100] # faster CRAN check -model <- svm(exchange, rho = uniform(0.98, -0.999, 0.999), +y <- exchange[1:100] # for faster CRAN check +model <- svm(y, rho = uniform(0.98, -0.999, 0.999), sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2)) obj <- function(pars) { - -logLik(svm(exchange, + -logLik(svm(y, rho = uniform(pars[1], -0.999, 0.999), sd_ar = halfnormal(pars[2], 5), sigma = halfnormal(pars[3], 2)), particles = 0) @@ -51,17 +51,17 @@ opt <- optim(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), upper = c(0.999, 10, 10), method = "L-BFGS-B") pars <- opt$par -model <- svm(exchange, +model <- svm(y, rho = uniform(pars[1],-0.999,0.999), sd_ar = halfnormal(pars[2], 5), sigma = halfnormal(pars[3], 2)) # alternative parameterization -model2 <- svm(exchange, rho = uniform(0.98,-0.999, 0.999), +model2 <- svm(y, rho = uniform(0.98,-0.999, 0.999), sd_ar = halfnormal(0.15, 5), mu = normal(0, 0, 1)) obj2 <- function(pars) { - -logLik(svm(exchange, + -logLik(svm(y, rho = uniform(pars[1], -0.999, 0.999), sd_ar = halfnormal(pars[2], 5), mu = normal(pars[3], 0, 1)), particles = 0) @@ -69,7 +69,7 @@ obj2 <- function(pars) { opt2 <- optim(c(0.98, 0.15, 0), obj2, lower = c(-0.999, 1e-4, -Inf), upper = c(0.999, 10, Inf), method = "L-BFGS-B") pars2 <- opt2$par -model2 <- svm(exchange, +model2 <- svm(y, rho = uniform(pars2[1],-0.999,0.999), sd_ar = halfnormal(pars2[2], 5), mu = normal(pars2[3], 0, 1)) diff --git a/src/mcmc.h b/src/mcmc.h index eb585e0f..e2878d27 100644 --- a/src/mcmc.h +++ b/src/mcmc.h @@ -42,7 +42,7 @@ class mcmc { void state_summary(T model, const Rcpp::Function update_fn = default_update_fn); - // gaussian mcmc + // linear-gaussian mcmc template<class T> void mcmc_gaussian(T model, const bool end_ram, const Rcpp::Function update_fn = default_update_fn, diff --git a/tests/testthat/test_as_data_frame.R b/tests/testthat/test_as_data_frame.R index cdeee5c2..524bdfc1 100644 --- a/tests/testthat/test_as_data_frame.R +++ b/tests/testthat/test_as_data_frame.R @@ -43,6 +43,6 @@ test_that("expanded and not expanded data frame work equally for states", { expect_error(sumr <- summary(mcmc_bsm, variable = "both", return_se = TRUE), NA) expect_equal(mean(d$value[d$variable == "sd_y"]), - sumr["sd_y", "Mean"]) + sumr$theta[2, 2]) }) diff --git a/tests/testthat/test_bootstrap_filter.R b/tests/testthat/test_bootstrap_filter.R index 561563aa..3d52d048 100644 --- a/tests/testthat/test_bootstrap_filter.R +++ b/tests/testthat/test_bootstrap_filter.R @@ -18,7 +18,7 @@ test_that("Test that bsm_lg gives identical results with ssm_ulg", { tol <- 1e-8 -test_that("Test that gaussian bsf still works", { +test_that("Test that linear-gaussian bsf still works", { expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index b7d16792..157db1eb 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -30,8 +30,9 @@ test_that("prior and posterior distributions coincide when no data is used", { # approx is enough here, the weights are uniform when there is no data fit <- run_mcmc(model, iter = 2e5,burnin = 1e4, mcmc_type = "approx") - expect_equivalent(prior_sumr, summary(fit)[, c(1, 3)], tol = 0.1) - + expect_equivalent(prior_sumr, + dplyr::arrange(summary(fit)[, c(2, 3)], order(rownames(prior_sumr))), + tol = 0.1) }) @@ -52,13 +53,13 @@ test_that("MCMC results from bssm paper are still correct", { fit_bssm <- run_mcmc(bssm_model, iter = 6e4, burnin = 1e4, particles = 10, seed = 1) expect_error(sumr_theta <- summary(fit_bssm)[, "Mean"], NA) - paper_theta <- c(0.092, 0.003, 5.392, -0.912) + paper_theta <- c(-0.912, 5.392, 0.092, 0.003) expect_equivalent(sumr_theta, paper_theta, tol = 0.01) expect_error(sumr_alpha <- summary(fit_bssm, variable = "states", times = 200)$Mean, NA) - paper_alpha <- c(6.962,0.006) + paper_alpha <- c(6.962, 0.006) expect_equivalent(sumr_alpha, paper_alpha, tol = 0.01) }) diff --git a/tests/testthat/test_particle_smoother.R b/tests/testthat/test_particle_smoother.R index 855e337e..686c913c 100644 --- a/tests/testthat/test_particle_smoother.R +++ b/tests/testthat/test_particle_smoother.R @@ -125,7 +125,7 @@ test_that("Particle smoother for svm returns finite values", { }) tol <- 1e-8 -test_that("Test that gaussian bsf smoother still works", { +test_that("Test that linear-gaussian bsf smoother still works", { expect_error(model_ssm_ulg <- ssm_ulg(y = 1:10, Z = matrix(c(1, 0), 2, 1), H = 2, T = array(c(1, 0, 1, 1), c(2, 2, 1)), From fac90ee8efd2be114d3ac4f7dcae3da341c8b10f Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 12:23:55 +0200 Subject: [PATCH 143/180] improved documentation and changed predict method --- NEWS | 4 ++ R/bootstrap_filter.R | 9 +++- R/ekpf_filter.R | 3 +- R/importance_sample.R | 8 +++- R/loglik.R | 15 +++--- R/particle_smoother.R | 5 ++ R/post_correction.R | 13 +++-- R/predict.R | 90 +++++++++++++++++++---------------- R/run_mcmc.R | 19 ++++++-- man/bootstrap_filter.Rd | 8 +++- man/ekpf_filter.Rd | 8 +++- man/importance_sample.Rd | 8 +++- man/logLik_bssm.Rd | 17 +++---- man/particle_smoother.Rd | 8 +++- man/post_correct.Rd | 12 +++-- man/predict.mcmc_output.Rd | 11 +++-- man/run_mcmc.Rd | 15 ++++-- man/sim_smoother.Rd | 8 +++- man/suggest_N.Rd | 2 +- src/R_postcorrection.cpp | 4 +- tests/testthat/test_predict.R | 37 ++++++++++++++ 21 files changed, 210 insertions(+), 94 deletions(-) diff --git a/NEWS b/NEWS index 5bf920c6..4b2b8bf4 100644 --- a/NEWS +++ b/NEWS @@ -5,6 +5,10 @@ bssm 2.0.0 (Release date: -) predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. * Rewrote the summary method completely, which now returns data.frame. This also resulted in some changes in order of the function arguments. + * The output of predict method is now a data frame with column weight + corresponding to the IS-weights in case of IS-MCMC. Previously resampling + was done internally, but now this is left for the user if needed + (i.e. for drawing state trajectories). * The asymptotic_var and iact functions are now exported to users, and they also contain alternative methods based on the posterior package. * New function estimate_ess can be used to compute effective sample size diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index f701b97c..e4906041 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -2,9 +2,14 @@ #' #' Function \code{bootstrap_filter} performs a bootstrap filtering with #' stratification resampling. +#' #' @param model A model object of class \code{bssm_model}. -#' @param particles Number of particles as a positive integer. -#' @param seed Seed for RNG (non-negative integer). +#' @param particles Number of particles as a positive integer. Suitable values +#' depend on the model and the data, and while larger values provide more +#' accurate estimates, the run time also increases with respect to the +#' number of particles, so it is generally a good idea to test the filter first +#' with smaller number of particles, say 100. +#' @param seed Seed for the C++ RNG (positive integer). #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the filtering distribution and #' corresponding weights (\code{weights}), as well as filtered and predicted diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 1234127f..654f1ebc 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -3,9 +3,8 @@ #' Function \code{ekpf_filter} performs a extended Kalman particle filtering #' with stratification resampling, based on Van Der Merwe et al (2001). #' +#' @inheritParams bootstrap_filter #' @param model Model of class \code{ssm_nlg}. -#' @param particles Number of particles as a positive integer. -#' @param seed Seed for RNG (positive integer). #' @param ... Ignored. #' @return A list containing samples, filtered estimates and the #' corresponding covariances, weights, and an estimate of log-likelihood. diff --git a/R/importance_sample.R b/R/importance_sample.R index 836355af..d96c814e 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -7,11 +7,15 @@ #' @inheritParams gaussian_approx #' @param model Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, #' \code{ssm_ung}, or \code{ssm_mng}. -#' @param nsim Number of samples (positive integer). +#' @param nsim Number of samples (positive integer). Suitable values +#' depend on the model and the data, and while larger values provide more +#' accurate estimates, the run time also increases with respect to to the +#' number of samples, so it is generally a good idea to test the filter first +#' with smaller number of particles, say 100. #' @param use_antithetic Logical. If \code{TRUE} (default), use antithetic #' variable for location in simulation smoothing. Ignored for \code{ssm_mng} #' models. -#' @param seed Seed for the random number generator (positive integer). +#' @param seed Seed for the C++ RNG (positive integer). #' @param ... Ignored. #' @export #' @rdname importance_sample diff --git a/R/loglik.R b/R/loglik.R index ab9bc9c0..41c8e903 100644 --- a/R/loglik.R +++ b/R/loglik.R @@ -5,15 +5,16 @@ #' #' @inheritParams particle_smoother #' @param object Model of class \code{bssm_model}. -#' @param particles Number of samples for particle filter. If 0, -#' approximate log-likelihood is returned either based on the Gaussian -#' approximation or EKF, depending on the \code{method} argument. +#' @param particles Number of samples for particle filter +#' (non-negative integer). If 0, approximate log-likelihood is returned either +#' based on the Gaussian approximation or EKF, depending on the \code{method} +#' argument. #' @param method Sampling method. For Gaussian and non-Gaussian models with #' linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default -#' for non-linear models) -#' and \code{"psi"} (\eqn{\psi}-APF, the default for other models). -#' For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle -#' filter (or just EKF/IEKF approximation in the case of \code{particles = 0}). +#' for non-linear models) and \code{"psi"} (\eqn{\psi}-APF, the default for +#' other models). For-nonlinear models option \code{"ekf"} +#' uses EKF/IEKF-based particle filter (or just EKF/IEKF approximation in the +#' case of \code{particles = 0}). #' @importFrom stats logLik #' @method logLik lineargaussian #' @rdname logLik_bssm diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 1ae644a6..6394a343 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -19,6 +19,11 @@ #' and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and #' for non-linear models option \code{"ekf"} (extended Kalman particle filter) #' is also available. +#' @param particles Number of particles as a positive integer. Suitable values +#' depend on the model, the data, and the chosen algorithm. While larger values +#' provide more accurate estimates, the run time also increases with respect to +#' the number of particles, so it is generally a good idea to test the filter +#' first with a smaller number of particles, say 10 or 100. #' @param max_iter Maximum number of iterations used in Gaussian approximation, #' as a positive integer. #' Default is 100 (although typically only few iterations are needed). diff --git a/R/post_correction.R b/R/post_correction.R index 00e37a7f..a0642dd7 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -31,7 +31,7 @@ get_map <- function(x) { #' number of particles to test. Default is \code{seq(10, 100, by = 10)}. #' @param replications Positive integer, how many replications should be used #' for computing the standard deviations? Default is 100. -#' @param seed Seed for the random number generator (positive integer). +#' @param seed Seed for the C++ RNG (positive integer). #' @return List with suggested number of particles \code{N} and matrix #' containing estimated standard deviations of the log-weights and #' corresponding number of particles. @@ -147,6 +147,8 @@ suggest_N <- function(model, theta, #' actually checked, i.e., it is also possible to input previous #' (asymptotically) exact output. #' @param particles Number of particles for \eqn{\psi}-APF (positive integer). +#' Suitable values depend on the model and the data, but often relatively +#' small value less than say 50 is enough. See also \code{suggest_N} #' @param threads Number of parallel threads (positive integer, default is 1). #' @param is_type Type of IS-correction. Possible choices are #'\code{"is3"} for simple importance sampling (weight is computed for each @@ -155,10 +157,11 @@ suggest_N <- function(model, theta, #' \code{"is1"} for importance sampling type weighting where the number of #' particles used forweight computations is proportional to the length of the #' jump chain block. -#' @param seed Seed for the random number generator (positive integer). -#' @return List with suggested number of particles \code{N} and matrix -#' containing estimated standard deviations of the log-weights and -#' corresponding number of particles. +#' @param seed Seed for the C++ RNG (positive integer). +#' @return The original object of class \code{mcmc_output} with updated +#' weights, log-posterior values and state samples or summaries (depending on +#' the \code{mcmc_output$mcmc_type}). +#' #' @references #' Doucet A, Pitt M K, Deligiannidis G, Kohn R (2018). #' Efficient implementation of Markov chain Monte Carlo when using an unbiased diff --git a/R/predict.R b/R/predict.R index 426d1643..3aebf75c 100644 --- a/R/predict.R +++ b/R/predict.R @@ -18,14 +18,17 @@ #' \code{future} to \code{FALSE}. #' @param type Type of predictions. Possible choices are #' \code{"mean"} \code{"response"}, or \code{"state"} level. -#' @param nsim Positive integer defining number of samples to draw. +#' @param nsim Positive integer defining number of samples to draw. Should be +#' less than or equal to \code{sum(object$counts)} i.e. the number of samples +#' in the MCMC output. Default is to use all the samples. #' @param future Default is \code{TRUE}, in which case predictions are for the #' future, using posterior samples of (theta, alpha_T+1) i.e. the #' posterior samples of hyperparameters and latest states. #' Otherwise it is assumed that \code{model} corresponds to the original model. -#' @param seed Seed for RNG (positive integer). Note that this affects only the -#' C++ side, and \code{predict} also uses R side RNG for subsampling, so for -#' replicable results you should call \code{set.seed} before \code{predict}. +#' @param seed Seed for the C++ RNG (positive integer). Note that this affects +#' only the C++ side, and \code{predict} also uses R side RNG for subsampling, +#' so for replicable results you should call \code{set.seed} before +#' \code{predict}. #' @param ... Ignored. #' @return A data.frame consisting of samples from the predictive #' posterior distribution. @@ -127,13 +130,6 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", if (!inherits(model, "bssm_model")) { stop("Argument 'model' should be an object of class 'bssm_model'.") } - nsim <- check_intmax(nsim, "nsim", max = 10 * object$iter) - seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - - if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") - - type <- match.arg(tolower(type), c("response", "mean", "state")) - if (object$output_type != 1) stop("MCMC output must contain posterior samples of the states.") @@ -144,7 +140,19 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", stop(paste("Number of unknown parameters 'theta' does not correspond to", "the MCMC output. ", sep = " ")) } - if (nsim < 1) stop("Number of samples 'nsim' should be at least one.") + if (missing(nsim)) { + nsim <- sum(object$counts) + } else { + nsim <- check_intmax(nsim, "nsim", positive = TRUE, max = Inf) + if (nsim > sum(object$count)) + stop(paste0("The number of samples should be smaller than or equal to ", + "the number of posterior samples ", sum(object$counts), ".")) + } + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) + + if (!test_flag(future)) stop("Argument 'future' should be TRUE or FALSE. ") + + type <- match.arg(tolower(type), c("response", "mean", "state")) if (attr(object, "model_type") %in% c("bsm_lg", "bsm_ng")) { object$theta[, 1:(ncol(object$theta) - length(model$beta))] <- @@ -164,15 +172,19 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", } } + idx <- sample(seq_len(sum(object$counts)), size = nsim) + if (object$mcmc_type %in% paste0("is", 1:3)) { + weight <- rep(object$weights, times = object$counts)[idx] + } else { + weight <- rep(1, nsim) + } + theta <- + t(apply(object$theta, 2, rep, times = object$counts)[idx, , drop = FALSE]) + if (future) { - w <- object$counts * - (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, - replace = TRUE) - theta <- t(object$theta[idx, , drop = FALSE]) - alpha <- matrix(object$alpha[nrow(object$alpha), , idx], - nrow = ncol(object$alpha)) + states <- t(apply(object$alpha[nrow(object$alpha), , , drop = FALSE], + 2, rep, object$counts)[idx, , drop = FALSE]) switch(attr(object, "model_type"), ssm_mlg =, @@ -183,7 +195,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", stop(paste("Model does not correspond to the MCMC output:", "Wrong number of states. ", sep = " ")) } - pred <- gaussian_predict(model, theta, alpha, + pred <- gaussian_predict(model, theta, states, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), @@ -203,7 +215,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 - pred <- nongaussian_predict(model, theta, alpha, + pred <- nongaussian_predict(model, theta, states, pmatch(type, c("response", "mean", "state")), seed, pmatch(attr(object, "model_type"), c("ssm_mng", "ssm_ung", "bsm_ng", "svm", "ar1_ng")) - 1L) @@ -222,10 +234,11 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", model$log_prior_pdf, model$known_params, model$known_tv_params, as.integer(model$time_varying), model$n_states, model$n_etas, - theta, alpha, pmatch(type, c("response", "mean", "state")), seed) + theta, states, pmatch(type, c("response", "mean", "state")), seed) } , stop("Not yet implemented for ssm_sde. ")) + if (type == "state") { if (attr(object, "model_type") == "ssm_nlg") { variables <- model$state_names @@ -240,6 +253,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", d <- data.frame(value = as.numeric(pred), variable = variables, time = rep(time(model$y), each = nrow(pred)), + weight = rep(weight, each = nrow(pred) * ncol(pred)), sample = rep(1:nsim, each = nrow(pred) * ncol(pred))) } else { @@ -247,7 +261,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", if (inherits(model, c("ssm_mng", "ssm_mlg", "ssm_nlg"))) { if (!identical(nrow(object$alpha) - 1L, nrow(model$y))) { stop(paste0("Number of observations in the model and MCMC output do ", - "not match.")) + "not match.")) } } else { if (!identical(nrow(object$alpha) - 1L, length(model$y))) { @@ -255,14 +269,10 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", "not match.")) } } - w <- object$counts * - (if (object$mcmc_type %in% paste0("is", 1:3)) object$weights else 1) - idx <- sample(seq_len(nrow(object$theta)), size = nsim, prob = w, - replace = TRUE) - n <- nrow(object$alpha) - 1L - m <- ncol(object$alpha) - states <- object$alpha[1:n, , idx, drop = FALSE] + n <- nrow(object$alpha) - 1L + states <- + apply(object$alpha, 1:2, rep, object$counts)[idx, 1:n, , drop = FALSE] if (type == "state") { if (attr(object, "model_type") == "ssm_nlg") { @@ -271,24 +281,23 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", variables <- names(model$a1) } d <- data.frame(value = as.numeric(states), - variable = rep(variables, each = n), - time = rep(time(model$y), times = m), - sample = rep(1:nsim, each = n * m)) + variable = rep(variables, each = nrow(states) * n), + time = rep(time(model$y), times = nrow(states)), + sample = 1:nsim, weight = weight) } else { variables <- colnames(model$y) if (is.null(variables)) variables <- paste("Series", 1:max(1, ncol(model$y))) - - theta <- t(object$theta[idx, ]) - states <- aperm(states, c(2, 1, 3)) + + states <- aperm(states, 3:1) switch(attr(object, "model_type"), ssm_mlg =, ssm_ulg =, bsm_lg =, ar1_lg = { - if (!identical(length(model$a1), m)) { + if (!identical(length(model$a1), nrow(states))) { stop(paste("Model does not correspond to the MCMC output:", "Wrong number of states. ", sep = " ")) } @@ -304,7 +313,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", bsm_ng =, svm =, ar1_ng = { - if (!identical(length(model$a1), m)) { + if (!identical(length(model$a1), nrow(states))) { stop(paste("Model does not correspond to the MCMC output:", "Wrong number of states. ", sep = " ")) } @@ -321,7 +330,7 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", warning("NA or NaN values in predictions, possible under/overflow?") }, ssm_nlg = { - if (!identical(model$n_states, m)) { + if (!identical(model$n_states, nrow(states))) { stop(paste("Model does not correspond to the MCMC output:", "Wrong number of states. ", sep = " ")) } @@ -339,7 +348,8 @@ predict.mcmc_output <- function(object, model, nsim, type = "response", d <- data.frame(value = as.numeric(pred), variable = variables, time = rep(time(model$y), each = nrow(pred)), - sample = rep(1:nsim, each = nrow(pred) * ncol(pred))) + sample = rep(1:nsim, each = nrow(pred) * ncol(pred)), + weight = rep(weight, each = nrow(pred) * ncol(pred))) } } d diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 5cef419b..95c31185 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -33,7 +33,11 @@ #' @importFrom stats tsp #' @importFrom rlang is_interactive #' @param model Model of class \code{bssm_model}. -#' @param iter A positive integer defining the total number of MCMC iterations. +#' @param iter A positive integer defining the total number of MCMC iterations. +#' Suitable value depends on the model, data, and the choice of specific +#' algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing +#' \code{iter} also increases run time, it is is generally good idea to first +#' test the performance with a smaller values such as 10000. #' @param output_type Either \code{"full"} #' (default, returns posterior samples from the posterior #' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of @@ -67,7 +71,7 @@ #' Note that parallel computing is only used in the post-correction phase of #' IS-MCMC and when sampling the states in case of (approximate) Gaussian #' models. -#' @param seed Seed for the random number generator (positive integer). +#' @param seed Seed for the C++ RNG (positive integer). #' @param local_approx If \code{TRUE} (default), Gaussian approximation #' needed for some of the methods is performed at each iteration. #' If \code{FALSE}, approximation is updated only once at the start of the @@ -78,7 +82,12 @@ #' @param conv_tol Positive tolerance parameter used in Gaussian approximation. #' @param particles A positive integer defining the number of state samples per #' MCMC iteration for models other than linear-Gaussian models. -#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. +#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable +#' values depend on the model, the data, \code{mcmc_type} and +#' \code{sampling_method}. While larger values provide more +#' accurate estimates, the run time also increases with respect to to the +#' number of particles, so it is generally a good idea to test the run time +#' first with smaller number of particles, say 10-100. #' @param mcmc_type What type of MCMC algorithm should be used for models other #' than linear-Gaussian models? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, @@ -257,6 +266,7 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", out$thin <- thin out$mcmc_type <- "gaussian_mcmc" out$output_type <- output_type + dim(out$counts) <- NULL out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] @@ -538,6 +548,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", out$output_type <- output_type out$call <- match.call() out$seed <- seed + dim(out$counts) <- NULL out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] @@ -703,6 +714,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", out$output_type <- output_type out$call <- match.call() out$seed <- seed + dim(out$counts) <- NULL out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_nlg" @@ -834,6 +846,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", out$output_type <- output_type out$call <- match.call() out$seed <- seed + dim(out$counts) <- NULL out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_sde" diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 3a468aa9..84cccb59 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -42,11 +42,15 @@ bootstrap_filter(model, particles, ...) \arguments{ \item{model}{A model object of class \code{bssm_model}.} -\item{particles}{Number of particles as a positive integer.} +\item{particles}{Number of particles as a positive integer. Suitable values +depend on the model and the data, and while larger values provide more +accurate estimates, the run time also increases with respect to the +number of particles, so it is generally a good idea to test the filter first +with smaller number of particles, say 100.} \item{...}{Ignored.} -\item{seed}{Seed for RNG (non-negative integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{L}{Positive integer defining the discretization level for SDE models.} } diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index b121f14e..daa63321 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -17,11 +17,15 @@ ekpf_filter(model, particles, ...) \arguments{ \item{model}{Model of class \code{ssm_nlg}.} -\item{particles}{Number of particles as a positive integer.} +\item{particles}{Number of particles as a positive integer. Suitable values +depend on the model and the data, and while larger values provide more +accurate estimates, the run time also increases with respect to the +number of particles, so it is generally a good idea to test the filter first +with smaller number of particles, say 100.} \item{...}{Ignored.} -\item{seed}{Seed for RNG (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} } \value{ A list containing samples, filtered estimates and the diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index a814c153..c01a5d30 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -21,7 +21,11 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) \item{model}{Model of class \code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}.} -\item{nsim}{Number of samples (positive integer).} +\item{nsim}{Number of samples (positive integer). Suitable values +depend on the model and the data, and while larger values provide more +accurate estimates, the run time also increases with respect to to the +number of samples, so it is generally a good idea to test the filter first +with smaller number of particles, say 100.} \item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic variable for location in simulation smoothing. Ignored for \code{ssm_mng} @@ -34,7 +38,7 @@ Default is 100 (although typically only few iterations are needed).} is claimed to be converged when the mean squared difference of the modes of is less than \code{conv_tol}.} -\item{seed}{Seed for the random number generator (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{...}{Ignored.} } diff --git a/man/logLik_bssm.Rd b/man/logLik_bssm.Rd index 0422850c..ca38a076 100644 --- a/man/logLik_bssm.Rd +++ b/man/logLik_bssm.Rd @@ -43,16 +43,17 @@ \item{...}{Ignored.} -\item{particles}{Number of samples for particle filter. If 0, -approximate log-likelihood is returned either based on the Gaussian -approximation or EKF, depending on the \code{method} argument.} +\item{particles}{Number of samples for particle filter +(non-negative integer). If 0, approximate log-likelihood is returned either +based on the Gaussian approximation or EKF, depending on the \code{method} +argument.} \item{method}{Sampling method. For Gaussian and non-Gaussian models with linear dynamics,options are \code{"bsf"} (bootstrap particle filter, default -for non-linear models) -and \code{"psi"} (\eqn{\psi}-APF, the default for other models). -For-nonlinear models option \code{"ekf"} uses EKF/IEKF-based particle -filter (or just EKF/IEKF approximation in the case of \code{particles = 0}).} +for non-linear models) and \code{"psi"} (\eqn{\psi}-APF, the default for +other models). For-nonlinear models option \code{"ekf"} +uses EKF/IEKF-based particle filter (or just EKF/IEKF approximation in the +case of \code{particles = 0}).} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, as a positive integer. @@ -61,7 +62,7 @@ Default is 100 (although typically only few iterations are needed).} \item{conv_tol}{Positive tolerance parameter used in Gaussian approximation. Default is 1e-8.} -\item{seed}{Seed for RNG (non-negative integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{iekf_iter}{Non-negative integer. If zero (default), first approximation for non-linear Gaussian models is obtained from extended diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 67434c16..41234301 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -50,7 +50,11 @@ particle_smoother(model, particles, ...) \arguments{ \item{model}{A model object of class \code{bssm_model}.} -\item{particles}{Number of particles as a positive integer.} +\item{particles}{Number of particles as a positive integer. Suitable values +depend on the model, the data, and the chosen algorithm. While larger values +provide more accurate estimates, the run time also increases with respect to +the number of particles, so it is generally a good idea to test the filter +first with a smaller number of particles, say 10 or 100.} \item{...}{Ignored.} @@ -62,7 +66,7 @@ and \code{"psi"} (\eqn{\psi}-APF, the default for other models), and for non-linear models option \code{"ekf"} (extended Kalman particle filter) is also available.} -\item{seed}{Seed for RNG (non-negative integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{max_iter}{Maximum number of iterations used in Gaussian approximation, as a positive integer. diff --git a/man/post_correct.Rd b/man/post_correct.Rd index b20f6342..f6c3a2cd 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -22,7 +22,9 @@ While the intended use assumes this is from approximate MCMC, it is not actually checked, i.e., it is also possible to input previous (asymptotically) exact output.} -\item{particles}{Number of particles for \eqn{\psi}-APF (positive integer).} +\item{particles}{Number of particles for \eqn{\psi}-APF (positive integer). +Suitable values depend on the model and the data, but often relatively +small value less than say 50 is enough. See also \code{suggest_N}} \item{threads}{Number of parallel threads (positive integer, default is 1).} @@ -34,12 +36,12 @@ MCMC iteration independently), particles used forweight computations is proportional to the length of the jump chain block.} -\item{seed}{Seed for the random number generator (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} } \value{ -List with suggested number of particles \code{N} and matrix -containing estimated standard deviations of the log-weights and -corresponding number of particles. +The original object of class \code{mcmc_output} with updated +weights, log-posterior values and state samples or summaries (depending on +the \code{mcmc_output$mcmc_type}). } \description{ Function \code{post_correct} updates previously obtained approximate MCMC diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index e2ecc7db..469cfe9d 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -27,7 +27,9 @@ It is also possible to input the original model for obtaining predictions for past time points. In this case, set argument \code{future} to \code{FALSE}.} -\item{nsim}{Positive integer defining number of samples to draw.} +\item{nsim}{Positive integer defining number of samples to draw. Should be +less than or equal to \code{sum(object$counts)} i.e. the number of samples +in the MCMC output. Default is to use all the samples.} \item{type}{Type of predictions. Possible choices are \code{"mean"} \code{"response"}, or \code{"state"} level.} @@ -37,9 +39,10 @@ future, using posterior samples of (theta, alpha_T+1) i.e. the posterior samples of hyperparameters and latest states. Otherwise it is assumed that \code{model} corresponds to the original model.} -\item{seed}{Seed for RNG (positive integer). Note that this affects only the -C++ side, and \code{predict} also uses R side RNG for subsampling, so for -replicable results you should call \code{set.seed} before \code{predict}.} +\item{seed}{Seed for the C++ RNG (positive integer). Note that this affects +only the C++ side, and \code{predict} also uses R side RNG for subsampling, +so for replicable results you should call \code{set.seed} before +\code{predict}.} \item{...}{Ignored.} } diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 4a8b3472..07ed80c6 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -95,7 +95,11 @@ run_mcmc(model, ...) \item{...}{Ignored.} -\item{iter}{A positive integer defining the total number of MCMC iterations.} +\item{iter}{A positive integer defining the total number of MCMC iterations. +Suitable value depends on the model, data, and the choice of specific +algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing +\code{iter} also increases run time, it is is generally good idea to first +test the performance with a smaller values such as 10000.} \item{output_type}{Either \code{"full"} (default, returns posterior samples from the posterior @@ -138,7 +142,7 @@ Note that parallel computing is only used in the post-correction phase of IS-MCMC and when sampling the states in case of (approximate) Gaussian models.} -\item{seed}{Seed for the random number generator (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{verbose}{If \code{TRUE}, prints a progress bar to the console. If missing, defined by \code{rlang::is_interactive}. @@ -146,7 +150,12 @@ Set to \code{FALSE} if number of iterations is less than 50.} \item{particles}{A positive integer defining the number of state samples per MCMC iteration for models other than linear-Gaussian models. -Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}.} +Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable +values depend on the model, the data, \code{mcmc_type} and +\code{sampling_method}. While larger values provide more +accurate estimates, the run time also increases with respect to to the +number of particles, so it is generally a good idea to test the run time +first with smaller number of particles, say 10-100.} \item{mcmc_type}{What type of MCMC algorithm should be used for models other than linear-Gaussian models? Possible choices are diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index 01b636e5..dd609d2c 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -30,9 +30,13 @@ sim_smoother(model, nsim, seed, use_antithetic = TRUE, ...) \code{bsm_ng}, \code{ar1_ng} \code{svm}, \code{ssm_ung}, or \code{ssm_mng}.} -\item{nsim}{Number of samples (positive integer).} +\item{nsim}{Number of samples (positive integer). Suitable values +depend on the model and the data, and while larger values provide more +accurate estimates, the run time also increases with respect to to the +number of samples, so it is generally a good idea to test the filter first +with smaller number of particles, say 100.} -\item{seed}{Seed for the random number generator (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} \item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic variable for location in simulation smoothing. Ignored for \code{ssm_mng} diff --git a/man/suggest_N.Rd b/man/suggest_N.Rd index 47bb817d..691f2228 100644 --- a/man/suggest_N.Rd +++ b/man/suggest_N.Rd @@ -27,7 +27,7 @@ number of particles to test. Default is \code{seq(10, 100, by = 10)}.} \item{replications}{Positive integer, how many replications should be used for computing the standard deviations? Default is 100.} -\item{seed}{Seed for the random number generator (positive integer).} +\item{seed}{Seed for the C++ RNG (positive integer).} } \value{ List with suggested number of particles \code{N} and matrix diff --git a/src/R_postcorrection.cpp b/src/R_postcorrection.cpp index 11dc126a..36391c3c 100644 --- a/src/R_postcorrection.cpp +++ b/src/R_postcorrection.cpp @@ -172,7 +172,7 @@ Rcpp::List postcorrection_nongaussian(const Rcpp::List model_, } approx_mcmc mcmc_run(counts.n_elem, 0, 1, n, m, p, - 0.234, 1, arma::mat(theta.n_rows, theta.n_rows), output_type, true); + 0.234, 1, arma::mat(theta.n_rows, theta.n_rows), output_type, false); mcmc_run.n_stored = counts.n_elem; // mcmc_run.trim_storage(); @@ -277,7 +277,7 @@ Rcpp::List postcorrection_nonlinear(const arma::mat& y, SEXP Z, SEXP H, n_states, n_etas, time_varying, seed); approx_mcmc mcmc_run(counts.n_elem, 0, 1, model.n, model.m, model.m, - 0.234, 1, arma::mat(theta.n_rows, theta.n_rows), output_type, true); + 0.234, 1, arma::mat(theta.n_rows, theta.n_rows), output_type, false); mcmc_run.n_stored = counts.n_elem; // mcmc_run.trim_storage(); mcmc_run.count_storage = counts; diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R index 254ce2cc..272f5d8e 100644 --- a/tests/testthat/test_predict.R +++ b/tests/testthat/test_predict.R @@ -90,6 +90,43 @@ test_that("Gaussian predictions work", { class(model) <- "aa" expect_error(predict(mcmc_results3, model2, type = "response", future = FALSE, nsim = 100)) + + + set.seed(1) + y <- rnorm(10, cumsum(rnorm(10, 0, 0.1)), 0.1) + model <- bsm_lg(y, + sd_level = halfnormal(1, 1), + sd_slope = halfnormal(0.1, 0.1), + sd_y = halfnormal(0.1, 1)) + + mcmc_results <- run_mcmc(model, iter = 1000) + future_model <- model + future_model$y <- rep(NA, 3) + + expect_error(predict(mcmc_results, future_model, type = "mean", + nsim = 1000), paste0("The number of samples should be smaller than or ", + "equal to the number of posterior samples 500.")) + expect_error(predict(mcmc_results, future_model, type = "state", + nsim = 50), NA) + + set.seed(1) + expect_error(pred <- predict(mcmc_results, future_model, type = "mean", + nsim = 500), NA) + + expect_gt(mean(pred$value[pred$time == 3]), 0) + expect_lt(mean(pred$value[pred$time == 3]), 0.5) + + # Posterior predictions for past observations: + set.seed(1) + expect_error(yrep <- predict(mcmc_results, model, type = "response", + future = FALSE, nsim = 100), NA) + set.seed(1) + expect_error(meanrep <- predict(mcmc_results, model, type = "mean", + future = FALSE, nsim = 100), NA) + + expect_equal(mean(yrep$value - meanrep$value), 0, tol = 0.1) + + }) test_that("Non-gaussian predictions work", { From d9ef351754972952e0f0798037d029c53feda78c Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 12:28:23 +0200 Subject: [PATCH 144/180] fix predict examples --- R/predict.R | 6 +++--- man/predict.mcmc_output.Rd | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/R/predict.R b/R/predict.R index 3aebf75c..31d7ffcc 100644 --- a/R/predict.R +++ b/R/predict.R @@ -48,7 +48,7 @@ #' start = tsp(model$y)[2] + 2 * deltat(model$y), #' frequency = frequency(model$y)) #' # use "state" for illustrative purposes, we could use type = "mean" directly -#' pred <- predict(mcmc_results, future_model = future_model, type = "state", +#' pred <- predict(mcmc_results, model = future_model, type = "state", #' nsim = 1000) #' #' library("dplyr") @@ -91,9 +91,9 @@ #' time = time(JohnsonJohnson))) #' #' # Posterior predictions for past observations: -#' yrep <- predict(mcmc_results, future_model = model, type = "response", +#' yrep <- predict(mcmc_results, model = model, type = "response", #' future = FALSE, nsim = 1000) -#' meanrep <- predict(mcmc_results, future_model = model, type = "mean", +#' meanrep <- predict(mcmc_results, model = model, type = "mean", #' future = FALSE, nsim = 1000) #' #' sumr_yrep <- yrep %>% diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index 469cfe9d..acb9f8ad 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -70,7 +70,7 @@ future_model$y <- ts(rep(NA, 25), start = tsp(model$y)[2] + 2 * deltat(model$y), frequency = frequency(model$y)) # use "state" for illustrative purposes, we could use type = "mean" directly -pred <- predict(mcmc_results, future_model = future_model, type = "state", +pred <- predict(mcmc_results, model = future_model, type = "state", nsim = 1000) library("dplyr") @@ -113,9 +113,9 @@ rbind(sumr_fit, sumr_pred) \%>\% time = time(JohnsonJohnson))) # Posterior predictions for past observations: -yrep <- predict(mcmc_results, future_model = model, type = "response", +yrep <- predict(mcmc_results, model = model, type = "response", future = FALSE, nsim = 1000) -meanrep <- predict(mcmc_results, future_model = model, type = "mean", +meanrep <- predict(mcmc_results, model = model, type = "mean", future = FALSE, nsim = 1000) sumr_yrep <- yrep \%>\% From 0b90a2e6ce4943dfc3e6073eb89b7ab76c8dc8f6 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 12:43:05 +0200 Subject: [PATCH 145/180] return original object with invisible, test flag --- R/check_diagnostics.R | 1 + R/cpp_example_models.R | 3 +++ R/print_mcmc.R | 1 + R/run_mcmc.R | 4 ++-- man/run_mcmc.Rd | 4 ++-- 5 files changed, 9 insertions(+), 4 deletions(-) diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 27b31ed2..721d06f6 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -87,4 +87,5 @@ check_diagnostics <- function(x) { max_rhat <- which.max(sumr$rhat) cat("\nLargest Rhat: ", round(sumr$rhat[max_rhat], 3), " (", sumr$variable[max_rhat], ")", sep = "") + invsible(x) } diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 61cb8a87..99fb6404 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -17,6 +17,9 @@ cpp_example_model <- function(example, return_code = FALSE) { example <- match.arg(tolower(example), c("nlg_linear_gaussian", "nlg_sin_exp", "nlg_growth", "nlg_ar_exp", "sde_poisson_ou", "sde_gbm")) + if (!test_flag(return_code)) + stop("Argument 'return_code' should be TRUE or FALSE. ") + code <- switch(example, "sde_poisson_ou" = { ' diff --git a/R/print_mcmc.R b/R/print_mcmc.R index 0eb0e404..28136a0a 100644 --- a/R/print_mcmc.R +++ b/R/print_mcmc.R @@ -44,4 +44,5 @@ print.mcmc_output <- function(x, ...) { } else cat("\nNo posterior samples for states available.\n") cat("\nRun time:\n") print(x$time) + invisible(x) } diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 95c31185..7c79d19f 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -84,10 +84,10 @@ #' MCMC iteration for models other than linear-Gaussian models. #' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable #' values depend on the model, the data, \code{mcmc_type} and -#' \code{sampling_method}. While larger values provide more +#' \code{sampling_method}. While large values provide more #' accurate estimates, the run time also increases with respect to to the #' number of particles, so it is generally a good idea to test the run time -#' first with smaller number of particles, say 10-100. +#' first with smaller number of particles, say 10 or 100. #' @param mcmc_type What type of MCMC algorithm should be used for models other #' than linear-Gaussian models? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 07ed80c6..eb261cd4 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -152,10 +152,10 @@ Set to \code{FALSE} if number of iterations is less than 50.} MCMC iteration for models other than linear-Gaussian models. Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable values depend on the model, the data, \code{mcmc_type} and -\code{sampling_method}. While larger values provide more +\code{sampling_method}. While large values provide more accurate estimates, the run time also increases with respect to to the number of particles, so it is generally a good idea to test the run time -first with smaller number of particles, say 10-100.} +first with smaller number of particles, say 10 or 100.} \item{mcmc_type}{What type of MCMC algorithm should be used for models other than linear-Gaussian models? Possible choices are From 2be789372682c4cf09aa2c640d42b6e8fedc4c0f Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 13:49:53 +0200 Subject: [PATCH 146/180] give arbitrary upper limits to satisfy autotest checks --- R/bootstrap_filter.R | 2 +- R/importance_sample.R | 2 +- R/particle_smoother.R | 2 +- R/run_mcmc.R | 4 ++-- man/bootstrap_filter.Rd | 2 +- man/ekpf_filter.Rd | 2 +- man/importance_sample.Rd | 2 +- man/particle_smoother.Rd | 2 +- man/run_mcmc.Rd | 4 ++-- man/sim_smoother.Rd | 2 +- 10 files changed, 12 insertions(+), 12 deletions(-) diff --git a/R/bootstrap_filter.R b/R/bootstrap_filter.R index e4906041..bc056353 100644 --- a/R/bootstrap_filter.R +++ b/R/bootstrap_filter.R @@ -8,7 +8,7 @@ #' depend on the model and the data, and while larger values provide more #' accurate estimates, the run time also increases with respect to the #' number of particles, so it is generally a good idea to test the filter first -#' with smaller number of particles, say 100. +#' with a small number of particles, e.g., less than 100. #' @param seed Seed for the C++ RNG (positive integer). #' @param ... Ignored. #' @return List with samples (\code{alpha}) from the filtering distribution and diff --git a/R/importance_sample.R b/R/importance_sample.R index d96c814e..9f190e3e 100644 --- a/R/importance_sample.R +++ b/R/importance_sample.R @@ -11,7 +11,7 @@ #' depend on the model and the data, and while larger values provide more #' accurate estimates, the run time also increases with respect to to the #' number of samples, so it is generally a good idea to test the filter first -#' with smaller number of particles, say 100. +#' with a small number of samples, e.g., less than 100. #' @param use_antithetic Logical. If \code{TRUE} (default), use antithetic #' variable for location in simulation smoothing. Ignored for \code{ssm_mng} #' models. diff --git a/R/particle_smoother.R b/R/particle_smoother.R index 6394a343..e70da937 100644 --- a/R/particle_smoother.R +++ b/R/particle_smoother.R @@ -23,7 +23,7 @@ #' depend on the model, the data, and the chosen algorithm. While larger values #' provide more accurate estimates, the run time also increases with respect to #' the number of particles, so it is generally a good idea to test the filter -#' first with a smaller number of particles, say 10 or 100. +#' first with a small number of particles, e.g., less than 100. #' @param max_iter Maximum number of iterations used in Gaussian approximation, #' as a positive integer. #' Default is 100 (although typically only few iterations are needed). diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 7c79d19f..e88a4e01 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -37,7 +37,7 @@ #' Suitable value depends on the model, data, and the choice of specific #' algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing #' \code{iter} also increases run time, it is is generally good idea to first -#' test the performance with a smaller values such as 10000. +#' test the performance with a small values, e.g., less than 10000. #' @param output_type Either \code{"full"} #' (default, returns posterior samples from the posterior #' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of @@ -87,7 +87,7 @@ #' \code{sampling_method}. While large values provide more #' accurate estimates, the run time also increases with respect to to the #' number of particles, so it is generally a good idea to test the run time -#' first with smaller number of particles, say 10 or 100. +#' firstwith a small number of particles, e.g., less than 100. #' @param mcmc_type What type of MCMC algorithm should be used for models other #' than linear-Gaussian models? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, diff --git a/man/bootstrap_filter.Rd b/man/bootstrap_filter.Rd index 84cccb59..b352824e 100644 --- a/man/bootstrap_filter.Rd +++ b/man/bootstrap_filter.Rd @@ -46,7 +46,7 @@ bootstrap_filter(model, particles, ...) depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to the number of particles, so it is generally a good idea to test the filter first -with smaller number of particles, say 100.} +with a small number of particles, e.g., less than 100.} \item{...}{Ignored.} diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index daa63321..65432d22 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -21,7 +21,7 @@ ekpf_filter(model, particles, ...) depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to the number of particles, so it is generally a good idea to test the filter first -with smaller number of particles, say 100.} +with a small number of particles, e.g., less than 100.} \item{...}{Ignored.} diff --git a/man/importance_sample.Rd b/man/importance_sample.Rd index c01a5d30..9a2dac64 100644 --- a/man/importance_sample.Rd +++ b/man/importance_sample.Rd @@ -25,7 +25,7 @@ importance_sample(model, nsim, use_antithetic, max_iter, conv_tol, seed, ...) depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to to the number of samples, so it is generally a good idea to test the filter first -with smaller number of particles, say 100.} +with a small number of samples, e.g., less than 100.} \item{use_antithetic}{Logical. If \code{TRUE} (default), use antithetic variable for location in simulation smoothing. Ignored for \code{ssm_mng} diff --git a/man/particle_smoother.Rd b/man/particle_smoother.Rd index 41234301..7b6ef671 100644 --- a/man/particle_smoother.Rd +++ b/man/particle_smoother.Rd @@ -54,7 +54,7 @@ particle_smoother(model, particles, ...) depend on the model, the data, and the chosen algorithm. While larger values provide more accurate estimates, the run time also increases with respect to the number of particles, so it is generally a good idea to test the filter -first with a smaller number of particles, say 10 or 100.} +first with a small number of particles, e.g., less than 100.} \item{...}{Ignored.} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index eb261cd4..fec664cc 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -99,7 +99,7 @@ run_mcmc(model, ...) Suitable value depends on the model, data, and the choice of specific algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing \code{iter} also increases run time, it is is generally good idea to first -test the performance with a smaller values such as 10000.} +test the performance with a small values, e.g., less than 10000.} \item{output_type}{Either \code{"full"} (default, returns posterior samples from the posterior @@ -155,7 +155,7 @@ values depend on the model, the data, \code{mcmc_type} and \code{sampling_method}. While large values provide more accurate estimates, the run time also increases with respect to to the number of particles, so it is generally a good idea to test the run time -first with smaller number of particles, say 10 or 100.} +firstwith a small number of particles, e.g., less than 100.} \item{mcmc_type}{What type of MCMC algorithm should be used for models other than linear-Gaussian models? Possible choices are diff --git a/man/sim_smoother.Rd b/man/sim_smoother.Rd index dd609d2c..24f59500 100644 --- a/man/sim_smoother.Rd +++ b/man/sim_smoother.Rd @@ -34,7 +34,7 @@ sim_smoother(model, nsim, seed, use_antithetic = TRUE, ...) depend on the model and the data, and while larger values provide more accurate estimates, the run time also increases with respect to to the number of samples, so it is generally a good idea to test the filter first -with smaller number of particles, say 100.} +with a small number of samples, e.g., less than 100.} \item{seed}{Seed for the C++ RNG (positive integer).} From cc8209be884dacc5fb09e4e7df04ec35283a6132 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 17:04:24 +0200 Subject: [PATCH 147/180] add citation info to readme --- README.Rmd | 6 ++++++ README.md | 19 ++++++++++++++----- man/figures/README-bivariate-fig-1.png | Bin 8839 -> 8839 bytes man/figures/README-bivariate-fig-2.png | Bin 10819 -> 10820 bytes man/figures/README-compare-1.png | Bin 13174 -> 13173 bytes man/figures/README-example-1.png | Bin 10984 -> 10985 bytes 6 files changed, 20 insertions(+), 5 deletions(-) diff --git a/README.Rmd b/README.Rmd index 8c5c8d90..6cf8ea60 100644 --- a/README.Rmd +++ b/README.Rmd @@ -95,6 +95,12 @@ The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, and 311877. Current development is focused on increased usability. For recent changes, see NEWS file. +### Citing the package + +If you use the `bssm` package in publications, please cite the corresponding R Journal paper: + +Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R." _R Journal_. To appear. ArXiv preprint https://arxiv.org/abs/2101.08492. + ## Installation You can install the released version of bssm from [CRAN](https://CRAN.R-project.org) with: diff --git a/README.md b/README.md index ef485f80..36a1b521 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,15 @@ The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, and 311877. Current development is focused on increased usability. For recent changes, see NEWS file. +### Citing the package + +If you use the `bssm` package in publications, please cite the +corresponding R Journal paper: + +Jouni Helske and Matti Vihola (2021). “bssm: Bayesian Inference of +Non-linear and Non-Gaussian State Space Models in R.” *R Journal*. To +appear. ArXiv preprint <https://arxiv.org/abs/2101.08492>. + ## Installation You can install the released version of bssm from @@ -117,7 +126,7 @@ fit <- run_mcmc(model, iter = 20000, burnin = 5000) fit #> #> Call: -#> run_mcmc.gaussian(model = model, iter = 20000, burnin = 5000) +#> run_mcmc.lineargaussian(model = model, iter = 20000, burnin = 5000) #> #> Iterations = 5001:20000 #> Thinning interval = 1 @@ -142,7 +151,7 @@ fit #> #> Run time: #> user system elapsed -#> 1.02 0.02 1.02 +#> 0.97 0.00 0.97 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -210,7 +219,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 14.47 0.19 14.45 +#> 11.28 0.11 11.27 ``` Comparison: @@ -294,7 +303,7 @@ fit <- run_mcmc(model, iter = 60000, burnin = 10000) fit #> #> Call: -#> run_mcmc.gaussian(model = model, iter = 60000, burnin = 10000) +#> run_mcmc.lineargaussian(model = model, iter = 60000, burnin = 10000) #> #> Iterations = 10001:60000 #> Thinning interval = 1 @@ -320,7 +329,7 @@ fit #> #> Run time: #> user system elapsed -#> 16.63 0.20 16.65 +#> 12.25 0.10 12.32 ``` Draw predictions: diff --git a/man/figures/README-bivariate-fig-1.png b/man/figures/README-bivariate-fig-1.png index a87efe8c9b9fbd16a2d5250cffd5ce567fb10a5c..47edbb047115bf1163dddda2d326c5489c566dea 100644 GIT binary patch delta 3719 zcmZ`(dpML^+h5NdjL0~qD0yoqN=`8`WIXh$tsI_miege>nu&7CPVqeD3+*=2(7~a3 zympLnN{%UMJETYrW>k(_<q)Z)(ROOyvA_4azJI=VUC+9%d#(Goe)sQQ>wfM;$3(}g z#;73xp1=?To&uR=2n-D|k-53K%ktp3xFor7@?DaeJ!K$)RJP{0a5yeW9FFYE=j0D# zh5!>_m`IxRH#Es5DM{vTmfoy>`LD-s@;NT~%ebWc=KTEO;rwB#z!M0h(&i+WW~o$! z8H%I=W06!koX;7SN+%~LrN{QjATqzqDchAwm%%cm3?h|E&59DM)B3h3KRa%d#?>mz zNS?~@ok5OPs6(3|D2!`Gu;C1y>ht>6?zkt&u3Uxr<Ko8%TJA^YWL>sph)(AfY2-*H zUe^>p2Q+#*d-h|uv*Uv{rq}mOgb!9mt-+@pmn6LCGRqsS{)>UWChM_A>mKVx#&}Il zN8xw-2-P-!P89uAZ{2(DCZI6M;-b1*T$p%*%OcvZ;Q-$@-{Zo?9xb4>eqy>#{Oc|! zhqW5MTzjIv{NzRK6J$`F#|?uV6t567<B?h4qV}`h+yJrz*OX|AMw#MemfkKiU*n>u 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z;w??xIJX_Wk3Fv+{h$rsv|vXKKWJMq%j0_{uzqr=xP|k70_*YO=4%4Dl=_p3H1+>Z z?_QFhUpl4JeW`Tkio)1@Xfk&GZ`4QaL|jX1a?O^<wap%St>vdl{qS2<iSNIFZN$g2 zLu-Qe=Fq}SRyO^6=wjhTn&qcPeQm?EJJVN?s;%L)m6?t4GvP*z;OwBlz%F~n;qPX< z)yXS6{0yr;gR4EaT(7t+Y)%(m|2|fyCt4Ove4F{ntJ-FCWhw+$IokNSPR}a<S1GIT zJ*-*RD|qhJOZs>9g9HO4U4^i*Jl12#muOZ~JUpZ$D##wj=#l*yEuJAQb#vwZc0$rq zVZLNKN^004eb3FDe?uw$2W*icvN~j>Hg@m(*5cQ$_pcQ|UnRB_1E%>)g0FAj=#{FN z;Ek=N>W%4BNvrcq;}NG~ZMSqb)+PqJ?*L`VLTJRsT*Mb8_vLBxtxaZK`u57NGKH9X z%pm~n{KIE`+Dm7HdOLk<<9BXR)<r`M!wHkTobk-Ib1Qt8?NUa%u%ikScENlLe_Qta zD9~qe2(8|X&zrorEr1AJC%l4`sdu4*DaW7K;PS>jp>-wzueAbbWmSxWE!ue?1MB~J b$EIjVo~PN(j!&1j5vxZH%=8QNPDlPXw7=d# From 44d4f2757ae0d6f06ad634a3c7967987e9362d32 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 17:05:26 +0200 Subject: [PATCH 148/180] fix typo --- R/check_diagnostics.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/check_diagnostics.R b/R/check_diagnostics.R index 721d06f6..6aeef140 100644 --- a/R/check_diagnostics.R +++ b/R/check_diagnostics.R @@ -87,5 +87,5 @@ check_diagnostics <- function(x) { max_rhat <- which.max(sumr$rhat) cat("\nLargest Rhat: ", round(sumr$rhat[max_rhat], 3), " (", sumr$variable[max_rhat], ")", sep = "") - invsible(x) + invisible(x) } From 56555d618bd50445d9df5ba1c665174ca3d5dd11 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 18:33:39 +0200 Subject: [PATCH 149/180] allow zero burnin --- R/run_mcmc.R | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index e88a4e01..ec7ef9b2 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -190,8 +190,8 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", threads <- check_intmax(threads, "threads") thin <- check_intmax(thin, "thin", max = 100) - iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_intmax(burnin, "burnin", max = 1e12) + iter <- check_intmax(iter, "iter", max = 1e12) + burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { @@ -419,8 +419,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") thin <- check_intmax(thin, "thin", max = 100) - iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e10) - burnin <- check_intmax(burnin, "burnin", max = 1e10) + iter <- check_intmax(iter, "iter", max = 1e12) + burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { @@ -590,8 +590,8 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") thin <- check_intmax(thin, "thin", max = 100) - iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_intmax(burnin, "burnin", max = 1e12) + iter <- check_intmax(iter, "iter", max = 1e12) + burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") @@ -761,8 +761,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", particles <- check_intmax(particles, "particles") threads <- check_intmax(threads, "threads") thin <- check_intmax(thin, "thin", max = 100) - iter <- check_intmax(iter, "iter", positive = FALSE, max = 1e12) - burnin <- check_intmax(burnin, "burnin", max = 1e12) + iter <- check_intmax(iter, "iter", max = 1e12) + burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") if (missing(verbose)) { From 4a96fb44875b7f685fa655436d216c033ac4ea4a Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 18:42:28 +0200 Subject: [PATCH 150/180] fix too long lines --- R/cpp_example_models.R | 4 ++-- R/models.R | 10 +++++----- R/run_mcmc.R | 6 +++--- man/ar1_ng.Rd | 6 +++--- man/bsm_lg.Rd | 4 ++-- man/bsm_ng.Rd | 10 +++++----- man/run_mcmc.Rd | 6 +++--- man/ssm_ung.Rd | 6 +++--- 8 files changed, 26 insertions(+), 26 deletions(-) diff --git a/R/cpp_example_models.R b/R/cpp_example_models.R index 99fb6404..c3ede175 100644 --- a/R/cpp_example_models.R +++ b/R/cpp_example_models.R @@ -14,8 +14,8 @@ #' cpp_example_model <- function(example, return_code = FALSE) { - example <- match.arg(tolower(example), c("nlg_linear_gaussian", "nlg_sin_exp", - "nlg_growth", "nlg_ar_exp", "sde_poisson_ou", "sde_gbm")) + example <- match.arg(tolower(example), c("nlg_linear_gaussian", + "nlg_sin_exp", "nlg_growth", "nlg_ar_exp", "sde_poisson_ou", "sde_gbm")) if (!test_flag(return_code)) stop("Argument 'return_code' should be TRUE or FALSE. ") diff --git a/R/models.R b/R/models.R index 5522072c..946b3fbe 100644 --- a/R/models.R +++ b/R/models.R @@ -832,9 +832,9 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' distribution this is the shape parameter, and for other distributions this #' is ignored. Should an object of class \code{bssm_prior} or #' a positive scalar. -#' @param u A vector of positive constants for non-Gaussian models. For Poisson, -#' gamma, and negative binomial distribution, this corresponds to the offset -#' term. For binomial, this is the number of trials. +#' @param u A vector of positive constants for non-Gaussian models. For +#' Poisson, gamma, and negative binomial distribution, this corresponds to the +#' offset term. For binomial, this is the number of trials. #' @param beta A prior for the regression coefficients. #' Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} #' (in case of multiple coefficients) or missing in case of no covariates. @@ -847,8 +847,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' which can also return non-integer value (in which case error is given). #' @param a1 Prior means for the initial states (level, slope, seasonals). #' Defaults to vector of zeros. -#' @param P1 Prior covariance matrix for the initial states (level, slope, seasonals). -#' Default is diagonal matrix with 1000 on the diagonal. +#' @param P1 Prior covariance matrix for the initial states (level, slope, +#' seasonals).Default is diagonal matrix with 100 on the diagonal. #' @param C Intercept terms for state equation, given as a m x n or m x 1 #' matrix. #' @return An object of class \code{bsm_ng}. diff --git a/R/run_mcmc.R b/R/run_mcmc.R index ec7ef9b2..d182e762 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -47,9 +47,9 @@ #' which is disregarded from the results. Defaults to \code{iter / 2}. #' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the #' burn-in period in order to find good proposal distribution. -#' @param thin A positive integer defining the thinning rate. All MCMC algorithms -#' in \code{bssm} use the jump chain representation (see refs), and the -#' thinning is applied to these blocks. Defaults to 1. +#' @param thin A positive integer defining the thinning rate. All the MCMC +#' algorithms in \code{bssm} use the jump chain representation (see refs), +#' and the thinning is applied to these blocks. Defaults to 1. #' For IS-corrected methods, larger value can also be #' statistically more effective. Note: With \code{output_type = "summary"}, #' the thinning does not affect the computations of the summary statistics in diff --git a/man/ar1_ng.Rd b/man/ar1_ng.Rd index 098aebc8..44a66c28 100644 --- a/man/ar1_ng.Rd +++ b/man/ar1_ng.Rd @@ -29,9 +29,9 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} +\item{u}{A vector of positive constants for non-Gaussian models. For +Poisson, gamma, and negative binomial distribution, this corresponds to the +offset term. For binomial, this is the number of trials.} \item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} diff --git a/man/bsm_lg.Rd b/man/bsm_lg.Rd index a0f53237..c082cb21 100644 --- a/man/bsm_lg.Rd +++ b/man/bsm_lg.Rd @@ -56,8 +56,8 @@ which can also return non-integer value (in which case error is given).} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} -\item{P1}{Prior covariance matrix for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1000 on the diagonal.} +\item{P1}{Prior covariance matrix for the initial states (level, slope, +seasonals).Default is diagonal matrix with 100 on the diagonal.} \item{D}{Intercept terms for observation equation, given as a length n numeric vector or a scalar in case of time-invariant intercept.} diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index 7f53cce4..be855f0a 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -52,9 +52,9 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} +\item{u}{A vector of positive constants for non-Gaussian models. For +Poisson, gamma, and negative binomial distribution, this corresponds to the +offset term. For binomial, this is the number of trials.} \item{beta}{A prior for the regression coefficients. Should be an object of class \code{bssm_prior} or \code{bssm_prior_list} @@ -72,8 +72,8 @@ which can also return non-integer value (in which case error is given).} \item{a1}{Prior means for the initial states (level, slope, seasonals). Defaults to vector of zeros.} -\item{P1}{Prior covariance matrix for the initial states (level, slope, seasonals). -Default is diagonal matrix with 1000 on the diagonal.} +\item{P1}{Prior covariance matrix for the initial states (level, slope, +seasonals).Default is diagonal matrix with 100 on the diagonal.} \item{C}{Intercept terms for state equation, given as a m x n or m x 1 matrix.} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index fec664cc..0d2ca9a9 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -112,9 +112,9 @@ which is disregarded from the results. Defaults to \code{iter / 2}. Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the burn-in period in order to find good proposal distribution.} -\item{thin}{A positive integer defining the thinning rate. All MCMC algorithms -in \code{bssm} use the jump chain representation (see refs), and the -thinning is applied to these blocks. Defaults to 1. +\item{thin}{A positive integer defining the thinning rate. All the MCMC +algorithms in \code{bssm} use the jump chain representation (see refs), +and the thinning is applied to these blocks. Defaults to 1. For IS-corrected methods, larger value can also be statistically more effective. Note: With \code{output_type = "summary"}, the thinning does not affect the computations of the summary statistics in diff --git a/man/ssm_ung.Rd b/man/ssm_ung.Rd index 3cc0463c..57a390ba 100644 --- a/man/ssm_ung.Rd +++ b/man/ssm_ung.Rd @@ -49,9 +49,9 @@ distribution this is the shape parameter, and for other distributions this is ignored. Should an object of class \code{bssm_prior} or a positive scalar.} -\item{u}{A vector of positive constants for non-Gaussian models. For Poisson, -gamma, and negative binomial distribution, this corresponds to the offset -term. For binomial, this is the number of trials.} +\item{u}{A vector of positive constants for non-Gaussian models. For +Poisson, gamma, and negative binomial distribution, this corresponds to the +offset term. For binomial, this is the number of trials.} \item{init_theta}{Initial values for the unknown hyperparameters theta (i.e. unknown variables excluding latent state variables).} From 835eba3abc76b3191b828c3349362970a48f119b Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 20:47:52 +0200 Subject: [PATCH 151/180] decrease iter in fitted example --- R/fitted.R | 2 +- man/fitted.mcmc_output.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/fitted.R b/R/fitted.R index 98669f78..b6d1e0db 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -19,7 +19,7 @@ #' prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) #' model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, #' sd_slope = prior, sd_seasonal = prior, period = 4) -#' fit <- run_mcmc(model, iter = 2e4) +#' fit <- run_mcmc(model, iter = 1e4) #' res <- fitted(fit, model) #' head(res) #' diff --git a/man/fitted.mcmc_output.Rd b/man/fitted.mcmc_output.Rd index 0fb9d0ff..b63fafbd 100644 --- a/man/fitted.mcmc_output.Rd +++ b/man/fitted.mcmc_output.Rd @@ -25,7 +25,7 @@ distribution of the mean. prior <- uniform(0.1 * sd(log10(UKgas)), 0, 1) model <- bsm_lg(log10(UKgas), sd_y = prior, sd_level = prior, sd_slope = prior, sd_seasonal = prior, period = 4) -fit <- run_mcmc(model, iter = 2e4) +fit <- run_mcmc(model, iter = 1e4) res <- fitted(fit, model) head(res) From 08ebfbf29dcd6c7c9741aa72f68bcb08297be4cf Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 21:12:11 +0200 Subject: [PATCH 152/180] 2.0.0 to rOpensci and CRAN --- NEWS | 2 +- README.Rmd | 13 ++++++++----- README.md | 6 +++--- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/NEWS b/NEWS index 4b2b8bf4..b82762f0 100644 --- a/NEWS +++ b/NEWS @@ -1,4 +1,4 @@ -bssm 2.0.0 (Release date: -) +bssm 2.0.0 (Release date: 2021-11-26) ============== * Added a progress bar for run_mcmc. * Added a fitted method for extraction of summary statistics of posterior diff --git a/README.Rmd b/README.Rmd index 6cf8ea60..633dbefb 100644 --- a/README.Rmd +++ b/README.Rmd @@ -25,11 +25,14 @@ knitr::opts_chunk$set( #' @srrstats {G1.0, G1.3, G1.4, G1.4a, G1.5, G1.6} General #' documentation, addressed by the vignettes and the corresponding R #' Journal paper. -#' @srrstats {G1.1} This is the first R package to implement delayed acceptance -#' pseudo-marginal MCMC for general state space models, and the first to -#' implement the IS-MCMC by Vihola, Helske, and Franks (2020). The IS-MCMC -#' method is also available in code{walker} package for a limited class of -#' models (a subset of the models supported by \code{bssm}). +#' @srrstats {G1.1} This is the first software to implement the IS-MCMC by +#' Vihola, Helske, and Franks (2020) and first R package to implement delayed +#' acceptance pseudo-marginal MCMC for state space models. The IS-MCMC method +#' is also available in [walker](github.com/helske/walker) package for a +#' limited class of time-varying GLMss (a small subset of the models +#' supported by this package). Some of the functionality for exponential family +#' state space models is also available in [KFAS](github.com/helske/KFAS), and +#' those models can be converted easily to bssm format for Bayesian analysis. #' @srrstats {G2.4, G2.4a, G2.4b, G2.4c, G2.6} Explicit conversions are used #' where necessary. #' diff --git a/README.md b/README.md index 36a1b521..8fdd0eab 100644 --- a/README.md +++ b/README.md @@ -151,7 +151,7 @@ fit #> #> Run time: #> user system elapsed -#> 0.97 0.00 0.97 +#> 0.87 0.00 0.88 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -219,7 +219,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 11.28 0.11 11.27 +#> 10.60 0.05 10.63 ``` Comparison: @@ -329,7 +329,7 @@ fit #> #> Run time: #> user system elapsed -#> 12.25 0.10 12.32 +#> 11.94 0.11 12.01 ``` Draw predictions: From 8fe61ddb64bcd90a818d4d37d7597ebc24e3cf89 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 22:12:19 +0200 Subject: [PATCH 153/180] modify rbuildignore in order to add readme --- .Rbuildignore | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 885af96d..7a27dde8 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,13 +1,8 @@ ^\.git$ ^.*\.Rproj$ ^\.Rproj\.user$ -^README\.md$ ^\.gitignore$ -^\.travis\.yml$ -^TODO$ ^\.Rhistory$ -^bssm.pdf$ -^growth_model.pdf$ ^vignettes/psi_pf_experiments/.*result.*\.rds$ ^vignettes/psi_pf_experiments/.*truth.*\.rds$ ^vignettes/psi_pf_experiments/.*\.sh$ @@ -17,3 +12,4 @@ ^codemeta\.json$ ^README\.Rmd$ ^benchmarks$ +^autotest_results.R$ From 19e2b3fc1ad9a06da5ff75466153b6e1a07e8d5e Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 23:56:05 +0200 Subject: [PATCH 154/180] update urls --- .Rbuildignore | 2 +- README.Rmd | 11 ++++++----- README.md | 15 +++++++-------- man/figures/README-bivariate-fig-1.png | Bin 8839 -> 8839 bytes man/figures/README-bivariate-fig-2.png | Bin 10820 -> 10819 bytes man/figures/README-compare-1.png | Bin 13173 -> 13174 bytes man/figures/README-example-1.png | Bin 10985 -> 10984 bytes 7 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 7a27dde8..e23d4569 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -12,4 +12,4 @@ ^codemeta\.json$ ^README\.Rmd$ ^benchmarks$ -^autotest_results.R$ +^autotest_results\.R$ diff --git a/README.Rmd b/README.Rmd index 633dbefb..3a85f19c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -9,7 +9,8 @@ knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", - out.width = "100%" + out.width = "100%", + cache = TRUE ) ``` @@ -68,9 +69,9 @@ knitr::opts_chunk$set( <!-- badges: start --> [![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) -[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) -[![CRAN version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) -[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) +[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) +[![CRAN version](http://www.r-pkg.org/badges/version/bssm)]( https://CRAN.R-project.org/package=bssm) +[![downloads](https://cranlogs.r-pkg.org/badges/bssm)](https://cranlogs.r-pkg.org/badges/bssm) <!-- badges: end --> @@ -85,7 +86,7 @@ supported. For details, see * [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), -* [Package vignettes at CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +* [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) There are also couple posters and a talk related to IS-correction methodology and bssm package: diff --git a/README.md b/README.md index 8fdd0eab..e8786936 100644 --- a/README.md +++ b/README.md @@ -10,10 +10,10 @@ state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test -coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://codecov.io/gh/helske/bssm?branch=master) +coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) [![CRAN -version](http://www.r-pkg.org/badges/version/bssm)](http://cran.r-project.org/package=bssm) -[![downloads](http://cranlogs.r-pkg.org/badges/bssm)](http://cranlogs.r-pkg.org/badges/bssm) +version](http://www.r-pkg.org/badges/version/bssm)](https://CRAN.R-project.org/package=bssm) +[![downloads](https://cranlogs.r-pkg.org/badges/bssm)](https://cranlogs.r-pkg.org/badges/bssm) <!-- badges: end --> @@ -28,8 +28,7 @@ For details, see - [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), -- [Package vignettes at - CRAN](https://cran.r-project.org/web/packages/bssm/index.html) +- [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) - Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) @@ -151,7 +150,7 @@ fit #> #> Run time: #> user system elapsed -#> 0.87 0.00 0.88 +#> 1.14 0.00 1.12 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -219,7 +218,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 10.60 0.05 10.63 +#> 15.02 0.20 14.95 ``` Comparison: @@ -329,7 +328,7 @@ fit #> #> Run time: #> user system elapsed -#> 11.94 0.11 12.01 +#> 15.70 0.18 15.75 ``` Draw predictions: diff --git a/man/figures/README-bivariate-fig-1.png b/man/figures/README-bivariate-fig-1.png index 47edbb047115bf1163dddda2d326c5489c566dea..a87efe8c9b9fbd16a2d5250cffd5ce567fb10a5c 100644 GIT binary patch delta 3699 zcmZ`(do)yS+rRgo80XPR(v0aTMX6K{F&*sboEmn8N~I=;7_ZUeP!jh3I;j-Z6e4UD zrWuFC5JJ5wA?#jb6nT2(*a($V2F*8~^{(~(^S#%)@3pS$`u(ocTGxF?ZHwA=a}~w{ zz#Fh`0`EdgOG}l(0xZ_eR?f8WIh+^{U(AVl>#ZUcDpfY0!{>8i_<Yq7^TnMpe=}k@ 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T9F)HXSvAx@vOibvZ18^oNEGm% From 8c52ea3253af02c238f936de1618f37db61f0118 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Thu, 25 Nov 2021 23:58:43 +0200 Subject: [PATCH 155/180] dont include README cache to tar.gz --- .Rbuildignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.Rbuildignore b/.Rbuildignore index e23d4569..28d85b5a 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,6 +10,7 @@ ^\.github$ ^codecov\.yml$ ^codemeta\.json$ -^README\.Rmd$ +^README\.Rmd +^README_cache$ ^benchmarks$ ^autotest_results\.R$ From d4f5c9f750c3472ecc9a5cc133f908fec5a62627 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Fri, 26 Nov 2021 11:39:40 +0200 Subject: [PATCH 156/180] remove nonexistent srr tag, fixes #30# --- tests/testthat/test_mcmc.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test_mcmc.R b/tests/testthat/test_mcmc.R index 157db1eb..802b090f 100644 --- a/tests/testthat/test_mcmc.R +++ b/tests/testthat/test_mcmc.R @@ -1,6 +1,6 @@ context("Test MCMC") -#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, BS7.0, BS7.1, BS7.2, BS7.7} +#' @srrstats {G5.4, G5.4a, G5.4b, G5.4c, G5.5, BS7.0, BS7.1, BS7.2} #' Replicate Helske & Vihola (2021). tol <- 1e-8 From b9ccc0d15e7d5e51be397366eae09324bc2464d6 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Fri, 10 Dec 2021 15:42:02 +0200 Subject: [PATCH 157/180] set weights to 1 --- DESCRIPTION | 2 +- NEWS | 4 ++++ src/R_mcmc.cpp | 7 +++++++ 3 files changed, 12 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 69551111..1be04f67 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.0 +Version: 2.0.0.1 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NEWS b/NEWS index b82762f0..fb34296d 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,7 @@ +bssm 2.0.x (Release date: -) +============== + * Fixed weights to one in case of non-linear model with mcmc_type="approx". + bssm 2.0.0 (Release date: 2021-11-26) ============== * Added a progress bar for run_mcmc. diff --git a/src/R_mcmc.cpp b/src/R_mcmc.cpp index 5919c01c..f7843bf6 100644 --- a/src/R_mcmc.cpp +++ b/src/R_mcmc.cpp @@ -764,7 +764,14 @@ Rcpp::List nonlinear_is_mcmc(const arma::mat& y, SEXP Z, SEXP H, approx_mcmc mcmc_run(iter, burnin, thin, model.n, model.m, model.m, target_acceptance, gamma, S, output_type, true, verbose); + if (nsim <= 1) { + mcmc_run.alpha_storage.zeros(); + mcmc_run.weight_storage.ones(); + mcmc_run.posterior_storage.zeros(); + } + mcmc_run.amcmc(model, sampling_method, end_ram); + if(approx) { if(output_type == 1) { mcmc_run.approx_state_posterior(model, n_threads); From 1989d57ac3da869d0f75645471ed90a9cc3fb422 Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@iki.fi> Date: Fri, 29 Apr 2022 23:30:06 +0300 Subject: [PATCH 158/180] update citation info --- README.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index e8786936..ca713138 100644 --- a/README.md +++ b/README.md @@ -26,8 +26,7 @@ models and discretely observed latent diffusion processes are supported. For details, see -- [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to - appear in R Journal), +- [The bssm paper on R Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), - [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) - Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte @@ -44,7 +43,7 @@ methodology and bssm package: R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) The `bssm` package was originally developed with the support of Academy -of Finland grants 284513, 312605, and 311877. Current development is +of Finland grants 284513, 312605, 311877, and 331817. Current development is focused on increased usability. For recent changes, see NEWS file. ### Citing the package @@ -53,8 +52,7 @@ If you use the `bssm` package in publications, please cite the corresponding R Journal paper: Jouni Helske and Matti Vihola (2021). “bssm: Bayesian Inference of -Non-linear and Non-Gaussian State Space Models in R.” *R Journal*. To -appear. ArXiv preprint <https://arxiv.org/abs/2101.08492>. +Non-linear and Non-Gaussian State Space Models in R.” *R Journal*. https://journal.r-project.org/archive/2021/RJ-2021-103/index.html>. ## Installation From 16f619c739173df875cb0dda1cc7f5e496915bfb Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 2 May 2022 07:15:42 +0300 Subject: [PATCH 159/180] tweak tolerances to pass MKL checks on CRAN, update citation info --- DESCRIPTION | 13 +-- NEWS | 201 ++++++++++++++++++----------------- codemeta.json | 77 ++++++-------- inst/CITATION | 15 +-- tests/testthat/test_basics.R | 147 ++++++++++++------------- 5 files changed, 223 insertions(+), 230 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1be04f67..a252e827 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.0.1 +Version: 2.0.1 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -14,14 +14,15 @@ Authors@R: role = "aut", comment = c(ORCID = "0000-0002-8041-7222"))) Description: Efficient methods for Bayesian inference of state space models - via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel + via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators - (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>). + (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), + particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models - with linear-Gaussian state dynamics, - as well as general non-linear Gaussian models and discretised - diffusion models are supported. + with linear-Gaussian state dynamics, as well as general non-linear Gaussian + models and discretised diffusion models are supported. + See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details. License: GPL (>= 2) Depends: R (>= 3.5.0) Suggests: diff --git a/NEWS b/NEWS index fb34296d..c5624d13 100644 --- a/NEWS +++ b/NEWS @@ -1,64 +1,65 @@ -bssm 2.0.x (Release date: -) +bssm 2.0.1 (Release date: 2022-05-02) ============== * Fixed weights to one in case of non-linear model with mcmc_type="approx". + * Adjusted tolerance of some testthat tests to comply with CRAN's MKL checks. bssm 2.0.0 (Release date: 2021-11-26) ============== * Added a progress bar for run_mcmc. - * Added a fitted method for extraction of summary statistics of posterior - predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. - * Rewrote the summary method completely, which now returns data.frame. This + * Added a fitted method for extraction of summary statistics of posterior + predictive distribution p(y_t | y_1, ..., y_n) for t = 1, ..., n. + * Rewrote the summary method completely, which now returns data.frame. This also resulted in some changes in order of the function arguments. - * The output of predict method is now a data frame with column weight - corresponding to the IS-weights in case of IS-MCMC. Previously resampling - was done internally, but now this is left for the user if needed + * The output of predict method is now a data frame with column weight + corresponding to the IS-weights in case of IS-MCMC. Previously resampling + was done internally, but now this is left for the user if needed (i.e. for drawing state trajectories). - * The asymptotic_var and iact functions are now exported to users, and they + * The asymptotic_var and iact functions are now exported to users, and they also contain alternative methods based on the posterior package. - * New function estimate_ess can be used to compute effective sample size + * New function estimate_ess can be used to compute effective sample size from weighted MCMC. - * Added compatibility with the posterior package by defining as_draws + * Added compatibility with the posterior package by defining as_draws method for converting run_mcmc output to draws_df object. * New function check_diagnostics for quick glance of ESS and Rhat values. * Large number of new tests, and improved documentation with added examples. - * Large number of internal tweaks so that the package complies with + * Large number of internal tweaks so that the package complies with goodpractices package and Ropensci statistical software standards. - + bssm 1.1.7-1 (Release date: 2021-09-21) ============== * Fixed an error in automatic tests due to lack of fixed RNG seed. bssm 1.1.7 (Release date: 2021-09-20) ============== - * Added a function cpp_example_model which can be used to extract and + * Added a function cpp_example_model which can be used to extract and compile some non-linear and SDE models used in the examples and vignettes. - * Added as_draws method for run_mcmc output so samples can be analysed using + * Added as_draws method for run_mcmc output so samples can be analysed using the posterior package. * Added more examples. * Fixed a tolerance of one MCMC test to pass the test on OSX as well. - * Fixed a bug in iterated extended Kalman smoothing which resulted incorrect + * Fixed a bug in iterated extended Kalman smoothing which resulted incorrect estimates. bssm 1.1.6 (Release date: 2021-09-06) ============== * Cleaned some codes and added lots of tests in line with pkgcheck tests. - * Fixed a bug in EKF-based particle filter which returned filtered estimates + * Fixed a bug in EKF-based particle filter which returned filtered estimates also in place of one-step ahead predictions. * Fixed a bug which caused an error in suggest_N for nlg_ssm. - * Fixed a bug which caused incorrect sampling of smoothing distribution for + * Fixed a bug which caused incorrect sampling of smoothing distribution for ar1_lg model when predicting past or when using simulation smoother. - * Fixed a bug which caused an error when predicting past values in + * Fixed a bug which caused an error when predicting past values in multivariate time series case. - * Fixed log-likelihood computation for gamma model with non-constant shape + * Fixed log-likelihood computation for gamma model with non-constant shape parameter when using (intermediate) Gaussian approximation. - * Fixed sampling of negative binomial distribution in predict method, which - used std::negative_binomial which converts non-integer phi to integer. + * Fixed sampling of negative binomial distribution in predict method, which + used std::negative_binomial which converts non-integer phi to integer. Sampling now uses Gamma-Poisson mixture for simulation. bssm 1.1.5 (Release date: 2021-06-14) ============== - * Added explicit check for nsim > 0 in predict method as sample function + * Added explicit check for nsim > 0 in predict method as sample function works with missing argument causing crypting warnings later. * Updated drownings data until 2019 and changed the temperature variable to an average over three stations. @@ -69,12 +70,12 @@ bssm 1.1.4 (Release date: 2021-04-13) * Better documentation for SV model, and changed ordering of arguments to emphasise the recommended parameterization. * Fixed predict method for SV model. - * Removed parallelization in one example which failed on Solaris for some + * Removed parallelization in one example which failed on Solaris for some unknown reason. - + bssm 1.1.3-2 (Release date: 2021-02-24) ============== - * Fixed missing parenthesis causing compilation fail in case of no OpenMP + * Fixed missing parenthesis causing compilation fail in case of no OpenMP support. * Added pandoc version >= 1.12.3 to system requirements. * Restructured C++ classes so no R structures are present in OpenMP regions. @@ -82,213 +83,213 @@ bssm 1.1.3-2 (Release date: 2021-02-24) bssm 1.1.3-1 (Release date: 2021-02-22) ============== * Fixed PM-MCMC and DA-MCMC for SDE models and added an example to `ssm_sde`. - * Fixed the state covariance estimates of IS-MCMC, approx-MCMC, and + * Fixed the state covariance estimates of IS-MCMC, approx-MCMC, and Gaussian MCMC when output_type = "summary". - * Fixed memory leaks due to uninitialized variables due to aborted particle + * Fixed memory leaks due to uninitialized variables due to aborted particle filter. - * Fixed numerical issues of multivariate normal density for nonlinear + * Fixed numerical issues of multivariate normal density for nonlinear models. * Removed dependency on R::lchoose for safer parallel code. * Added vignette for SDE models. * Updated citation information and streamlined the main vignette. - + bssm 1.1.2 (Release date: 2021-02-08) ============== - * Changed the definition of D in ssm_ulg and ssm_ung, functions now accept - D as scalar or vector as + * Changed the definition of D in ssm_ulg and ssm_ung, functions now accept + D as scalar or vector as was originally intended. - * Fixed a segfault issue with parallel state sampling in general - ssm_ulg/mlg/ung/mng models caused by calls to R function inside parallel + * Fixed a segfault issue with parallel state sampling in general + ssm_ulg/mlg/ung/mng models caused by calls to R function inside parallel region. - * Fixed a bug from version 1.0.0 in IS1 type sampling which actually lead + * Fixed a bug from version 1.0.0 in IS1 type sampling which actually lead to IS2 type sampling. * Fixed out-of-bounds error in IS3 sampling. - * Fixed weight computations for multivariate nonlinear models in case of + * Fixed weight computations for multivariate nonlinear models in case of psi-APF in some border cases with non-standard H. * Removed Armadillo bound checks for efficiency gains. - + bssm 1.1.1 (Release date: 2021-01-22) ============== - - * Added missing scaling for Gamma distribution in importance sampling + + * Added missing scaling for Gamma distribution in importance sampling weights for added numerical robustness. * Fixed sequential importance sampling for multivariate non-gaussian models. * Fixed simulation smoother for multivariate Gaussian models. bssm 1.1.0 (Release date: 2021-01-19) ============== - - * Added function `suggest_N` which can be used to choose + + * Added function `suggest_N` which can be used to choose suitable number of particles for IS-MCMC. - * Added function `post_correct` which can be used to update + * Added function `post_correct` which can be used to update previous approximate MCMC with IS-weights. - * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. - * The adaptation of the proposal distribution now continues also after the - burn-in by default. + * Gamma priors are now supported in easy-to-use models such as `bsm_lg`. + * The adaptation of the proposal distribution now continues also after the + burn-in by default. * Changed default MCMC type to typically most efficient and robust IS2. - * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` + * Renamed `nsim` argument to `particles` in most of the R functions (`nsim` also works with a warning). - * Fixed a bug with bsm models with covariates, where all standard deviation + * Fixed a bug with bsm models with covariates, where all standard deviation parameters were fixed. This resulted error within MCMC algorithms. - * Fixed a dimension drop bug in the predict method which caused error for + * Fixed a dimension drop bug in the predict method which caused error for univariate models. * Fixed some docs and added more examples. * Fixed few typos in vignette (thanks Kyle Hussman) * Reduced runtime of MCMC in growth model vignette as requested by CRAN. - + bssm 1.0.1-1 (Release date: 2020-11-12) ============== - * Added an argument `future` for predict method which allows - predictions for current time points by supplying the original model - (e.g., for posterior predictive checks). + * Added an argument `future` for predict method which allows + predictions for current time points by supplying the original model + (e.g., for posterior predictive checks). At the same time the argument name `future_model` was changed to `model`. - * Fixed a bug in summary.mcmc_run which resulted error when + * Fixed a bug in summary.mcmc_run which resulted error when trying to obtain summary for states only. - * Added a check for Kalman filter for a degenerate case where all + * Added a check for Kalman filter for a degenerate case where all observational level and state level variances are zero. - * Renamed argument `n_threads` to `threads` for consistency + * Renamed argument `n_threads` to `threads` for consistency with `iter` and `burnin` arguments. * Improved documentation, added examples. * Added a vignette regarding psi-APF for non-linear models. - + bssm 1.0.0 (Release date: 2020-06-09) ============== Major update - * Major changes for model definitions, now model updating and priors - can be defined via R functions (non-linear and SDE models still rely on + * Major changes for model definitions, now model updating and priors + can be defined via R functions (non-linear and SDE models still rely on C++ snippets). * Added support for multivariate non-Gaussian models. * Added support for gamma distributions. - * Added the function as.data.frame for mcmc output which converts the MCMC + * Added the function as.data.frame for mcmc output which converts the MCMC samples to data.frame format for easier post-processing. * Added truncated normal prior. - * Many argument names and model building functions have been changed for + * Many argument names and model building functions have been changed for clarity and consistency. - * Major overhaul of C++ internals which can bring minor efficiency gains + * Major overhaul of C++ internals which can bring minor efficiency gains and smaller installation size. - * Allow zero as initial value for positive-constrained parameters of bsm + * Allow zero as initial value for positive-constrained parameters of bsm models. - * Small changes to summary method which can now return also only summaries + * Small changes to summary method which can now return also only summaries of the states. - * Fixed a bug in initializing run_mcmc for negative binomial model. + * Fixed a bug in initializing run_mcmc for negative binomial model. * Fixed a bug in phi-APF for non-linear models. - * Reimplemented predict method which now always produces data frame of + * Reimplemented predict method which now always produces data frame of samples. - + bssm 0.1.11 (Release date: 2020-02-25) ============== - * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, + * Switched (back) to approximate posterior in RAM for PM-SPDK and PM-PSI, as it seems to work better with noisy likelihood estimates. * Print and summary methods for MCMC output are now coherent in their output. - + bssm 0.1.10 (Release date: 2020-02-04) ============== * Fixed missing weight update for IS-SPDK without OPENMP flag. * Removed unused usage argument ... from expand_sample. - + bssm 0.1.9 (Release date: 2020-01-27) ============== * Fixed state sampling for PM-MCMC with SPDK. * Added ts attribute for svm model. * Corrected asymptotic variance for summary methods. - + bssm 0.1.8-1 (Release date: 2019-12-20) ============== * Tweaked tests in order to pass MKL case at CRAN. bssm 0.1.8 (Release date: 2019-09-23) ============== - * Fixed a bug in predict method which prevented the method working in case + * Fixed a bug in predict method which prevented the method working in case of ngssm models. - * Fixed a bug in predict method which threw an error due to dimension drop of + * Fixed a bug in predict method which threw an error due to dimension drop of models with single state. * Fixed issues with the vignette. bssm 0.1.7 (Release date: 2019-03-19) ============== - * Fixed a bug in EKF smoother which resulted wrong smoothed state estimates - in case of partially missing multivariate observations. Thanks for Santeri - Karppinen for spotting the bug. - * Added twisted SMC based simulation smoothing algorithm for Gaussian models, + * Fixed a bug in EKF smoother which resulted wrong smoothed state estimates + in case of partially missing multivariate observations. Thanks for Santeri + Karppinen for spotting the bug. + * Added twisted SMC based simulation smoothing algorithm for Gaussian models, as an alternative to Kalman smoother based simulation. - + bssm 0.1.6-1 (Release date: 2018-11-20) ============== - * Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik + * Fixed wrong dimension declarations in pseudo-marginal MCMC and logLik methods for SDE and ng_ar1 models. * Added a missing Jacobian for ng_bsm and bsm models using IS-correction. - * Changed internal parameterization of ng_bsm and bsm models from + * Changed internal parameterization of ng_bsm and bsm models from log(1+theta) to log(theta). - + bssm 0.1.5 (Release date: 2018-05-23) ============== - * Fixed the Cholesky decomposition in filtering recursions of multivariate + * Fixed the Cholesky decomposition in filtering recursions of multivariate models. * as_gssm now works for multivariate Gaussian models of KFAS as well. * Fixed several issues regarding partially missing observations in multivariate models. * Added the MASS package to Suggests as it is used in some unit tests. * Added missing type argument to SDE MCMC call with delayed acceptance. - + bssm 0.1.4-1 (Release date: 2018-02-04) ============== * Fixed the use of uninitialized values in psi-filter from version 0.1.3. bssm 0.1.4 (Release date: 2018-02-04) ============== - * MCMC output can now be defined with argument `type`. Instead of returning - joint posterior samples, run_mcmc can now return only marginal samples of + * MCMC output can now be defined with argument `type`. Instead of returning + joint posterior samples, run_mcmc can now return only marginal samples of theta, or summary statistics of the states. - * Due to the above change, argument `sim_states` was removed from the + * Due to the above change, argument `sim_states` was removed from the Gaussian MCMC methods. - * MCMC functions are now less memory intensive, especially with + * MCMC functions are now less memory intensive, especially with `type="theta"`. bssm 0.1.3 (Release date: 2018-01-07) ============== * Streamlined the output of the print method for MCMC results. - * Fixed major bugs in predict method which caused wrong values for the + * Fixed major bugs in predict method which caused wrong values for the prediction intervals. * Fixed some package dependencies. - * Sampling for standard deviation parameters of BSM and their non-Gaussian - counterparts is now done in logarithmic scale for slightly increased + * Sampling for standard deviation parameters of BSM and their non-Gaussian + counterparts is now done in logarithmic scale for slightly increased efficiency. - * Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1) + * Added a new model class ar1 for univariate (possibly noisy) Gaussian AR(1) processes. - * MCMC output now includes posterior predictive distribution of states for + * MCMC output now includes posterior predictive distribution of states for one step ahead to the future. - + bssm 0.1.2 (Release date: 2017-11-21) ============== * API change for run_mcmc: All MCMC methods are now under the argument - method, instead of having separate arguments for delayed acceptance and IS + method, instead of having separate arguments for delayed acceptance and IS schemes. - * summary method for MCMC output now omits the computation of SE and ESS in + * summary method for MCMC output now omits the computation of SE and ESS in order to speed up the function. - * Added new model class lgg_ssm, which is a linear-Gaussian model defined + * Added new model class lgg_ssm, which is a linear-Gaussian model defined directly via C++ like non-linear ssm_nlg models. This allows more flexible prior definitions and complex system matrix constructions. - * Added another new model class, ssm_sde, which is a model with continuous - state dynamics defined as SDE. These too are defined via couple + * Added another new model class, ssm_sde, which is a model with continuous + state dynamics defined as SDE. These too are defined via couple simple C++ functions. * Added non-gaussian AR(1) model class. * Added argument nsim for predict method, which allows multiple draws per MCMC iteration. - * The noise multiplier matrices H and R in ssm_nlg models can now depend on + * The noise multiplier matrices H and R in ssm_nlg models can now depend on states. - + bssm 0.1.1-1 (Release date: 2017-06-27) ============== * Use byte compiler. * Skip tests relying in certain numerical precision on CRAN. - + bssm 0.1.1 (Release date: 2017-06-27) ============== - + * Switched from C++11 PRNGs to sitmo. * Fixed some portability issues in C++ codes. diff --git a/codemeta.json b/codemeta.json index 091bc6d7..3afede09 100644 --- a/codemeta.json +++ b/codemeta.json @@ -1,22 +1,19 @@ { - "@context": [ - "https://doi.org/10.5063/schema/codemeta-2.0", - "http://schema.org" - ], + "@context": "https://doi.org/10.5063/schema/codemeta-2.0", "@type": "SoftwareSourceCode", "identifier": "bssm", - "description": "Efficient methods for Bayesian inference of state space models \n via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel \n importance sampling type weighted estimators \n (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>). \n Gaussian, Poisson, binomial, negative binomial, and Gamma\n observation densities and basic stochastic volatility models \n with linear-Gaussian state dynamics, \n as well as general non-linear Gaussian models and discretised \n diffusion models are supported.", + "description": "Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.", "name": "bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space\n Models", "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "2.0.0", + "version": "2.0.1", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", "url": "https://r-project.org" }, - "runtimePlatform": "R Under development (unstable) (2021-04-12 r80161)", + "runtimePlatform": "R version 4.1.2 (2021-11-01)", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", @@ -38,9 +35,6 @@ "@id": "https://orcid.org/0000-0002-8041-7222" } ], - "contributor": {}, - "copyrightHolder": {}, - "funder": {}, "maintainer": [ { "@type": "Person", @@ -176,14 +170,14 @@ "sameAs": "https://CRAN.R-project.org/package=testthat" } ], - "softwareRequirements": [ - { + "softwareRequirements": { + "1": { "@type": "SoftwareApplication", "identifier": "R", "name": "R", "version": ">= 3.5.0" }, - { + "2": { "@type": "SoftwareApplication", "identifier": "magrittr", "name": "magrittr", @@ -195,7 +189,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=magrittr" }, - { + "3": { "@type": "SoftwareApplication", "identifier": "checkmate", "name": "checkmate", @@ -207,7 +201,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=checkmate" }, - { + "4": { "@type": "SoftwareApplication", "identifier": "coda", "name": "coda", @@ -220,7 +214,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=coda" }, - { + "5": { "@type": "SoftwareApplication", "identifier": "diagis", "name": "diagis", @@ -232,7 +226,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=diagis" }, - { + "6": { "@type": "SoftwareApplication", "identifier": "dplyr", "name": "dplyr", @@ -244,7 +238,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=dplyr" }, - { + "7": { "@type": "SoftwareApplication", "identifier": "posterior", "name": "posterior", @@ -256,7 +250,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=posterior" }, - { + "8": { "@type": "SoftwareApplication", "identifier": "Rcpp", "name": "Rcpp", @@ -269,7 +263,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=Rcpp" }, - { + "9": { "@type": "SoftwareApplication", "identifier": "rlang", "name": "rlang", @@ -281,7 +275,7 @@ }, "sameAs": "https://CRAN.R-project.org/package=rlang" }, - { + "10": { "@type": "SoftwareApplication", "identifier": "tidyr", "name": "tidyr", @@ -293,28 +287,9 @@ }, "sameAs": "https://CRAN.R-project.org/package=tidyr" }, - { - "@type": "SoftwareApplication", - "identifier": "https://sysreqs.r-hub.io/get/pandoc" - }, - { - "@type": "SoftwareApplication", - "identifier": "https://sysreqs.r-hub.io/get/cxx11" - } - ], - "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS", - "readme": "https://github.com/helske/bssm/blob/master/README.md", - "fileSize": "1180.262KB", - "contIntegration": ["https://github.com/helske/bssm/actions", "https://codecov.io/gh/helske/bssm?branch=master"], - "keywords": [ - "bayesian-inference", - "markov-chain-monte-carlo", - "particle-filter", - "time-series", - "state-space", - "r", - "cpp" - ], + "SystemRequirements": "C++11, pandoc (>= 1.12.3, needed for vignettes)" + }, + "fileSize": "2345.1KB", "citation": [ { "@type": "ScholarlyArticle", @@ -332,13 +307,18 @@ } ], "name": "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", - "url": "https://arxiv.org/abs/2101.08492", - "description": "To appear", + "identifier": "10.32614/RJ-2021-103", + "url": "https://doi.org/10.32614/RJ-2021-103", + "pagination": "578--589", + "@id": "https://doi.org/10.32614/RJ-2021-103", + "sameAs": "https://doi.org/10.32614/RJ-2021-103", "isPartOf": { "@type": "PublicationIssue", + "issueNumber": "2", "datePublished": "2021", "isPartOf": { "@type": ["PublicationVolume", "Periodical"], + "volumeNumber": "13", "name": "R Journal" } } @@ -378,5 +358,10 @@ } } ], - "developmentStatus": "https://www.repostatus.org/#active" + "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS", + "readme": "https://github.com/helske/bssm/blob/master/README.md", + "contIntegration": ["https://github.com/helske/bssm/actions", "https://app.codecov.io/gh/helske/bssm?branch=master"], + "developmentStatus": "https://www.repostatus.org/#active", + "keywords": ["bayesian-inference", "markov-chain-monte-carlo", "particle-filter", "time-series", "state-space", "r", "cpp"], + "relatedLink": "https://CRAN.R-project.org/package=bssm" } diff --git a/inst/CITATION b/inst/CITATION index 1292f448..edc8ab09 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -2,12 +2,15 @@ c( bibentry( bibtype = "Article", key = "helske-vihola2021", - title= "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", - author="Jouni Helske and Matti Vihola", - year="2021", + title = "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in {R}", + author =" Jouni Helske and Matti Vihola", + year= "2021", + doi = "10.32614/RJ-2021-103", journal = "R Journal", - note = "To appear", - url = "https://arxiv.org/abs/2101.08492" + url = "https://doi.org/10.32614/RJ-2021-103", + pages = "578--589", + volume = "13", + number = "2" ), bibentry( bibtype = "Article", @@ -19,4 +22,4 @@ c( year = "2020", key = "vihola-helske-franks" ) -) \ No newline at end of file +) diff --git a/tests/testthat/test_basics.R b/tests/testthat/test_basics.R index 90019491..f0f5778f 100644 --- a/tests/testthat/test_basics.R +++ b/tests/testthat/test_basics.R @@ -2,6 +2,8 @@ context("Test basics") #' @srrstats {G5.4, G5.4b, G5.6, G5.6a, G5.6b, G5.7} Compare with KFAS. +tol <- 1e-6 + test_that("results for Gaussian models are comparable to KFAS", { library("KFAS") model_KFAS <- SSModel(1:10 ~ SSMtrend(2, Q = list(0.01^2, 0)), H = 2) @@ -14,15 +16,15 @@ test_that("results for Gaussian models are comparable to KFAS", { sd_y = 0.01)) model_bssm <- bsm_lg(1:10, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, sd_y = sqrt(2)) - + expect_equal(logLik(model_KFAS, convtol = 1e-12), logLik(model_bssm, 0)) out_KFAS <- KFS(model_KFAS, filtering = "state", convtol = 1e-12) expect_error(out_bssm <- kfilter(model_bssm), NA) - expect_equivalent(out_KFAS$a, out_bssm$at) - expect_equivalent(out_KFAS$P, out_bssm$Pt) + expect_equivalent(out_KFAS$a, out_bssm$at, tolerance = tol) + expect_equivalent(out_KFAS$P, out_bssm$Pt, tolerance = tol) expect_error(out_bssm <- smoother(model_bssm), NA) - expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat) - expect_equivalent(out_KFAS$V, out_bssm$Vt) + expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat, tolerance = tol) + expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = tol) }) test_that("results for multivariate Gaussian model are comparable to KFAS", { @@ -36,84 +38,85 @@ test_that("results for multivariate Gaussian model are comparable to KFAS", { Q = matrix(1), P1inf = diag(2)) + SSMseasonal(period = 12, sea.type = "trigonometric"), data = Seatbelts, H = matrix(NA, 2, 2)) - + diag(kfas_model$P1) <- 50 diag(kfas_model$P1inf) <- 0 - kfas_model$H <- structure(c(0.00544500509177812, 0.00437558178720609, + kfas_model$H <- structure(c(0.00544500509177812, 0.00437558178720609, 0.00437558178720609, 0.00885692410165593), .Dim = c(2L, 2L, 1L)) kfas_model$R <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0152150188066314, 0.0144897116711475 - ), .Dim = c(29L, 1L, 1L), .Dimnames = list(c("log(PetrolPrice).front", - "log(kms).front", "log(PetrolPrice).rear", "log(kms).rear", "law.front", - "sea_trig1.front", "sea_trig*1.front", "sea_trig2.front", - "sea_trig*2.front", "sea_trig3.front", "sea_trig*3.front", - "sea_trig4.front", "sea_trig*4.front", - "sea_trig5.front", "sea_trig*5.front", "sea_trig6.front", "sea_trig1.rear", - "sea_trig*1.rear", "sea_trig2.rear", "sea_trig*2.rear", "sea_trig3.rear", - "sea_trig*3.rear", "sea_trig4.rear", "sea_trig*4.rear", "sea_trig5.rear", - "sea_trig*5.rear", "sea_trig6.rear", "custom1", "custom2"), NULL, + ), .Dim = c(29L, 1L, 1L), .Dimnames = list(c("log(PetrolPrice).front", + "log(kms).front", "log(PetrolPrice).rear", "log(kms).rear", "law.front", + "sea_trig1.front", "sea_trig*1.front", "sea_trig2.front", + "sea_trig*2.front", "sea_trig3.front", "sea_trig*3.front", + "sea_trig4.front", "sea_trig*4.front", + "sea_trig5.front", "sea_trig*5.front", "sea_trig6.front", "sea_trig1.rear", + "sea_trig*1.rear", "sea_trig2.rear", "sea_trig*2.rear", "sea_trig3.rear", + "sea_trig*3.rear", "sea_trig4.rear", "sea_trig*4.rear", "sea_trig5.rear", + "sea_trig*5.rear", "sea_trig6.rear", "custom1", "custom2"), NULL, NULL)) - + bssm_model <- as_bssm(kfas_model) - expect_equivalent(logLik(kfas_model), logLik(bssm_model)) - expect_equivalent(KFS(kfas_model)$alphahat, smoother(bssm_model)$alphahat) - + expect_equivalent(logLik(kfas_model), logLik(bssm_model), tolerance = tol) + expect_equivalent(KFS(kfas_model)$alphahat, + smoother(bssm_model)$alphahat, tolerance = tol) + }) test_that("different smoothers give identical results", { model_bssm <- bsm_lg(log10(AirPassengers), P1 = diag(1e2, 13), sd_slope = 0, - sd_y = uniform(0.005, 0, 10), sd_level = uniform(0.01, 0, 10), + sd_y = uniform(0.005, 0, 10), sd_level = uniform(0.01, 0, 10), sd_seasonal = uniform(0.005, 0, 1)) - + expect_error(out_bssm1 <- smoother(model_bssm), NA) expect_error(out_bssm2 <- fast_smoother(model_bssm), NA) - expect_equivalent(out_bssm2, out_bssm1$alphahat) + expect_equivalent(out_bssm2, out_bssm1$alphahat, tolerance = tol) }) test_that("results for Poisson model are comparable to KFAS", { library("KFAS") set.seed(1) - model_KFAS <- SSModel(rpois(10, exp(0.2) * (2:11)) ~ + model_KFAS <- SSModel(rpois(10, exp(0.2) * (2:11)) ~ SSMtrend(2, Q = list(0.01^2, 0)), distribution = "poisson", u = 2:11) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - + model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, u = 2:11, distribution = "poisson") - + expect_equal(logLik(model_KFAS), logLik(model_bssm, 0)) out_KFAS <- KFS(model_KFAS, filtering = "state") expect_error(out_bssm <- kfilter(model_bssm), NA) - expect_equivalent(out_KFAS$a, out_bssm$at) - expect_equivalent(out_KFAS$P, out_bssm$Pt) + expect_equivalent(out_KFAS$a, out_bssm$at, tolerance = tol) + expect_equivalent(out_KFAS$P, out_bssm$Pt, tolerance = tol) expect_error(out_bssm <- smoother(model_bssm), NA) - expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat) - expect_equivalent(out_KFAS$V, out_bssm$Vt) + expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat, tolerance = tol) + expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = tol) }) test_that("results for binomial model are comparable to KFAS", { library("KFAS") set.seed(1) - model_KFAS <- SSModel(rbinom(10, 2:11, 0.4) ~ + model_KFAS <- SSModel(rbinom(10, 2:11, 0.4) ~ SSMtrend(2, Q = list(0.01^2, 0)), distribution = "binomial", u = 2:11) model_KFAS$P1inf[] <- 0 diag(model_KFAS$P1) <- 1e2 - + model_bssm <- bsm_ng(model_KFAS$y, P1 = diag(1e2, 2), sd_slope = 0, sd_level = 0.01, u = 2:11, distribution = "binomial") - + expect_equal(logLik(model_KFAS), logLik(model_bssm, 0)) out_KFAS <- KFS(model_KFAS, filtering = "state") expect_error(out_bssm <- kfilter(model_bssm), NA) - expect_equivalent(out_KFAS$a, out_bssm$at) - expect_equivalent(out_KFAS$P, out_bssm$Pt) + expect_equivalent(out_KFAS$a, out_bssm$at, tolerance = tol) + expect_equivalent(out_KFAS$P, out_bssm$Pt, tolerance = tol) expect_error(out_bssm <- smoother(model_bssm), NA) - expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat) - expect_equivalent(out_KFAS$V, out_bssm$Vt) + expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat, tolerance = tol) + expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = tol) }) test_that("results for bivariate non-Gaussian model are comparable to KFAS", { @@ -123,75 +126,75 @@ test_that("results for bivariate non-Gaussian model are comparable to KFAS", { x1 <- cumsum(rnorm(n)) x2 <- cumsum(rnorm(n, sd = 0.2)) u <- rep(c(1, 15), c(4, 6)) - y <- cbind(rbinom(n, size = u, prob = plogis(x1)), + y <- cbind(rbinom(n, size = u, prob = plogis(x1)), rpois(n, u * exp(x1 + x2)), rgamma(n, 10, 10 / exp(x2)), rnorm(n, x2, 0.1)) - - model_KFAS <- SSModel(y ~ - SSMtrend(1, Q = 1, a1 = -0.5, P1 = 0.5, type = "common", index = 1:2) + + + model_KFAS <- SSModel(y ~ + SSMtrend(1, Q = 1, a1 = -0.5, P1 = 0.5, type = "common", index = 1:2) + SSMtrend(1, Q = 0.2^2, P1 = 1, type = "common", index = 2:4), - distribution = c("binomial", "poisson", "gamma", "gaussian"), + distribution = c("binomial", "poisson", "gamma", "gaussian"), u = cbind(u, u, 10, 0.1^2)) model_bssm <- as_bssm(model_KFAS) - + approx_bssm <- gaussian_approx(model_bssm, conv_tol = 1e-16) approx_KFAS <- approxSSM(model_KFAS, tol = 1e-16) - - expect_equivalent(approx_bssm$y, approx_KFAS$y, tol = 1e-8) - expect_equivalent(approx_bssm$H^2, approx_KFAS$H, tol = 1e-8) - - expect_equivalent(logLik(model_KFAS, nsim = 0), - logLik(model_bssm, particles = 0), tol = 1e-8) - expect_equivalent(logLik(model_KFAS, nsim = 100, seed = 1), - logLik(model_bssm, particles = 100, method = "spdk", seed = 1), + + expect_equivalent(approx_bssm$y, approx_KFAS$y, tolerance = tol) + expect_equivalent(approx_bssm$H^2, approx_KFAS$H, tolerance = tol) + + expect_equivalent(logLik(model_KFAS, nsim = 0), + logLik(model_bssm, particles = 0), tolerance = tol) + expect_equivalent(logLik(model_KFAS, nsim = 100, seed = 1), + logLik(model_bssm, particles = 100, method = "spdk", seed = 1), tolerance = 1) - + expect_equivalent( logLik(model_bssm, particles = 100, method = "psi", seed = 1), - logLik(model_bssm, particles = 100, method = "spdk", seed = 1), + logLik(model_bssm, particles = 100, method = "spdk", seed = 1), tolerance = 1) - + # note large tolerance due to the sd of bsf expect_equivalent( logLik(model_bssm, particles = 100, method = "psi", seed = 1), - logLik(model_bssm, particles = 100, method = "bsf", seed = 1), + logLik(model_bssm, particles = 100, method = "bsf", seed = 1), tolerance = 10) - + out_KFAS <- KFS(model_KFAS) expect_error(out_bssm <- smoother(model_bssm), NA) - expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat, tolerance = 1e-8) - expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = 1e-8) + expect_equivalent(out_KFAS$alphahat, out_bssm$alphahat, tolerance = tol) + expect_equivalent(out_KFAS$V, out_bssm$Vt, tolerance = tol) is_KFAS <- importanceSSM(model_KFAS, nsim = 1e4) expect_error(is_bssm <- importance_sample(model_bssm, nsim = 1e4), NA) - expect_equivalent(apply(is_bssm$alpha, 1:2, mean)[1:n, ], + expect_equivalent(apply(is_bssm$alpha, 1:2, mean)[1:n, ], apply(is_KFAS$samples, 1:2, mean), tolerance = 0.1) - expect_equivalent(apply(is_bssm$alpha, 1:2, sd)[1:n, ], + expect_equivalent(apply(is_bssm$alpha, 1:2, sd)[1:n, ], apply(is_KFAS$samples, 1:2, sd), tolerance = 0.1) }) test_that("multivariate normal pdf works", { - - expect_equivalent(bssm:::dmvnorm(1, 3, matrix(2, 1, 1), TRUE, TRUE), - dnorm(1, 3, 2, log = TRUE)) - expect_equivalent(bssm:::dmvnorm(1, 3, matrix(4, 1, 1), TRUE, TRUE), - dnorm(1, 3, 4, log = TRUE)) - + + expect_equivalent(bssm:::dmvnorm(1, 3, matrix(2, 1, 1), TRUE, TRUE), + dnorm(1, 3, 2, log = TRUE), tolerance = tol) + expect_equivalent(bssm:::dmvnorm(1, 3, matrix(4, 1, 1), TRUE, TRUE), + dnorm(1, 3, 4, log = TRUE), tolerance = tol) + set.seed(1) a <- crossprod(matrix(rnorm(9), 3, 3)) logp1 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) expect_equivalent(logp1, -14.0607446337904, tolerance = 1e-6) - + chola <- t(chol(a)) - logp2 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), chola, TRUE, TRUE), + logp2 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), chola, TRUE, TRUE), NA) - expect_equivalent(logp2, logp1, tolerance = 1e-8) - + expect_equivalent(logp2, logp1, tolerance = tol) + b <- matrix(0, 3, 3) constant <- bssm:::precompute_dmvnorm(a, b, 0:2) - expect_equivalent(logp1, + expect_equivalent(logp1, bssm:::fast_dmvnorm(1:3, -0.1 * (3:1), b, 0:2, constant), tolerance = 1e-8) - + a[2, ] <- a[, 2] <- 0 logp3 <- expect_error(bssm:::dmvnorm(1:3, -0.1 * (3:1), a, FALSE, TRUE), NA) expect_equivalent(logp3, -12.5587625856078, tolerance = 1e-6) From d1d826d0899e58f6f02e91aedf48bbbc8c59748e Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 2 May 2022 08:34:34 +0300 Subject: [PATCH 160/180] remove ubuntu devel test --- .github/workflows/R-CMD-check.yaml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 07afb6ff..64d34922 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -20,7 +20,6 @@ jobs: config: - {os: macOS-latest, r: 'release'} - {os: windows-latest, r: 'release'} - - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - {os: ubuntu-latest, r: 'oldrel/1'} @@ -62,7 +61,7 @@ jobs: with: name: ${{ runner.os }}-r${{ matrix.config.r }}-results path: check - + - name: Test coverage run: covr::codecov() - shell: Rscript {0} \ No newline at end of file + shell: Rscript {0} From a2b006d29e6baa805daa59aebc3d9a6b031c00a7 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Mon, 2 May 2022 20:47:11 +0300 Subject: [PATCH 161/180] update docs --- R/bssm-package.R | 145 ++++++------ R/run_mcmc.R | 538 +++++++++++++++++++++---------------------- README.Rmd | 8 +- man/bssm.Rd | 6 +- man/drownings.Rd | 6 +- man/exchange.Rd | 5 +- man/negbin_model.Rd | 12 +- man/negbin_series.Rd | 6 +- man/run_mcmc.Rd | 84 +++---- vignettes/bssm.Rmd | 2 +- 10 files changed, 406 insertions(+), 406 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index 1487d2a6..c7285764 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -1,52 +1,52 @@ -#' +#' #' Bayesian Inference of State Space Models #' -#' This package contains functions for efficient Bayesian inference of state +#' This package contains functions for efficient Bayesian inference of state #' space models (SSMs), where model is assumed to be either -#' -#' * Exponential family state space models, where the state equation is linear -#' Gaussian, and the conditional observation density is either Gaussian, -#' Poisson, binomial, negative binomial or Gamma density. -#' +#' +#' * Exponential family state space models, where the state equation is linear +#' Gaussian, and the conditional observation density is either Gaussian, +#' Poisson, binomial, negative binomial or Gamma density. +#' #' * Basic stochastic volatility model. -#' +#' #' * General non-linear model with Gaussian noise terms. -#' -#' * Model with continuous SDE dynamics. -#' -#' Missing values in response series are allowed as per SSM theory and can be -#' automatically predicted, but there can be no missing values in the system +#' +#' * Model with continuous SDE dynamics. +#' +#' Missing values in response series are allowed as per SSM theory and can be +#' automatically predicted, but there can be no missing values in the system #' matrices of the model. -#' -#' The \code{bssm} package includes several MCMC sampling and sequential Monte -#' Carlo methods for models outside classic linear-Gaussian framework. For -#' definitions of the currently supported models and methods, usage of the -#' package as well as some theory behind the novel IS-MCMC and -#' \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, -#' Helske, Franks (2020), and the package vignettes. -#' -#' @references -#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. R Journal (to appear). -#' https://arxiv.org/abs/2101.08492 -#' -#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators -#' based on approximate marginal Markov chain Monte Carlo. +#' +#' The \code{bssm} package includes several MCMC sampling and sequential Monte +#' Carlo methods for models outside classic linear-Gaussian framework. For +#' definitions of the currently supported models and methods, usage of the +#' package as well as some theory behind the novel IS-MCMC and +#' \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, +#' Helske, Franks (2020), and the package vignettes. +#' +#' @references +#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +#' https://doi.org/10.32614/RJ-2021-103 +#' +#' Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators +#' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @docType package #' @name bssm #' @aliases bssm #' @importFrom Rcpp evalCpp -#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start +#' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples -#' model <- bsm_lg(Nile, +#' model <- bsm_lg(Nile, #' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), #' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), #' a1 = 1000, P1 = 500^2) -#' +#' #' fit <- run_mcmc(model, iter = 2000) #' fit NULL @@ -54,53 +54,54 @@ NULL #' #' Dataset containing number of deaths by drowning in Finland in 1969-2019, #' corresponding population sizes (in hundreds of thousands), and -#' yearly average summer temperatures (June to August), based on simple -#' unweighted average of three weather stations: Helsinki (Southern Finland), +#' yearly average summer temperatures (June to August), based on simple +#' unweighted average of three weather stations: Helsinki (Southern Finland), #' Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). #' #' @name drownings #' @docType data #' @format A time series object containing 51 observations. -#' @source Statistics Finland +#' @source Statistics Finland #' \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. #' @keywords datasets #' @examples #' data("drownings") #' model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], -#' xreg = drownings[, "summer_temp"], distribution = "poisson", +#' xreg = drownings[, "summer_temp"], distribution = "poisson", #' beta = normal(0, 0, 1), #' sd_level = gamma_prior(0.1,2, 10), sd_slope = gamma_prior(0, 2, 10)) -#' -#' fit <- run_mcmc(model, iter = 5000, +#' +#' fit <- run_mcmc(model, iter = 5000, #' output_type = "summary", mcmc_type = "approx") #' fit #' ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) NULL #' Pound/Dollar daily exchange rates #' -#' Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and +#' Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and #' Koopman (2012). #' #' @name exchange #' @docType data #' @format A vector of length 945. -#' @source \url{http://www.ssfpack.com/DKbook.html}. +#' @source The data used to be available on the www.ssfpack.com/DKbook.html but +#' this page is does not seem to be available anymore. #' @keywords datasets -#' @references -#' James Durbin, Siem Jan Koopman (2012). +#' @references +#' James Durbin, Siem Jan Koopman (2012). #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), #' sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) -#' +#' #' out <- particle_smoother(model, particles = 500) -#' plot.ts(cbind(model$y, exp(out$alphahat))) -NULL +#' plot.ts(cbind(model$y, exp(out$alphahat))) +NULL #' Simulated Poisson Time Series Data #' -#' See example for code for reproducing the data. This was used in +#' See example for code for reproducing the data. This was used in #' Vihola, Helske, Franks (2020). #' #' @srrstats {G5.0, G5.1, G5.4} used in Vihola, Helske, Franks (2020). @@ -108,21 +109,21 @@ NULL #' @docType data #' @format A vector of length 100. #' @keywords datasets -#' @references -#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type -#' estimators based on approximate marginal Markov chain Monte Carlo. +#' @references +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +#' estimators based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -#' -#' @examples +#' +#' @examples #' # The data was generated as follows: #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) #' y <- rpois(100, exp(cumsum(slope + c(0, rnorm(99, sd = 0.1))))) NULL -#' +#' #' Simulated Negative Binomial Time Series Data #' -#' See example for code for reproducing the data. This was used in +#' See example for code for reproducing the data. This was used in #' Helske and Vihola (2021). #' #' @srrstats {G5.1} used in Helske and Vihola (2021). @@ -131,12 +132,12 @@ NULL #' @format A time series \code{mts} object with 200 time points and two series. #' @keywords datasets #' @seealso \code{negbin_model} -#' @references -#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. R Journal (to appear). -#' https://arxiv.org/abs/2101.08492 -#' -#' @examples +#' @references +#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +#' https://doi.org/10.32614/RJ-2021-103 +#' +#' @examples #' # The data was generated as follows: #' set.seed(123) #' n <- 200 @@ -144,16 +145,16 @@ NULL #' drift <- 0.01 #' beta <- -0.9 #' phi <- 5 -#' +#' #' level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) #' x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) #' y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) -#' +#' NULL #' Estimated Negative Binomial Model of Helske and Vihola (2021) #' -#' This model was used in Helske and Vihola (2021), but with larger number of -#' iterations. Here only 2000 iterations were used in order to reduce the size +#' This model was used in Helske and Vihola (2021), but with larger number of +#' iterations. Here only 2000 iterations were used in order to reduce the size #' of the model object in CRAN. #' #' @srrstats {G5.0, G5.1, G5.4, BS7.2} used in Helske and Vihola (2021). @@ -161,24 +162,24 @@ NULL #' @docType data #' @format A object of class \code{mcmc_output}. #' @keywords datasets -#' @references -#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. R Journal (to appear). -#' https://arxiv.org/abs/2101.08492 -#' -#' @examples +#' @references +#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +#' https://doi.org/10.32614/RJ-2021-103 +#' +#' @examples #' # reproducing the model: #' data("negbin_series") #' # Construct model for bssm -#' bssm_model <- bsm_ng(negbin_series[, "y"], +#' bssm_model <- bsm_ng(negbin_series[, "y"], #' xreg = negbin_series[, "x"], #' beta = normal(0, 0, 10), #' phi = halfnormal(1, 10), -#' sd_level = halfnormal(0.1, 1), +#' sd_level = halfnormal(0.1, 1), #' sd_slope = halfnormal(0.01, 0.1), -#' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), +#' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), #' distribution = "negative binomial") -#' +#' #' \donttest{ #' # In the paper we used 60000 iterations with first 10000 as burnin #' fit_bssm <- run_mcmc(bssm_model, iter = 2000, particles = 10, seed = 1) diff --git a/R/run_mcmc.R b/R/run_mcmc.R index d182e762..470d86a6 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -1,124 +1,124 @@ #' Bayesian Inference of State Space Models #' #' Adaptive Markov chain Monte Carlo simulation for SSMs using -#' Robust Adaptive Metropolis algorithm by Vihola (2012). Several different -#' MCMC sampling schemes are implemented, see parameter -#' arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and +#' Robust Adaptive Metropolis algorithm by Vihola (2012). Several different +#' MCMC sampling schemes are implemented, see parameter +#' arguments, package vignette, Vihola, Helske, Franks (2020) and Helske and #' Vihola (2021) for details. #' #' @details -#' +#' #' For linear-Gaussian models, option \code{"summary"} does not simulate -#' states directly but computes the posterior means and variances of states -#' using fast Kalman smoothing. This is slightly faster, -#' more memory efficient and more accurate than calculations based on -#' simulation smoother. In other cases, the means and -#' covariances are computed using the full output of particle filter -#' instead of subsampling one of these as in case of -#' \code{output_type = "full"}. The states are sampled up to the time point n+1 -#' where n is the length of the input time series i.e. the last values are -#' one-step-ahead predictions. (for predicting further, see +#' states directly but computes the posterior means and variances of states +#' using fast Kalman smoothing. This is slightly faster, +#' more memory efficient and more accurate than calculations based on +#' simulation smoother. In other cases, the means and +#' covariances are computed using the full output of particle filter +#' instead of subsampling one of these as in case of +#' \code{output_type = "full"}. The states are sampled up to the time point n+1 +#' where n is the length of the input time series i.e. the last values are +#' one-step-ahead predictions. (for predicting further, see #' \code{?predict.mcmc_output}). -#' -#' Initial values for the sampling are taken from the model object -#' (\code{model$theta}). If you want to continue from previous run, you can -#' reconstruct your original model by plugging in the previously obtained -#' parameters to \code{model$theta}, providing the S matrix for the RAM -#' algorithm and setting \code{burnin = 0}. See example. Note however, that -#' this is not identical as running all the iterations once, due to the -#' RNG "discontinuity" and because even without burnin bssm does include -#' "theta_0" i.e. the initial theta in the final chain (even with +#' +#' Initial values for the sampling are taken from the model object +#' (\code{model$theta}). If you want to continue from previous run, you can +#' reconstruct your original model by plugging in the previously obtained +#' parameters to \code{model$theta}, providing the S matrix for the RAM +#' algorithm and setting \code{burnin = 0}. See example. Note however, that +#' this is not identical as running all the iterations once, due to the +#' RNG "discontinuity" and because even without burnin bssm does include +#' "theta_0" i.e. the initial theta in the final chain (even with #' \code{burnin=0}). -#' +#' #' @importFrom stats tsp #' @importFrom rlang is_interactive #' @param model Model of class \code{bssm_model}. -#' @param iter A positive integer defining the total number of MCMC iterations. -#' Suitable value depends on the model, data, and the choice of specific -#' algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing -#' \code{iter} also increases run time, it is is generally good idea to first +#' @param iter A positive integer defining the total number of MCMC iterations. +#' Suitable value depends on the model, data, and the choice of specific +#' algorithms (\code{mcmc_type} and \code{sampling_method}). As increasing +#' \code{iter} also increases run time, it is is generally good idea to first #' test the performance with a small values, e.g., less than 10000. -#' @param output_type Either \code{"full"} -#' (default, returns posterior samples from the posterior -#' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of -#' theta), or \code{"summary"} (return the mean and variance estimates of the +#' @param output_type Either \code{"full"} +#' (default, returns posterior samples from the posterior +#' \eqn{p(\alpha, \theta | y)}), \code{"theta"} (for marginal posterior of +#' theta), or \code{"summary"} (return the mean and variance estimates of the #' states and posterior samples of theta). See details. -#' @param burnin A positive integer defining the length of the burn-in period -#' which is disregarded from the results. Defaults to \code{iter / 2}. -#' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the +#' @param burnin A positive integer defining the length of the burn-in period +#' which is disregarded from the results. Defaults to \code{iter / 2}. +#' Note that all MCMC algorithms of \code{bssm} use adaptive MCMC during the #' burn-in period in order to find good proposal distribution. -#' @param thin A positive integer defining the thinning rate. All the MCMC -#' algorithms in \code{bssm} use the jump chain representation (see refs), -#' and the thinning is applied to these blocks. Defaults to 1. -#' For IS-corrected methods, larger value can also be -#' statistically more effective. Note: With \code{output_type = "summary"}, -#' the thinning does not affect the computations of the summary statistics in +#' @param thin A positive integer defining the thinning rate. All the MCMC +#' algorithms in \code{bssm} use the jump chain representation (see refs), +#' and the thinning is applied to these blocks. Defaults to 1. +#' For IS-corrected methods, larger value can also be +#' statistically more effective. Note: With \code{output_type = "summary"}, +#' the thinning does not affect the computations of the summary statistics in #' case of pseudo-marginal methods. #' @param gamma Tuning parameter for the adaptation of RAM algorithm. Must be #' between 0 and 1. -#' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. +#' @param target_acceptance Target acceptance rate for MCMC. Defaults to 0.234. #' Must be between 0 and 1. -#' @param S Matrix defining the initial value for the lower triangular matrix +#' @param S Matrix defining the initial value for the lower triangular matrix #' of the RAM algorithm, so that the covariance matrix of the Gaussian proposal -#' distribution is \eqn{SS'}. Note that for some parameters -#' (currently the standard deviation, dispersion, and autoregressive parameters -#' of the BSM and AR(1) models) the sampling is done in unconstrained parameter +#' distribution is \eqn{SS'}. Note that for some parameters +#' (currently the standard deviation, dispersion, and autoregressive parameters +#' of the BSM and AR(1) models) the sampling is done in unconstrained parameter #' space, i.e. internal_theta = log(theta) (and logit(rho) or AR coefficient). #' @param end_adaptive_phase Logical, if \code{TRUE}, S is held fixed after the #' burnin period. Default is \code{FALSE}. -#' @param threads Number of threads for state simulation. Positive integer +#' @param threads Number of threads for state simulation. Positive integer #' (default is 1). #' Note that parallel computing is only used in the post-correction phase of -#' IS-MCMC and when sampling the states in case of (approximate) Gaussian +#' IS-MCMC and when sampling the states in case of (approximate) Gaussian #' models. #' @param seed Seed for the C++ RNG (positive integer). #' @param local_approx If \code{TRUE} (default), Gaussian approximation -#' needed for some of the methods is performed at each iteration. -#' If \code{FALSE}, approximation is updated only once at the start of the +#' needed for some of the methods is performed at each iteration. +#' If \code{FALSE}, approximation is updated only once at the start of the #' MCMC using the initial model. #' @param max_iter Maximum number of iterations used in Gaussian approximation, -#' as a positive integer. +#' as a positive integer. #' Default is 100 (although typically only few iterations are needed). #' @param conv_tol Positive tolerance parameter used in Gaussian approximation. -#' @param particles A positive integer defining the number of state samples per +#' @param particles A positive integer defining the number of state samples per #' MCMC iteration for models other than linear-Gaussian models. -#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable -#' values depend on the model, the data, \code{mcmc_type} and -#' \code{sampling_method}. While large values provide more -#' accurate estimates, the run time also increases with respect to to the -#' number of particles, so it is generally a good idea to test the run time +#' Ignored if \code{mcmc_type} is \code{"approx"} or \code{"ekf"}. Suitable +#' values depend on the model, the data, \code{mcmc_type} and +#' \code{sampling_method}. While large values provide more +#' accurate estimates, the run time also increases with respect to to the +#' number of particles, so it is generally a good idea to test the run time #' firstwith a small number of particles, e.g., less than 100. -#' @param mcmc_type What type of MCMC algorithm should be used for models other +#' @param mcmc_type What type of MCMC algorithm should be used for models other #' than linear-Gaussian models? Possible choices are #' \code{"pm"} for pseudo-marginal MCMC, -#' \code{"da"} for delayed acceptance version of PMCMC , -#' \code{"approx"} for approximate inference based on the Gaussian +#' \code{"da"} for delayed acceptance version of PMCMC , +#' \code{"approx"} for approximate inference based on the Gaussian #' approximation of the model, -#' \code{"ekf"} for approximate inference using extended Kalman filter -#' (for \code{ssm_nlg}), +#' \code{"ekf"} for approximate inference using extended Kalman filter +#' (for \code{ssm_nlg}), #' or one of the three importance sampling type weighting schemes: -#' \code{"is3"} for simple importance sampling (weight is computed for each +#' \code{"is3"} for simple importance sampling (weight is computed for each #' MCMC iteration independently), #' \code{"is2"} for jump chain importance sampling type weighting (default), or -#' \code{"is1"} for importance sampling type weighting where the number of +#' \code{"is1"} for importance sampling type weighting where the number of #' particles used for #' weight computations is proportional to the length of the jump chain block. -#' @param sampling_method Method for state sampling when for models other than -#' linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). +#' @param sampling_method Method for state sampling when for models other than +#' linear-Gaussian models. If \code{"psi"}, \eqn{\psi}-APF is used (default). #' If \code{"spdk"}, non-sequential importance sampling #' based on Gaussian approximation is used. If \code{"bsf"}, bootstrap filter -#' is used. If \code{"ekf"}, particle filter based on EKF-proposals are used +#' is used. If \code{"ekf"}, particle filter based on EKF-proposals are used #' (only for \code{ssm_nlg} models). -#' @param iekf_iter Non-negative integer. The default zero corresponds to -#' normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF -#' with \code{iekf_iter} iterations. Used only for models of class +#' @param iekf_iter Non-negative integer. The default zero corresponds to +#' normal EKF, whereas \code{iekf_iter > 0} corresponds to iterated EKF +#' with \code{iekf_iter} iterations. Used only for models of class #' \code{ssm_nlg}. -#' @param L_c,L_f For \code{ssm_sde} models, Positive integer values defining -#' the discretization levels for first and second stages (defined as 2^L). +#' @param L_c,L_f For \code{ssm_sde} models, Positive integer values defining +#' the discretization levels for first and second stages (defined as 2^L). #' For pseudo-marginal methods (\code{"pm"}), maximum of these is used. -#' @param verbose If \code{TRUE}, prints a progress bar to the console. If +#' @param verbose If \code{TRUE}, prints a progress bar to the console. If #' missing, defined by \code{rlang::is_interactive}. -#' Set to \code{FALSE} if number of iterations is less than 50. +#' Set to \code{FALSE} if number of iterations is less than 50. #' @param ... Ignored. #' @return An object of class \code{mcmc_output}. #' @export @@ -127,92 +127,92 @@ #' @srrstats {BS2.6} #' @srrstats {BS2.7} Illustrated in the examples. #' @srrstats {BS2.7, BS1.3, BS1.3a, BS1.3b, BS2.8} Explained in docs. -#' @srrstats {BS2.9} The argument 'seed' is set to random value if not +#' @srrstats {BS2.9} The argument 'seed' is set to random value if not #' specified by the user. -#' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the -#' input model, whereas some metadata (like the class of input model and seed) +#' @srrstats {BS5.0, BS5.1, BS5.2} Starting values are integrated into the +#' input model, whereas some metadata (like the class of input model and seed) #' is returned by run_mcmc. -#' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off +#' @srrstats {BS2.12, BS2.13} There is a progress bar which can be switched off #' with \code{verbose = FALSE}. #' @srrstats {BS1.2c} Examples on defining priors. #' @srrstats {BS2.14} No warnings are issues during MCMC. #' @rdname run_mcmc -#' @references +#' @references #' Vihola M (2012). Robust adaptive Metropolis algorithm with #' coerced acceptance rate. Statistics and Computing, 22(5), p 997-1008. #' https://doi.org/10.1007/s11222-011-9269-5 -#' -#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type -#' estimators based on approximate marginal Markov chain Monte Carlo. +#' +#' Vihola, M, Helske, J, Franks, J (2020). Importance sampling type +#' estimators based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -#' -#' Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -#' Non-Gaussian State Space Models in R. R Journal (to appear). -#' https://arxiv.org/abs/2101.08492 -#' +#' +#' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +#' Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +#' https://doi.org/10.32614/RJ-2021-103 +#' run_mcmc <- function(model, ...) { UseMethod("run_mcmc", model) } #' @method run_mcmc lineargaussian #' @rdname run_mcmc #' @export -#' @examples -#' model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), -#' sigma = halfnormal(1, 10), mu = normal(500, 500, 500), +#' @examples +#' model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), +#' sigma = halfnormal(1, 10), mu = normal(500, 500, 500), #' sd_y = halfnormal(1, 10)) -#' +#' #' mcmc_results <- run_mcmc(model, iter = 2e4) #' summary(mcmc_results, return_se = TRUE) -#' +#' #' sumr <- summary(mcmc_results, variable = "states") #' library("ggplot2") -#' ggplot(sumr, aes(time, Mean)) + -#' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.25) + +#' ggplot(sumr, aes(time, Mean)) + +#' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.25) + #' geom_line() + theme_bw() + #' geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), #' col = 2) -#' +#' #' # Continue from the previous run #' model$theta[] <- mcmc_results$theta[nrow(mcmc_results$theta), ] #' run_more <- run_mcmc(model, S = mcmc_results$S, iter = 1000, burnin = 0) -#' +#' run_mcmc.lineargaussian <- function(model, iter, output_type = "full", burnin = floor(iter / 2), thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, - seed = sample(.Machine$integer.max, size = 1), + seed = sample(.Machine$integer.max, size = 1), verbose, ...) { - + check_missingness(model) - - if (!test_flag(end_adaptive_phase)) + + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - + threads <- check_intmax(threads, "threads") thin <- check_intmax(thin, "thin", max = 100) iter <- check_intmax(iter, "iter", max = 1e12) burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") - + if (missing(verbose)) { verbose <- is_interactive() } else { - if (!test_flag(verbose)) + if (!test_flag(verbose)) stop("Argument 'verbose' should be TRUE or FALSE. ") } if (iter < 50) verbose <- FALSE - - if (length(model$theta) == 0) + + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - + check_prop(target_acceptance) check_prop(gamma, "gamma") output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) - + if (inherits(model, "bsm_lg")) { names_ind <- !model$fixed & c(TRUE, TRUE, model$slope, model$seasonal) - transformed <- + transformed <- c("sd_y", "sd_level", "sd_slope", "sd_seasonal")[names_ind] model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) } else { @@ -221,23 +221,23 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) } } - + if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } if(output_type == "full") { - nsamples <- - ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { - warning(paste("Number of state samples to be stored is approximately", + warning(paste("Number of state samples to be stored is approximately", nsamples, "you might run out of memory.")) } } out <- gaussian_mcmc(model, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, model_type(model), verbose) - + if (output_type == 1) { colnames(out$alpha) <- names(model$a1) } else { @@ -248,10 +248,10 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", frequency = frequency(model$y)) } } - - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) - + if (inherits(model, "bsm_lg")) { out$theta[, transformed] <- exp(out$theta[, transformed]) } else { @@ -270,8 +270,8 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] - attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), + attr(out, "ts") <- + list(start = start(model$y), end = end(model$y), frequency = frequency(model$y)) out } @@ -280,129 +280,129 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", #' @export #' @examples #' set.seed(1) -#' n <- 50 +#' n <- 50 #' slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) #' level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) #' y <- rpois(n, exp(level)) -#' poisson_model <- bsm_ng(y, -#' sd_level = halfnormal(0.01, 1), -#' sd_slope = halfnormal(0.01, 0.1), +#' poisson_model <- bsm_ng(y, +#' sd_level = halfnormal(0.01, 1), +#' sd_slope = halfnormal(0.01, 0.1), #' P1 = diag(c(10, 0.1)), distribution = "poisson") -#' +#' #' # Note small number of iterations for CRAN checks -#' mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, +#' mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, #' mcmc_type = "da") #' summary(mcmc_out, what = "theta", return_se = TRUE) -#' +#' #' set.seed(123) #' n <- 50 #' sd_level <- 0.1 #' drift <- 0.01 #' beta <- -0.9 #' phi <- 5 -#' +#' #' level <- cumsum(c(5, drift + rnorm(n - 1, sd = sd_level))) #' x <- 3 + (1:n) * drift + sin(1:n + runif(n, -1, 1)) #' y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) -#' +#' #' model <- bsm_ng(y, xreg = x, #' beta = normal(0, 0, 10), #' phi = halfnormal(1, 10), -#' sd_level = halfnormal(0.1, 1), +#' sd_level = halfnormal(0.1, 1), #' sd_slope = halfnormal(0.01, 0.1), -#' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), +#' a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), #' distribution = "negative binomial") -#' +#' #' # run IS-MCMC #' # Note small number of iterations for CRAN checks #' fit <- run_mcmc(model, iter = 4000, #' particles = 10, mcmc_type = "is2", seed = 1) #' -#' # extract states +#' # extract states #' d_states <- as.data.frame(fit, variable = "states", time = 1:n) -#' +#' #' library("dplyr") #' library("ggplot2") -#' +#' #' # compute summary statistics -#' level_sumr <- d_states %>% +#' level_sumr <- d_states %>% #' filter(variable == "level") %>% #' group_by(time) %>% -#' summarise(mean = diagis::weighted_mean(value, weight), -#' lwr = diagis::weighted_quantile(value, weight, -#' 0.025), -#' upr = diagis::weighted_quantile(value, weight, +#' summarise(mean = diagis::weighted_mean(value, weight), +#' lwr = diagis::weighted_quantile(value, weight, +#' 0.025), +#' upr = diagis::weighted_quantile(value, weight, #' 0.975)) -#' +#' #' # visualize -#' level_sumr %>% ggplot(aes(x = time, y = mean)) + +#' level_sumr %>% ggplot(aes(x = time, y = mean)) + #' geom_line() + #' geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + #' geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + -#' theme_bw() + -#' theme(legend.title = element_blank()) + +#' theme_bw() + +#' theme(legend.title = element_blank()) + #' xlab("Time") + ylab("Level") -#' +#' #' # theta #' d_theta <- as.data.frame(fit, variable = "theta") -#' ggplot(d_theta, aes(x = value)) + -#' geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + -#' facet_wrap(~ variable, scales = "free") + +#' ggplot(d_theta, aes(x = value)) + +#' geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + +#' facet_wrap(~ variable, scales = "free") + #' theme_bw() -#' -#' +#' +#' #' # Bivariate Poisson model: -#' +#' #' set.seed(1) #' x <- cumsum(c(3, rnorm(19, sd = 0.5))) #' y <- cbind( -#' rpois(20, exp(x)), +#' rpois(20, exp(x)), #' rpois(20, exp(x))) -#' +#' #' prior_fn <- function(theta) { #' # half-normal prior using transformation #' dnorm(exp(theta), 0, 1, log = TRUE) + theta # plus jacobian term #' } -#' +#' #' update_fn <- function(theta) { #' list(R = array(exp(theta), c(1, 1, 1))) #' } -#' -#' model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, +#' +#' model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, #' R = 0.1, P1 = 1, distribution = "poisson", -#' init_theta = log(0.1), +#' init_theta = log(0.1), #' prior_fn = prior_fn, update_fn = update_fn) -#' +#' #' # Note small number of iterations for CRAN checks #' out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") -#' -#' sumr <- as.data.frame(out, variable = "states") %>% +#' +#' sumr <- as.data.frame(out, variable = "states") %>% #' group_by(time) %>% mutate(value = exp(value)) %>% -#' summarise(mean = mean(value), +#' summarise(mean = mean(value), #' ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) -#' ggplot(sumr, aes(time, mean)) + -#' geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + -#' geom_line() + -#' geom_line(data = data.frame(mean = y[, 1], time = 1:20), -#' colour = "tomato") + -#' geom_line(data = data.frame(mean = y[, 2], time = 1:20), +#' ggplot(sumr, aes(time, mean)) + +#' geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + +#' geom_line() + +#' geom_line(data = data.frame(mean = y[, 1], time = 1:20), +#' colour = "tomato") + +#' geom_line(data = data.frame(mean = y[, 2], time = 1:20), #' colour = "tomato") + #' theme_bw() -#' +#' run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", sampling_method = "psi", burnin = floor(iter / 2), - thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, + thin = 1, gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, local_approx = TRUE, threads = 1, - seed = sample(.Machine$integer.max, size = 1), max_iter = 100, + seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, verbose, ...) { - + check_missingness(model) - - if (!test_flag(end_adaptive_phase)) + + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -414,7 +414,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", } else { particles <- check_intmax(particles, "particles") } - + threads <- check_intmax(threads, "threads") model$max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) model$conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -422,79 +422,79 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", iter <- check_intmax(iter, "iter", max = 1e12) burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") - + if (missing(verbose)) { verbose <- is_interactive() } else { - if (!test_flag(verbose)) + if (!test_flag(verbose)) stop("Argument 'verbose' should be TRUE or FALSE. ") } if (iter < 50) verbose <- FALSE - + if (!test_flag(local_approx)) { stop("Argument 'local_approx' should be TRUE or FALSE. ") } else model$local_approx <- local_approx - if (length(model$theta) == 0) + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() check_prop(target_acceptance) check_prop(gamma, "gamma") - + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) - mcmc_type <- match.arg(tolower(mcmc_type), + mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3), "approx")) if (mcmc_type == "approx") particles <- 0 - if (particles < 2 && mcmc_type != "approx") + if (particles < 2 && mcmc_type != "approx") stop(paste("Number of state samples less than 2, use 'mcmc_type' 'approx'", "instead.", sep = " ")) - - sampling_method <- - pmatch(match.arg(tolower(sampling_method), c("psi", "bsf", "spdk")), + + sampling_method <- + pmatch(match.arg(tolower(sampling_method), c("psi", "bsf", "spdk")), c("psi", "bsf", "spdk")) - - dists <- + + dists <- c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian") - model$distribution <- + model$distribution <- pmatch(model$distribution, dists, duplicates.ok = TRUE) - 1 - + if(output_type == "full") { - nsamples <- - ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { - warning(paste("Number of state samples to be stored is approximately", + warning(paste("Number of state samples to be stored is approximately", nsamples, "you might run out of memory.")) } } - + if (inherits(model, "bsm_ng")) { - + names_ind <- c(!model$fixed & c(TRUE, model$slope, model$seasonal), model$noise) - + transformed <- c( - c("sd_level", "sd_slope", "sd_seasonal", "sd_noise")[names_ind], + c("sd_level", "sd_slope", "sd_seasonal", "sd_noise")[names_ind], if (dists[model$distribution + 1] %in% dists[4:5]) "phi") - + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) } else { if (inherits(model, "ar1_ng")) { - - transformed <- c("sigma", + + transformed <- c("sigma", if (dists[model$distribution + 1] %in% dists[4:5]) "phi") - + model$theta[transformed] <- log(pmax(0.001, model$theta[transformed])) } } - + if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - + switch(mcmc_type, "da" = { - out <- nongaussian_da_mcmc(model, + out <- nongaussian_da_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, seed, end_adaptive_phase, threads, sampling_method, model_type(model), verbose) @@ -502,7 +502,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", "pm" = { out <- nongaussian_pm_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, - seed, end_adaptive_phase, threads, + seed, end_adaptive_phase, threads, sampling_method, model_type(model), verbose) }, "is1" =, @@ -510,7 +510,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", "is3" = { out <- nongaussian_is_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, - seed, end_adaptive_phase, threads, + seed, end_adaptive_phase, threads, sampling_method, pmatch(mcmc_type, paste0("is", 1:3)), model_type(model), FALSE, verbose) @@ -518,7 +518,7 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", "approx" = { out <- nongaussian_is_mcmc(model, output_type, particles, iter, burnin, thin, gamma, target_acceptance, S, - seed, end_adaptive_phase, threads, + seed, end_adaptive_phase, threads, sampling_method, 2, model_type(model), TRUE, verbose) }) if (output_type == 1) { @@ -531,8 +531,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", frequency = frequency(model$y)) } } - - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) if (inherits(model, "bsm_ng")) { out$theta[, transformed] <- exp(out$theta[, transformed]) @@ -552,8 +552,8 @@ run_mcmc.nongaussian <- function(model, iter, particles, output_type = "full", out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- class(model)[1] - attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), + attr(out, "ts") <- + list(start = start(model$y), end = end(model$y), frequency = frequency(model$y)) out } @@ -566,14 +566,14 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", gamma = 2 / 3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, threads = 1, seed = sample(.Machine$integer.max, size = 1), max_iter = 100, conv_tol = 1e-8, iekf_iter = 0, verbose, ...) { - + check_missingness(model) - - if (!test_flag(end_adaptive_phase)) + + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -585,7 +585,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", } else { particles <- check_intmax(particles, "particles") } - + threads <- check_intmax(threads, "threads") max_iter <- check_intmax(max_iter, "max_iter", positive = FALSE) conv_tol <- check_positive_real(conv_tol, "conv_tol") @@ -594,47 +594,47 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) iekf_iter <- check_intmax(iekf_iter, "iekf_iter", positive = FALSE) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") - + if (missing(verbose)) { verbose <- is_interactive() } else { - if (!test_flag(verbose)) + if (!test_flag(verbose)) stop("Argument 'verbose' should be TRUE or FALSE. ") } if (iter < 50) verbose <- FALSE - - if (length(model$theta) == 0) + + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - + check_prop(target_acceptance) check_prop(gamma, "gamma") - + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) - mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3), + mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3), "ekf", "approx")) if (mcmc_type %in% c("ekf", "approx")) particles <- 0 - sampling_method <- pmatch(match.arg(tolower(sampling_method), + sampling_method <- pmatch(match.arg(tolower(sampling_method), c("psi", "bsf", "ekf")), c("psi", "bsf", NA, "ekf")) - + if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - + if (particles < 2 && !(mcmc_type %in% c("ekf", "approx"))) stop(paste("Number of state samples less than 2, use 'mcmc_type'", "'approx' or 'ekf' instead.", sep = " ")) - + if(output_type == "full") { - nsamples <- - ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * + nsamples <- + ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * model$n_states * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { - warning(paste("Number of state samples to be stored is approximately", + warning(paste("Number of state samples to be stored is approximately", nsamples, "you might run out of memory.")) } } - + out <- switch(mcmc_type, "da" = { nonlinear_da_mcmc(t(model$y), model$Z, model$H, model$T, @@ -668,7 +668,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, pmatch(mcmc_type, paste0("is", 1:3)), - sampling_method, max_iter, conv_tol, iekf_iter, + sampling_method, max_iter, conv_tol, iekf_iter, output_type, FALSE, verbose) }, "ekf" = { @@ -688,7 +688,7 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", model$n_states, model$n_etas, seed, particles, iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, threads, 2, - sampling_method, max_iter, conv_tol, + sampling_method, max_iter, conv_tol, iekf_iter, output_type, TRUE, verbose) } ) @@ -702,11 +702,11 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", frequency = frequency(model$y)) } } - - - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + + + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) - + out$iter <- iter out$burnin <- burnin out$thin <- thin @@ -718,38 +718,38 @@ run_mcmc.ssm_nlg <- function(model, iter, particles, output_type = "full", out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_nlg" - attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), + attr(out, "ts") <- + list(start = start(model$y), end = end(model$y), frequency = frequency(model$y)) out } #' @method run_mcmc ssm_sde #' @rdname run_mcmc #' @export -#' @references -#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based -#' on approximate marginal Markov chain Monte Carlo. +#' @references +#' Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based +#' on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492 run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", mcmc_type = "is2", L_c, L_f, burnin = floor(iter/2), thin = 1, gamma = 2/3, target_acceptance = 0.234, S, end_adaptive_phase = FALSE, - threads = 1, seed = sample(.Machine$integer.max, size = 1), verbose, + threads = 1, seed = sample(.Machine$integer.max, size = 1), verbose, ...) { - + check_missingness(model) - - if (any(c(model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, + + if (any(c(model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf) %in% c("<pointer: (nil)>", "<pointer: 0x0>"))) { - stop(paste("NULL pointer detected, please recompile the pointer file", + stop(paste("NULL pointer detected, please recompile the pointer file", "and reconstruct the model.", sep = " ")) } - - if (!test_flag(end_adaptive_phase)) + + if (!test_flag(end_adaptive_phase)) stop("Argument 'end_adaptive_phase' should be TRUE or FALSE. ") - + seed <- check_intmax(seed, "seed", FALSE, max = .Machine$integer.max) - + if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { @@ -764,29 +764,29 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", iter <- check_intmax(iter, "iter", max = 1e12) burnin <- check_intmax(burnin, "burnin", positive = FALSE, max = 1e12) if(burnin > iter) stop("Argument 'burnin' should be smaller than 'iter'.") - + if (missing(verbose)) { verbose <- is_interactive() } else { - if (!test_flag(verbose)) + if (!test_flag(verbose)) stop("Argument 'verbose' should be TRUE or FALSE. ") } if (iter < 50) verbose <- FALSE - - if (length(model$theta) == 0) + + if (length(model$theta) == 0) stop("No unknown parameters ('model$theta' has length of zero).") a <- proc.time() - + check_prop(target_acceptance) check_prop(gamma, "gamma") - + output_type <- pmatch(tolower(output_type), c("full", "summary", "theta")) mcmc_type <- match.arg(tolower(mcmc_type), c("pm", "da", paste0("is", 1:3))) - + if (missing(S)) { S <- diag(0.1 * pmax(0.1, abs(model$theta)), length(model$theta)) } - + if (mcmc_type != "pm") { if (L_f <= L_c) stop("L_f should be larger than L_c.") if (L_c < 1) stop("L_c should be at least 1") @@ -799,11 +799,11 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", if(output_type == "full") { nsamples <- length(model$y) * (iter - burnin) / thin * target_acceptance if (nsamples > 1e12) { - warning(paste("Number of state samples to be stored is approximately", + warning(paste("Number of state samples to be stored is approximately", nsamples, "you might run out of memory.")) } } - + out <- switch(mcmc_type, "da" = { out <- sde_da_mcmc(model$y, model$x0, model$positive, @@ -814,7 +814,7 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", end_adaptive_phase, output_type, verbose) }, "pm" = { - + out <- sde_pm_mcmc(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, @@ -822,23 +822,23 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", iter, burnin, thin, gamma, target_acceptance, S, end_adaptive_phase, output_type, verbose) }, - "is1" =, - "is2" =, + "is1" =, + "is2" =, "is3" = { out <- sde_is_mcmc(model$y, model$x0, model$positive, model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta, particles, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, - end_adaptive_phase, pmatch(mcmc_type, paste0("is", 1:3)), + end_adaptive_phase, pmatch(mcmc_type, paste0("is", 1:3)), threads, output_type, verbose) }) - + colnames(out$alpha) <- model$state_names - - colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- + + colnames(out$theta) <- rownames(out$S) <- colnames(out$S) <- names(model$theta) - + out$iter <- iter out$burnin <- burnin out$thin <- thin @@ -850,8 +850,8 @@ run_mcmc.ssm_sde <- function(model, iter, particles, output_type = "full", out$time <- proc.time() - a class(out) <- "mcmc_output" attr(out, "model_type") <- "ssm_sde" - attr(out, "ts") <- - list(start = start(model$y), end = end(model$y), + attr(out, "ts") <- + list(start = start(model$y), end = end(model$y), frequency = frequency(model$y)) out } diff --git a/README.Rmd b/README.Rmd index 3a85f19c..c0e91c41 100644 --- a/README.Rmd +++ b/README.Rmd @@ -85,7 +85,7 @@ supported. For details, see -* [The bssm paper on ArXiv](https://arxiv.org/abs/2101.08492) (to appear in R Journal), +* [The bssm paper on The R Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), * [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) * Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) @@ -95,15 +95,13 @@ There are also couple posters and a talk related to IS-correction methodology an * [SMC 2017 workshop: Accelerating MCMC with an approximation ](http://users.jyu.fi/~jovetale/posters/SMC2017) * [UseR!2017: Bayesian non-Gaussian state space models in R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) -The `bssm` package was originally developed with the support of Academy of -Finland grants 284513, 312605, and 311877. Current development is focused on -increased usability. For recent changes, see NEWS file. +The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, 311877, and 331817. Current development is focused on increased usability. For recent changes, see NEWS file. ### Citing the package If you use the `bssm` package in publications, please cite the corresponding R Journal paper: -Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R." _R Journal_. To appear. ArXiv preprint https://arxiv.org/abs/2101.08492. +Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R." The R Journal (2021) 13:2, pages 578-589. https://journal.r-project.org/archive/2021/RJ-2021-103/index.html ## Installation diff --git a/man/bssm.Rd b/man/bssm.Rd index db9d683d..c6589e09 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -30,7 +30,7 @@ package as well as some theory behind the novel IS-MCMC and Helske, Franks (2020), and the package vignettes. } \examples{ -model <- bsm_lg(Nile, +model <- bsm_lg(Nile, sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), a1 = 1000, P1 = 500^2) @@ -40,8 +40,8 @@ fit } \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. R Journal (to appear). -https://arxiv.org/abs/2101.08492 +Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +https://doi.org/10.32614/RJ-2021-103 Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. diff --git a/man/drownings.Rd b/man/drownings.Rd index fc33d1ae..b29c9c68 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -21,11 +21,11 @@ Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). \examples{ data("drownings") model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], - xreg = drownings[, "summer_temp"], distribution = "poisson", + xreg = drownings[, "summer_temp"], distribution = "poisson", beta = normal(0, 0, 1), sd_level = gamma_prior(0.1,2, 10), sd_slope = gamma_prior(0, 2, 10)) - -fit <- run_mcmc(model, iter = 5000, + +fit <- run_mcmc(model, iter = 5000, output_type = "summary", mcmc_type = "approx") fit ts.plot(model$y/model$u, exp(fit$alphahat[, 1]), col = 1:2) diff --git a/man/exchange.Rd b/man/exchange.Rd index 68ed0b93..97687b7f 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -8,7 +8,8 @@ A vector of length 945. } \source{ -\url{http://www.ssfpack.com/DKbook.html}. +The data used to be available on the www.ssfpack.com/DKbook.html but +this page is does not seem to be available anymore. } \description{ Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and @@ -20,7 +21,7 @@ model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) out <- particle_smoother(model, particles = 500) -plot.ts(cbind(model$y, exp(out$alphahat))) +plot.ts(cbind(model$y, exp(out$alphahat))) } \references{ James Durbin, Siem Jan Koopman (2012). diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd index e20412c0..b95a0928 100644 --- a/man/negbin_model.Rd +++ b/man/negbin_model.Rd @@ -16,13 +16,13 @@ of the model object in CRAN. # reproducing the model: data("negbin_series") # Construct model for bssm -bssm_model <- bsm_ng(negbin_series[, "y"], +bssm_model <- bsm_ng(negbin_series[, "y"], xreg = negbin_series[, "x"], beta = normal(0, 0, 10), phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), + sd_level = halfnormal(0.1, 1), sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), distribution = "negative binomial") \donttest{ @@ -32,8 +32,8 @@ fit_bssm } } \references{ -Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. R Journal (to appear). -https://arxiv.org/abs/2101.08492 +Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +https://doi.org/10.32614/RJ-2021-103 } \keyword{datasets} diff --git a/man/negbin_series.Rd b/man/negbin_series.Rd index 0a1fad79..c49f50ec 100644 --- a/man/negbin_series.Rd +++ b/man/negbin_series.Rd @@ -26,9 +26,9 @@ y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) } \references{ -Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. R Journal (to appear). -https://arxiv.org/abs/2101.08492 +Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +https://doi.org/10.32614/RJ-2021-103 } \seealso{ \code{negbin_model} diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 0d2ca9a9..2ff583c8 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -234,8 +234,8 @@ RNG "discontinuity" and because even without burnin bssm does include \code{burnin=0}). } \examples{ -model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), - sigma = halfnormal(1, 10), mu = normal(500, 500, 500), +model <- ar1_lg(LakeHuron, rho = uniform(0.5,-1,1), + sigma = halfnormal(1, 10), mu = normal(500, 500, 500), sd_y = halfnormal(1, 10)) mcmc_results <- run_mcmc(model, iter = 2e4) @@ -243,28 +243,28 @@ summary(mcmc_results, return_se = TRUE) sumr <- summary(mcmc_results, variable = "states") library("ggplot2") -ggplot(sumr, aes(time, Mean)) + - geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`), alpha = 0.25) + +ggplot(sumr, aes(time, Mean)) + + geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`), alpha = 0.25) + geom_line() + theme_bw() + geom_point(data = data.frame(Mean = LakeHuron, time = time(LakeHuron)), col = 2) - + # Continue from the previous run model$theta[] <- mcmc_results$theta[nrow(mcmc_results$theta), ] run_more <- run_mcmc(model, S = mcmc_results$S, iter = 1000, burnin = 0) set.seed(1) -n <- 50 +n <- 50 slope <- cumsum(c(0, rnorm(n - 1, sd = 0.001))) level <- cumsum(slope + c(0, rnorm(n - 1, sd = 0.2))) y <- rpois(n, exp(level)) -poisson_model <- bsm_ng(y, - sd_level = halfnormal(0.01, 1), - sd_slope = halfnormal(0.01, 0.1), +poisson_model <- bsm_ng(y, + sd_level = halfnormal(0.01, 1), + sd_slope = halfnormal(0.01, 0.1), P1 = diag(c(10, 0.1)), distribution = "poisson") - + # Note small number of iterations for CRAN checks -mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, +mcmc_out <- run_mcmc(poisson_model, iter = 1000, particles = 10, mcmc_type = "da") summary(mcmc_out, what = "theta", return_se = TRUE) @@ -282,9 +282,9 @@ y <- rnbinom(n, size = phi, mu = exp(beta * x + level)) model <- bsm_ng(y, xreg = x, beta = normal(0, 0, 10), phi = halfnormal(1, 10), - sd_level = halfnormal(0.1, 1), + sd_level = halfnormal(0.1, 1), sd_slope = halfnormal(0.01, 0.1), - a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), + a1 = c(0, 0), P1 = diag(c(10, 0.1)^2), distribution = "negative binomial") # run IS-MCMC @@ -292,45 +292,45 @@ model <- bsm_ng(y, xreg = x, fit <- run_mcmc(model, iter = 4000, particles = 10, mcmc_type = "is2", seed = 1) -# extract states +# extract states d_states <- as.data.frame(fit, variable = "states", time = 1:n) library("dplyr") library("ggplot2") # compute summary statistics -level_sumr <- d_states \%>\% +level_sumr <- d_states \%>\% filter(variable == "level") \%>\% group_by(time) \%>\% - summarise(mean = diagis::weighted_mean(value, weight), - lwr = diagis::weighted_quantile(value, weight, - 0.025), - upr = diagis::weighted_quantile(value, weight, + summarise(mean = diagis::weighted_mean(value, weight), + lwr = diagis::weighted_quantile(value, weight, + 0.025), + upr = diagis::weighted_quantile(value, weight, 0.975)) # visualize -level_sumr \%>\% ggplot(aes(x = time, y = mean)) + +level_sumr \%>\% ggplot(aes(x = time, y = mean)) + geom_line() + geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + - theme_bw() + - theme(legend.title = element_blank()) + + theme_bw() + + theme(legend.title = element_blank()) + xlab("Time") + ylab("Level") # theta d_theta <- as.data.frame(fit, variable = "theta") -ggplot(d_theta, aes(x = value)) + - geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + - facet_wrap(~ variable, scales = "free") + +ggplot(d_theta, aes(x = value)) + + geom_density(aes(weight = weight), adjust = 2, fill = "#92f0a8") + + facet_wrap(~ variable, scales = "free") + theme_bw() - - + + # Bivariate Poisson model: set.seed(1) x <- cumsum(c(3, rnorm(19, sd = 0.5))) y <- cbind( - rpois(20, exp(x)), + rpois(20, exp(x)), rpois(20, exp(x))) prior_fn <- function(theta) { @@ -342,24 +342,24 @@ update_fn <- function(theta) { list(R = array(exp(theta), c(1, 1, 1))) } -model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, +model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, R = 0.1, P1 = 1, distribution = "poisson", - init_theta = log(0.1), + init_theta = log(0.1), prior_fn = prior_fn, update_fn = update_fn) - + # Note small number of iterations for CRAN checks out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") -sumr <- as.data.frame(out, variable = "states") \%>\% +sumr <- as.data.frame(out, variable = "states") \%>\% group_by(time) \%>\% mutate(value = exp(value)) \%>\% - summarise(mean = mean(value), + summarise(mean = mean(value), ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) -ggplot(sumr, aes(time, mean)) + -geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + -geom_line() + -geom_line(data = data.frame(mean = y[, 1], time = 1:20), - colour = "tomato") + -geom_line(data = data.frame(mean = y[, 2], time = 1:20), +ggplot(sumr, aes(time, mean)) + +geom_ribbon(aes(ymin = ymin, ymax = ymax),alpha = 0.25) + +geom_line() + +geom_line(data = data.frame(mean = y[, 1], time = 1:20), + colour = "tomato") + +geom_line(data = data.frame(mean = y[, 2], time = 1:20), colour = "tomato") + theme_bw() @@ -373,9 +373,9 @@ Vihola, M, Helske, J, Franks, J (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 -Helske, J, Vihola, M (2021). bssm: Bayesian Inference of Non-linear and -Non-Gaussian State Space Models in R. R Journal (to appear). -https://arxiv.org/abs/2101.08492 +Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and +Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. +https://doi.org/10.32614/RJ-2021-103 Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index 91141a20..e4160dee 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -27,7 +27,7 @@ knitr::opts_chunk$set(echo = TRUE) This is a short vignette illustrating the `bssm` package. For more detailed exposition, please see the corresponding R Journal paper: -Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R". Accepted to R Journal. [ArXiv preprint](https://arxiv.org/abs/2101.08492). +Jouni Helske and Matti Vihola (2021). "bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R". The R Journal (2021) 13:2, pages 578-589. [Link to the paper](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html). # Introduction From 745373d02f0673d6556e669ce9494e1369787966 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Fri, 6 May 2022 15:00:55 +0300 Subject: [PATCH 162/180] add ropensci badge, switch to markdown NEWS --- DESCRIPTION | 2 +- NEWS => NEWS.md | 5 ++++ README.Rmd | 1 + README.md | 77 +++++++++++++++++++++++++++++++------------------ codemeta.json | 11 +++++-- 5 files changed, 64 insertions(+), 32 deletions(-) rename NEWS => NEWS.md (98%) diff --git a/DESCRIPTION b/DESCRIPTION index a252e827..f6410bfc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.1 +Version: 2.0.1.1 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NEWS b/NEWS.md similarity index 98% rename from NEWS rename to NEWS.md index c5624d13..4218cc2f 100644 --- a/NEWS +++ b/NEWS.md @@ -1,3 +1,8 @@ +bssm 2.0.x (2022-) +===================================== +* Switched to markdown NEWS with a plan to be more clear about the future + changes in the package. + bssm 2.0.1 (Release date: 2022-05-02) ============== * Fixed weights to one in case of non-linear model with mcmc_type="approx". diff --git a/README.Rmd b/README.Rmd index c0e91c41..ec17b4ab 100644 --- a/README.Rmd +++ b/README.Rmd @@ -68,6 +68,7 @@ knitr::opts_chunk$set( <!-- badges: start --> [![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/489_status.svg)](https://github.com/ropensci/software-review/issues/489) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) [![CRAN version](http://www.r-pkg.org/badges/version/bssm)]( https://CRAN.R-project.org/package=bssm) diff --git a/README.md b/README.md index ca713138..dfecfa0d 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,8 @@ [![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) +[![Status at rOpenSci Software Peer +Review](https://badges.ropensci.org/489_status.svg)](https://github.com/ropensci/software-review/issues/489) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) @@ -26,7 +28,8 @@ models and discretely observed latent diffusion processes are supported. For details, see -- [The bssm paper on R Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), +- [The bssm paper on The R + Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), - [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) - Paper on [Importance sampling type estimators based on approximate marginal Markov chain Monte @@ -43,8 +46,9 @@ methodology and bssm package: R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) The `bssm` package was originally developed with the support of Academy -of Finland grants 284513, 312605, 311877, and 331817. Current development is -focused on increased usability. For recent changes, see NEWS file. +of Finland grants 284513, 312605, 311877, and 331817. Current +development is focused on increased usability. For recent changes, see +NEWS file. ### Citing the package @@ -52,7 +56,9 @@ If you use the `bssm` package in publications, please cite the corresponding R Journal paper: Jouni Helske and Matti Vihola (2021). “bssm: Bayesian Inference of -Non-linear and Non-Gaussian State Space Models in R.” *R Journal*. https://journal.r-project.org/archive/2021/RJ-2021-103/index.html>. +Non-linear and Non-Gaussian State Space Models in R.” The R Journal +(2021) 13:2, pages 578-589. +<https://journal.r-project.org/archive/2021/RJ-2021-103/index.html> ## Installation @@ -88,12 +94,14 @@ allowed); ``` r library("bssm") +#> Warning: package 'bssm' was built under R version 4.1.3 #> #> Attaching package: 'bssm' #> The following object is masked from 'package:base': #> #> gamma library("dplyr") +#> Warning: package 'dplyr' was built under R version 4.1.3 #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': @@ -103,6 +111,7 @@ library("dplyr") #> #> intersect, setdiff, setequal, union library("ggplot2") +#> Warning: package 'ggplot2' was built under R version 4.1.3 set.seed(1) data("airquality", package = "datasets") @@ -148,7 +157,7 @@ fit #> #> Run time: #> user system elapsed -#> 1.14 0.00 1.12 +#> 1.00 0.03 1.03 obs <- data.frame(Time = 1:nrow(airquality), Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) @@ -216,7 +225,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 15.02 0.20 14.95 +#> 11.20 0.11 11.25 ``` Comparison: @@ -241,26 +250,38 @@ as predictor for ozone. As it contains few missing values, we cannot use it directly. As the number of missing time points is very small, simple imputation would likely be acceptable, but let’s consider more another approach. For simplicity, the slope terms of the previous models are now -omitted, and we focus on the Gaussian case. Let *μ*<sub>*t*</sub> be the -true solar radiation at time *t*. Now for ozone *O*<sub>*t*</sub> we -assume following model: - -*O*<sub>*t*</sub> = *D*<sub>*t*</sub> + *α*<sub>*t*</sub> + *β*<sub>*S*</sub>*μ*<sub>*t*</sub> + *σ*<sub>*ϵ*</sub>*ϵ*<sub>*t*</sub> -*α*<sub>*t* + 1</sub> = *α*<sub>*t*</sub> + *σ*<sub>*η*</sub>*η*<sub>*t*</sub> -*α*<sub>1</sub> ∼ *N*(0, 100<sup>2</sup>I), -wheere *D*<sub>*t*</sub> = *β**X*<sub>*t*</sub> contains regression -terms related to wind and temperature, *α*<sub>*t*</sub> is the time -varying intercept term, and *β*<sub>*S*</sub> is the effect of solar -radiation *μ*<sub>*t*</sub>. - -Now for the observed solar radiation *S*<sub>*t*</sub> we assume - -*S*<sub>*t*</sub> = *μ*<sub>*t*</sub> -*μ*<sub>*t* + 1</sub> = *μ*<sub>*t*</sub> + *σ*<sub>*ξ*</sub>*ξ*<sub>*t*</sub>, -*μ*<sub>1</sub> ∼ *N*(0, 100<sup>2</sup>), -i.e. we assume as simple random walk for the *μ* which we observe -without error or not at all (there is no error term in the observation -equation *S*<sub>*t*</sub> = *μ*<sub>*t*</sub>). +omitted, and we focus on the Gaussian case. Let +![\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_t "\mu_t") +be the true solar radiation at time +![t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;t "t"). +Now for ozone +![O_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;O_t "O_t") +we assume following model: + +![O_t = D_t + \\alpha_t + \\beta_S \\mu_t + \\sigma\_\\epsilon \\epsilon_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;O_t%20%3D%20D_t%20%2B%20%5Calpha_t%20%2B%20%5Cbeta_S%20%5Cmu_t%20%2B%20%5Csigma_%5Cepsilon%20%5Cepsilon_t "O_t = D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t") +![\\alpha\_{t+1} = \\alpha_t + \\sigma\_\\eta\\eta_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_%7Bt%2B1%7D%20%3D%20%5Calpha_t%20%2B%20%5Csigma_%5Ceta%5Ceta_t "\alpha_{t+1} = \alpha_t + \sigma_\eta\eta_t") +![\\alpha_1 \\sim N(0, 100^2\\textrm{I})](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_1%20%5Csim%20N%280%2C%20100%5E2%5Ctextrm%7BI%7D%29 "\alpha_1 \sim N(0, 100^2\textrm{I})"), +wheere +![D_t = \\beta X_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;D_t%20%3D%20%5Cbeta%20X_t "D_t = \beta X_t") +contains regression terms related to wind and temperature, +![\\alpha_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_t "\alpha_t") +is the time varying intercept term, and +![\\beta_S](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cbeta_S "\beta_S") +is the effect of solar radiation +![\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_t "\mu_t"). + +Now for the observed solar radiation +![S_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t "S_t") +we assume + +![S_t = \\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t%20%3D%20%5Cmu_t "S_t = \mu_t") +![\\mu\_{t+1} = \\mu_t + \\sigma\_\\xi\\xi_t,](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_%7Bt%2B1%7D%20%3D%20%5Cmu_t%20%2B%20%5Csigma_%5Cxi%5Cxi_t%2C "\mu_{t+1} = \mu_t + \sigma_\xi\xi_t,") +![\\mu_1 \\sim N(0, 100^2)](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_1%20%5Csim%20N%280%2C%20100%5E2%29 "\mu_1 \sim N(0, 100^2)"), +i.e. we assume as simple random walk for the +![\\mu](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu "\mu") +which we observe without error or not at all (there is no error term in +the observation equation +![S_t=\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t%3D%5Cmu_t "S_t=\mu_t")). We combine these two models as a bivariate Gaussian model with `ssm_mlg`: @@ -322,11 +343,11 @@ fit #> #> variable time Mean SE SD 2.5% 97.5% ESS #> alpha 154 -16.44435 0.3659912 14.99708 -46.321645 13.01863 1679 -#> mu 154 223.60490 1.3409568 116.49063 -6.206302 453.18554 7546 +#> mu 154 223.60490 1.3409568 116.49063 -6.206301 453.18554 7546 #> #> Run time: #> user system elapsed -#> 15.70 0.18 15.75 +#> 12.26 0.12 12.30 ``` Draw predictions: diff --git a/codemeta.json b/codemeta.json index 3afede09..3209680f 100644 --- a/codemeta.json +++ b/codemeta.json @@ -7,7 +7,7 @@ "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "2.0.1", + "version": "2.0.1.1", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", @@ -289,7 +289,7 @@ }, "SystemRequirements": "C++11, pandoc (>= 1.12.3, needed for vignettes)" }, - "fileSize": "2345.1KB", + "fileSize": "11958.877KB", "citation": [ { "@type": "ScholarlyArticle", @@ -358,10 +358,15 @@ } } ], - "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS", + "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS.md", "readme": "https://github.com/helske/bssm/blob/master/README.md", "contIntegration": ["https://github.com/helske/bssm/actions", "https://app.codecov.io/gh/helske/bssm?branch=master"], "developmentStatus": "https://www.repostatus.org/#active", + "review": { + "@type": "Review", + "url": "https://github.com/ropensci/software-review/issues/489", + "provider": "https://ropensci.org" + }, "keywords": ["bayesian-inference", "markov-chain-monte-carlo", "particle-filter", "time-series", "state-space", "r", "cpp"], "relatedLink": "https://CRAN.R-project.org/package=bssm" } From a0ae478b3a848c1d7ef92b89147e346b3a8e3e21 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 09:49:50 +0300 Subject: [PATCH 163/180] point to the R journal paper and the vignette in the ?bssm --- DESCRIPTION | 4 ++-- NEWS.md | 6 +++--- R/bssm-package.R | 10 ++++++++-- man/bssm.Rd | 8 ++++++-- 4 files changed, 19 insertions(+), 9 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f6410bfc..85223452 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.1.1 +Version: 2.0.1.2 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -54,6 +54,6 @@ URL: https://github.com/helske/bssm ByteCompile: true Encoding: UTF-8 NeedsCompilation: yes -RoxygenNote: 7.1.2 +RoxygenNote: 7.2.0 Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd", "srr::srr_stats_roclet")) diff --git a/NEWS.md b/NEWS.md index 4218cc2f..582864f2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,7 @@ -bssm 2.0.x (2022-) +bssm 2.0.1.2 (2022-) ===================================== -* Switched to markdown NEWS with a plan to be more clear about the future - changes in the package. + * Switched to markdown NEWS with a plan to be more clear about the future + changes in the package. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/bssm-package.R b/R/bssm-package.R index c7285764..0e315c4b 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -2,9 +2,13 @@ #' Bayesian Inference of State Space Models #' #' This package contains functions for efficient Bayesian inference of state -#' space models (SSMs), where model is assumed to be either +#' space models (SSMs). For details, see the package vignette and the R Journal +#' paper. #' -#' * Exponential family state space models, where the state equation is linear +#' @details +#' The model is assumed to be either +#' +#' * Exponential family state space model, where the state equation is linear #' Gaussian, and the conditional observation density is either Gaussian, #' Poisson, binomial, negative binomial or Gamma density. #' @@ -42,6 +46,8 @@ #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples +#' # Fit a local level model (latent random walk + observational level noise) +#' # to the Nile dataset using the bsm_lg function: #' model <- bsm_lg(Nile, #' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), #' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), diff --git a/man/bssm.Rd b/man/bssm.Rd index c6589e09..e162cb52 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -6,11 +6,13 @@ \title{Bayesian Inference of State Space Models} \description{ This package contains functions for efficient Bayesian inference of state -space models (SSMs), where model is assumed to be either +space models (SSMs). For details, see the package vignette and the R Journal +paper. } \details{ +The model is assumed to be either \itemize{ -\item Exponential family state space models, where the state equation is linear +\item Exponential family state space model, where the state equation is linear Gaussian, and the conditional observation density is either Gaussian, Poisson, binomial, negative binomial or Gamma density. \item Basic stochastic volatility model. @@ -30,6 +32,8 @@ package as well as some theory behind the novel IS-MCMC and Helske, Franks (2020), and the package vignettes. } \examples{ +# Fit a local level model (latent random walk + observational level noise) +# to the Nile dataset using the bsm_lg function: model <- bsm_lg(Nile, sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), From 8afa5f65585ed644917bab910021f5739189bf84 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 10:21:48 +0300 Subject: [PATCH 164/180] added documentation to ?bssm --- NEWS.md | 1 + R/bssm-package.R | 60 +++++++++++++++++++++++++++++++++++++++++++--- man/bssm.Rd | 62 +++++++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 117 insertions(+), 6 deletions(-) diff --git a/NEWS.md b/NEWS.md index 582864f2..fe3adbc4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,6 +2,7 @@ bssm 2.0.1.2 (2022-) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future changes in the package. + * Added more details to `?bssm` help page. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/bssm-package.R b/R/bssm-package.R index 0e315c4b..e24b93c4 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -22,13 +22,41 @@ #' automatically predicted, but there can be no missing values in the system #' matrices of the model. #' -#' The \code{bssm} package includes several MCMC sampling and sequential Monte +#' The package contains multiple functions for building the model: +#' +#' * `bsm_lg` for basic univariate structural time series model (BSM), +#' `ar1` for univariate noisy AR(1) process, and `ssm_ulg` and `ssm_mlg` for +#' arbitrary linear gaussian model with univariate/multivariate +#' observations. +#' * The non-Gaussian versions (where observations are non-Gaussian) of the +#' above models can be constructed using the functions `bsm_ng`, `ar1_ng`, +#' `ssm_ung` and `ssm_mng`. +#' * An univariate stochastic volatility model can be defined using a function +#' `svm`. +#' * For non-linear models, user must define the model using C++ snippets and +#' the the function `ssm_nlg`. See details in the `growth_model` vignette. +#' * Diffusion models can be defined with the function `ssm_sde`, again using +#' the C++ snippets. See `sde_model` vignette for details. +#' +#' See the corresponding functions for some examples and details. +#' +#' After building the model, the model can be estimated via `run_mcmc` +#' function. The documentation of this function gives some examples. The +#' \code{bssm} package includes several MCMC sampling and sequential Monte #' Carlo methods for models outside classic linear-Gaussian framework. For #' definitions of the currently supported models and methods, usage of the #' package as well as some theory behind the novel IS-MCMC and #' \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, #' Helske, Franks (2020), and the package vignettes. #' +#' The output of the `run_mcmc` can be analysed by extracting the posterior +#' samples of the latent states and hyperparameters using `as.data.frame`, +#' `as_draws`, `expand_sample`, and `summary` methods, as well as `fitted` and +#' `predict` methods. Functionality of the `ggplot2`, `bayesplot`, can be used +#' to visualize the posterior draws or their summary statistics, and further +#' diagnostics checks can be performed with the help of the `posterior` for +#' example. +#' #' @references #' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and #' Non-Gaussian State Space Models in R. The R Journal (2021) 13:2, 578-589. @@ -38,6 +66,15 @@ #' based on approximate marginal Markov chain Monte Carlo. #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' +#' H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag +#' New York, 2016. +#' +#' Gabry J, Mahr T (2022). “bayesplot: Plotting for Bayesian Models.” R package +#' version 1.9.0, https://mc-stan.org/bayesplot. +#' +#' Bürkner P, Gabry J, Kay M, Vehtari A (2022). “posterior: Tools for Working +#' with Posterior Distributions.” R package version 1.2.1, +#' https://mc-stan.org/posterior. #' @docType package #' @name bssm #' @aliases bssm @@ -46,15 +83,32 @@ #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples -#' # Fit a local level model (latent random walk + observational level noise) -#' # to the Nile dataset using the bsm_lg function: +#' # Create a local level model (latent random walk + noise) to the Nile +#' # dataset using the bsm_lg function: #' model <- bsm_lg(Nile, #' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), #' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), #' a1 = 1000, P1 = 500^2) #' +#' # the priors for the unknown paramters sd_y and sd_level were defined +#' # as trunctated normal distributions, see ?bssm_prior for details +#' +#' # Run the MCMC for 2000 iterations (notice the small number of iterations to +#' # comply with the CRAN's check requirements) #' fit <- run_mcmc(model, iter = 2000) +#' +#' # print some summary information: #' fit +#' +#' # extract the summary statistics for state variable +#' sumr <- summary(fit,variable = "states") +#' +#' # visualize +#' library("ggplot2") +#' ggplot(sumr, aes(time, Mean)) + +#' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`),alpha = 0.25) + +#' geom_line() + +#' theme_bw() NULL #' Deaths by drowning in Finland in 1969-2019 #' diff --git a/man/bssm.Rd b/man/bssm.Rd index e162cb52..7c1923e4 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -24,23 +24,69 @@ Missing values in response series are allowed as per SSM theory and can be automatically predicted, but there can be no missing values in the system matrices of the model. -The \code{bssm} package includes several MCMC sampling and sequential Monte +The package contains multiple functions for building the model: +\itemize{ +\item \code{bsm_lg} for basic univariate structural time series model (BSM), +\code{ar1} for univariate noisy AR(1) process, and \code{ssm_ulg} and \code{ssm_mlg} for +arbitrary linear gaussian model with univariate/multivariate +observations. +\item The non-Gaussian versions (where observations are non-Gaussian) of the +above models can be constructed using the functions \code{bsm_ng}, \code{ar1_ng}, +\code{ssm_ung} and \code{ssm_mng}. +\item An univariate stochastic volatility model can be defined using a function +\code{svm}. +\item For non-linear models, user must define the model using C++ snippets and +the the function \code{ssm_nlg}. See details in the \code{growth_model} vignette. +\item Diffusion models can be defined with the function \code{ssm_sde}, again using +the C++ snippets. See \code{sde_model} vignette for details. +} + +See the corresponding functions for some examples and details. + +After building the model, the model can be estimated via \code{run_mcmc} +function. The documentation of this function gives some examples. The +\code{bssm} package includes several MCMC sampling and sequential Monte Carlo methods for models outside classic linear-Gaussian framework. For definitions of the currently supported models and methods, usage of the package as well as some theory behind the novel IS-MCMC and \eqn{\psi}{psi}-APF algorithms, see Helske and Vihola (2021), Vihola, Helske, Franks (2020), and the package vignettes. + +The output of the \code{run_mcmc} can be analysed by extracting the posterior +samples of the latent states and hyperparameters using \code{as.data.frame}, +\code{as_draws}, \code{expand_sample}, and \code{summary} methods, as well as \code{fitted} and +\code{predict} methods. Functionality of the \code{ggplot2}, \code{bayesplot}, can be used +to visualize the posterior draws or their summary statistics, and further +diagnostics checks can be performed with the help of the \code{posterior} for +example. } \examples{ -# Fit a local level model (latent random walk + observational level noise) -# to the Nile dataset using the bsm_lg function: +# Create a local level model (latent random walk + noise) to the Nile +# dataset using the bsm_lg function: model <- bsm_lg(Nile, sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), a1 = 1000, P1 = 500^2) +# the priors for the unknown paramters sd_y and sd_level were defined +# as trunctated normal distributions, see ?bssm_prior for details + +# Run the MCMC for 2000 iterations (notice the small number of iterations to +# comply with the CRAN's check requirements) fit <- run_mcmc(model, iter = 2000) + +# print some summary information: fit + +# extract the summary statistics for state variable +sumr <- summary(fit,variable = "states") + +# visualize +library("ggplot2") +ggplot(sumr, aes(time, Mean)) + + geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`),alpha = 0.25) + + geom_line() + + theme_bw() } \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and @@ -50,4 +96,14 @@ https://doi.org/10.32614/RJ-2021-103 Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 + +H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag +New York, 2016. + +Gabry J, Mahr T (2022). “bayesplot: Plotting for Bayesian Models.” R package +version 1.9.0, https://mc-stan.org/bayesplot. + +Bürkner P, Gabry J, Kay M, Vehtari A (2022). “posterior: Tools for Working +with Posterior Distributions.” R package version 1.2.1, +https://mc-stan.org/posterior. } From 6fcff5e12d1b6aaa6e915cfe86058a26d66b71d5 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 11:08:28 +0300 Subject: [PATCH 165/180] added more details on priors --- NEWS.md | 3 +- R/priors.R | 154 +++++++++++++++++++++++++++------------------- man/bssm_prior.Rd | 43 ++++++++++--- 3 files changed, 125 insertions(+), 75 deletions(-) diff --git a/NEWS.md b/NEWS.md index fe3adbc4..88ddcf7d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,7 +2,8 @@ bssm 2.0.1.2 (2022-) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future changes in the package. - * Added more details to `?bssm` help page. + * Added more details to the `?bssm` help page. + * Added more details to the `?bssm_prior` help page. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/priors.R b/R/priors.R index 75a618cf..56b0c87d 100644 --- a/R/priors.R +++ b/R/priors.R @@ -1,38 +1,62 @@ #' Prior objects for bssm models #' -#' These simple objects of class \code{bssm_prior} are used to construct a -#' prior distributions for the some of the model objects of \code{bssm} -#' package. Currently supported priors are uniform -#' (\code{uniform()}), half-normal (\code{halfnormal()}), -#' normal (\code{normal()}), gamma (\code{gamma}), and -#' truncated normal distribution (\code{tnormal()}). All parameters are -#' vectorized so for regression coefficient vector beta you can define prior -#' for example as \code{normal(0, 0, c(10, 20))}. -#' -#' The longer name versions of the prior functions with \code{_prior} ending -#' are identical with shorter versions and they are available only to -#' avoid clash with R's primitive function \code{gamma} (other long prior names +#' These simple objects of class \code{bssm_prior} are used to construct a +#' prior distributions for the hyperparameters theta for some of the model +#' objects of \code{bssm} package. Note that these priors do not include the +#' constant terms as they do not affect the sampling. +#' +#' Currently supported priors are +#' +#' * uniform prior (\code{uniform()}) with a probability density function (pdf) +#' defined as \eqn{\frac{1}{max - min}} for \eqn{min < theta < max}. +#' * normal (\code{normal()}), a normal distribution parameterized via mean and +#' standard deviation, i.e. N(mean, sd^2). +#' * truncated normal distribution (\code{tnormal()}), a normal distribution +#' with known truncation points (from below and/or above). Ignoring the +#' scaling factors, this corresponds to the pdf of N(mean, sd^2) when +#' \eqn{min < theta < max} and zero otherwise. +#' * half-normal (\code{halfnormal()}) with a pdf matching the pdf of the +#' truncated normal distribution with min=0 and max=inf. +#' * gamma (\code{gamma}), a gamma distribution with shape and rate +#' parameterization. +#' +#' All parameters are vectorized so for regression coefficient vector beta you +#' can define prior for example as \code{normal(0, 0, c(10, 20))}. +#' +#' For the general exponential models, i.e. models built with the `ssm_ulg`, +#' `ssm_ung`, `ssm_mlg`, and `ssm_mng`, you can define arbitrary priors by +#' defining the `prior_fn` function, which takes the one argument, `theta`, +#' corresponding to the hyperparameter vector of the model, +#' and returns a log-density of the (joint) prior (see the R Journal paper and +#' e.g. `ssm_ulg` for examples). Similarly, the priors for the non-linear +#' models (`ssm_nlg`) and SDE models (`ssm_sde`) are constructed +#' via C++ snippets (see the vignettes for details). +#' +#' The longer name versions of the prior functions with \code{_prior} ending +#' are identical with shorter versions and they are available only to +#' avoid clash with R's primitive function \code{gamma} (other long prior names #' are just for consistent naming). -#' +#' #' @rdname bssm_prior #' @aliases bssm_prior bssm_prior_list -#' @param init Initial value for the parameter, used in initializing the model -#' components and as a starting values in MCMC. +#' @param init Initial value for the parameter, used in initializing the model +#' components and as a starting values in MCMC. #' @param min Lower bound of the uniform and truncated normal prior. #' @param max Upper bound of the uniform and truncated normal prior. -#' @param sd Positive value defining the standard deviation of the +#' @param sd Positive value defining the standard deviation of the #' (underlying i.e. non-truncated) Normal distribution. #' @param mean Mean of the Normal prior. #' @param shape Positive shape parameter of the Gamma prior. #' @param rate Positive rate parameter of the Gamma prior. -#' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case +#' @return object of class \code{bssm_prior} or \code{bssm_prior_list} in case #' of multiple priors (i.e. multiple regression coefficients). #' @export -#' @srrstats {BS1.2c, BS2.2, BS2.3, BS2.4, BS2.6, BS2.7} Explains prior +#' @srrstats {BS1.2c, BS2.2, BS2.3, BS2.4, BS2.6, BS2.7} Explains prior #' definitions and initial values. -#' @srrstats {BS2.5} Checks are in place for the distributional parameters of +#' @srrstats {BS2.5} Checks are in place for the distributional parameters of #' priors and their initial values. #' @examples +#' #' # create uniform prior on [-1, 1] for one parameter with initial value 0.2: #' uniform(init = 0.2, min = -1.0, max = 1.0) #' # two normal priors at once i.e. for coefficients beta: @@ -45,24 +69,24 @@ #' halfnormal(init = 0.01, sd = 0.1) #' # Truncated normal #' tnormal(init = 5.2, mean = 5.0, sd = 3.0, min = 0.5, max = 9.5) -#' -#' +#' +#' #' # Further examples for diagnostic purposes: #' uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) #' tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) #' halfnormal(c(0, 0.2), c(1.0, 1.2)) -#' # not run because autotest bug +#' # not run because autotest bug #' # gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) -#' +#' #' # longer versions: #' uniform_prior(init = c(0, 0.2), min = c(-1.0, 0.001), max = c(1.0, 1.2)) #' normal_prior(init = c(0, 0.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2)) -#' tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), +#' tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), #' min = c(1.2, 2), max = 3.3) #' halfnormal_prior(init = c(0, 0.2), sd = c(1.0, 1.2)) #' gamma_prior(init = c(0.1, 0.2), shape = c(1.2, 2), rate = c(3.3, 3.3)) -#' +#' uniform_prior <- function(init, min, max) { if (any(!is.numeric(init), !is.numeric(min), !is.numeric(max))) { stop("Parameters for priors must be numeric.") @@ -76,15 +100,15 @@ uniform_prior <- function(init, min, max) { "in the support of the prior.", sep = " ")) } n <- max(length(init), length(min), length(max)) - + if (n > 1) { - structure(lapply(1:n, function(i) + structure(lapply(1:n, function(i) structure(list(prior_distribution = "uniform", init = safe_pick(init, i), - min = safe_pick(min, i), max = safe_pick(max, i)), - class = "bssm_prior_list")), + min = safe_pick(min, i), max = safe_pick(max, i)), + class = "bssm_prior_list")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "uniform", init = init, + structure(list(prior_distribution = "uniform", init = init, min = min, max = max), class = "bssm_prior") } } @@ -95,7 +119,7 @@ uniform <- uniform_prior #' @rdname bssm_prior #' @export halfnormal_prior <- function(init, sd) { - + if (any(!is.numeric(init), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } @@ -108,14 +132,14 @@ halfnormal_prior <- function(init, sd) { "non-negative.", sep = " ")) } n <- max(length(init), length(sd)) - + if (n > 1) { - structure(lapply(1:n, function(i) - structure(list(prior_distribution = "halfnormal", + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "halfnormal", init = safe_pick(init, i), sd = safe_pick(sd, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "halfnormal", init = init, sd = sd), + structure(list(prior_distribution = "halfnormal", init = init, sd = sd), class = "bssm_prior") } } @@ -126,7 +150,7 @@ halfnormal <- halfnormal_prior #' @rdname bssm_prior #' @export normal_prior <- function(init, mean, sd) { - + if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } @@ -134,17 +158,17 @@ normal_prior <- function(init, mean, sd) { stop(paste("Standard deviation parameter for Normal distribution must", "be positive.", sep = " ")) } - + n <- max(length(init), length(mean), length(sd)) if (n > 1) { - structure(lapply(1:n, function(i) - structure(list(prior_distribution = "normal", - init = safe_pick(init, i), mean = safe_pick(mean, i), - sd = safe_pick(sd, i)), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "normal", + init = safe_pick(init, i), mean = safe_pick(mean, i), + sd = safe_pick(sd, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { - structure(list(prior_distribution = "normal", init = init, mean = mean, + structure(list(prior_distribution = "normal", init = init, mean = mean, sd = sd), class = "bssm_prior") } } @@ -156,27 +180,27 @@ normal <- normal_prior #' @rdname bssm_prior #' @export tnormal_prior <- function(init, mean, sd, min = -Inf, max = Inf) { - + if (any(!is.numeric(init), !is.numeric(mean), !is.numeric(sd))) { stop("Parameters for priors must be numeric.") } if (any(init < min) | any(init > max)) { - stop(paste("Initial value for parameter with truncated Normal is not", + stop(paste("Initial value for parameter with truncated Normal is not", "between the lower and upper bounds.", sep = " ")) } - + if (any(sd < 0)) { stop(paste("Standard deviation parameter for truncated Normal distribution", "must be positive.", sep = " ")) } - + n <- max(length(init), length(mean), length(sd)) if (n > 1) { - structure(lapply(1:n, function(i) - structure(list(prior_distribution = "tnormal", - init = safe_pick(init, i), mean = safe_pick(mean, i), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "tnormal", + init = safe_pick(init, i), mean = safe_pick(mean, i), sd = safe_pick(sd, i), - min = safe_pick(min, i), max = safe_pick(max, i)), + min = safe_pick(min, i), max = safe_pick(max, i)), class = "bssm_prior")), class = "bssm_prior_list") } else { structure(list(prior_distribution = "tnormal", init = init, mean = mean, @@ -191,7 +215,7 @@ tnormal <- tnormal_prior #' @rdname bssm_prior #' @export gamma_prior <- function(init, shape, rate) { - + if (any(!is.numeric(init), !is.numeric(shape), !is.numeric(rate))) { stop("Parameters for priors must be numeric.") } @@ -203,15 +227,15 @@ gamma_prior <- function(init, shape, rate) { } n <- max(length(init), length(shape), length(rate)) if (n > 1) { - structure(lapply(1:n, function(i) - structure(list(prior_distribution = "gamma", - init = safe_pick(init, i), shape = safe_pick(shape, i), - rate = safe_pick(rate, i)), + structure(lapply(1:n, function(i) + structure(list(prior_distribution = "gamma", + init = safe_pick(init, i), shape = safe_pick(shape, i), + rate = safe_pick(rate, i)), class = "bssm_prior")), class = "bssm_prior_list") - + } else { - structure(list(prior_distribution = "gamma", init = init, - shape = shape, rate = rate), + structure(list(prior_distribution = "gamma", init = init, + shape = shape, rate = rate), class = "bssm_prior") } } @@ -220,19 +244,19 @@ gamma_prior <- function(init, shape, rate) { gamma <- gamma_prior combine_priors <- function(x) { - - if (length(x) == 0) + + if (length(x) == 0) return(list(prior_distributions = 0, parameters = matrix(0, 0, 0))) - - prior_distributions <- vapply(x, "[[", "prior_distribution", + + prior_distributions <- vapply(x, "[[", "prior_distribution", FUN.VALUE = character(1)) parameters <- matrix(NA, 4, length(prior_distributions)) for (i in seq_along(prior_distributions)) { parameters[1:(length(x[[i]]) - 2), i] <- as.numeric(x[[i]][-(1:2)]) } - list(prior_distributions = - pmatch(prior_distributions, c("uniform", "halfnormal", "normal", - "tnormal", "gamma"), duplicates.ok = TRUE) - 1, + list(prior_distributions = + pmatch(prior_distributions, c("uniform", "halfnormal", "normal", + "tnormal", "gamma"), duplicates.ok = TRUE) - 1, parameters = parameters) } diff --git a/man/bssm_prior.Rd b/man/bssm_prior.Rd index c64b20ce..946dc465 100644 --- a/man/bssm_prior.Rd +++ b/man/bssm_prior.Rd @@ -58,21 +58,46 @@ of multiple priors (i.e. multiple regression coefficients). } \description{ These simple objects of class \code{bssm_prior} are used to construct a -prior distributions for the some of the model objects of \code{bssm} -package. Currently supported priors are uniform -(\code{uniform()}), half-normal (\code{halfnormal()}), -normal (\code{normal()}), gamma (\code{gamma}), and -truncated normal distribution (\code{tnormal()}). All parameters are -vectorized so for regression coefficient vector beta you can define prior -for example as \code{normal(0, 0, c(10, 20))}. +prior distributions for the hyperparameters theta for some of the model +objects of \code{bssm} package. Note that these priors do not include the +constant terms as they do not affect the sampling. } \details{ +Currently supported priors are +\itemize{ +\item uniform prior (\code{uniform()}) with a probability density function (pdf) +defined as \eqn{\frac{1}{max - min}} for \eqn{min < theta < max}. +\item normal (\code{normal()}), a normal distribution parameterized via mean and +standard deviation, i.e. N(mean, sd^2). +\item truncated normal distribution (\code{tnormal()}), a normal distribution +with known truncation points (from below and/or above). Ignoring the +scaling factors, this corresponds to the pdf of N(mean, sd^2) when +\eqn{min < theta < max} and zero otherwise. +\item half-normal (\code{halfnormal()}) with a pdf matching the pdf of the +truncated normal distribution with min=0 and max=inf. +\item gamma (\code{gamma}), a gamma distribution with shape and rate +parameterization. +} + +All parameters are vectorized so for regression coefficient vector beta you +can define prior for example as \code{normal(0, 0, c(10, 20))}. + +For the general exponential models, i.e. models built with the \code{ssm_ulg}, +\code{ssm_ung}, \code{ssm_mlg}, and \code{ssm_mng}, you can define arbitrary priors by +defining the \code{prior_fn} function, which takes the one argument, \code{theta}, +corresponding to the hyperparameter vector of the model, +and returns a log-density of the (joint) prior (see the R Journal paper and +e.g. \code{ssm_ulg} for examples). Similarly, the priors for the non-linear +models (\code{ssm_nlg}) and SDE models (\code{ssm_sde}) are constructed +via C++ snippets (see the vignettes for details). + The longer name versions of the prior functions with \code{_prior} ending are identical with shorter versions and they are available only to avoid clash with R's primitive function \code{gamma} (other long prior names are just for consistent naming). } \examples{ + # create uniform prior on [-1, 1] for one parameter with initial value 0.2: uniform(init = 0.2, min = -1.0, max = 1.0) # two normal priors at once i.e. for coefficients beta: @@ -92,13 +117,13 @@ uniform(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) normal(c(0, 0.2), c(-1.0, 0.001), c(1.0, 1.2)) tnormal(c(2, 2.2), c(-1.0, 0.001), c(1.0, 1.2), c(1.2, 2), 3.3) halfnormal(c(0, 0.2), c(1.0, 1.2)) -# not run because autotest bug +# not run because autotest bug # gamma(c(0.1, 0.2), c(1.2, 2), c(3.3, 3.3)) # longer versions: uniform_prior(init = c(0, 0.2), min = c(-1.0, 0.001), max = c(1.0, 1.2)) normal_prior(init = c(0, 0.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2)) -tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), +tnormal_prior(init = c(2, 2.2), mean = c(-1.0, 0.001), sd = c(1.0, 1.2), min = c(1.2, 2), max = 3.3) halfnormal_prior(init = c(0, 0.2), sd = c(1.0, 1.2)) gamma_prior(init = c(0.1, 0.2), shape = c(1.2, 2), rate = c(3.3, 3.3)) From 47efd047286a27696378c79c58679ccaa9f8254f Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 11:40:45 +0300 Subject: [PATCH 166/180] a default plot method for the MCMC output --- DESCRIPTION | 3 +- NAMESPACE | 2 + NEWS.md | 5 +- R/as_draws.R | 114 ++++++++++++++++++++---------------- R/bssm-package.R | 21 +++++-- R/plot_mcmc.R | 38 ++++++++++++ man/as_draws-mcmc_output.Rd | 8 +-- man/bssm.Rd | 20 +++++-- man/plot.mcmc_output.Rd | 39 ++++++++++++ 9 files changed, 185 insertions(+), 65 deletions(-) create mode 100644 R/plot_mcmc.R create mode 100644 man/plot.mcmc_output.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 85223452..8cab6f3d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.1.2 +Version: 2.0.1.3 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -38,6 +38,7 @@ Suggests: testthat Imports: magrittr, + bayesplot, checkmate, coda (>= 0.18-1), diagis, diff --git a/NAMESPACE b/NAMESPACE index 11e92aa1..87b4acff 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -24,6 +24,7 @@ S3method(particle_smoother,lineargaussian) S3method(particle_smoother,nongaussian) S3method(particle_smoother,ssm_nlg) S3method(particle_smoother,ssm_sde) +S3method(plot,mcmc_output) S3method(predict,mcmc_output) S3method(print,mcmc_output) S3method(run_mcmc,lineargaussian) @@ -82,6 +83,7 @@ export(ukf) export(uniform) export(uniform_prior) importFrom(Rcpp,evalCpp) +importFrom(bayesplot,mcmc_combo) importFrom(checkmate,test_atomic_vector) importFrom(checkmate,test_count) importFrom(checkmate,test_double) diff --git a/NEWS.md b/NEWS.md index 88ddcf7d..24a41113 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,9 +1,12 @@ -bssm 2.0.1.2 (2022-) +bssm 2.0.1.3 (2022-) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future changes in the package. * Added more details to the `?bssm` help page. * Added more details to the `?bssm_prior` help page. + * Added option to extract only hyperparameters in `as_draws` method. Also + fixed a but in `as_draws` which caused the it to ignore `states` argument. + * Added a default plot method for the `run_mcmc` output. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/as_draws.R b/R/as_draws.R index ec1260da..523b78e5 100644 --- a/R/as_draws.R +++ b/R/as_draws.R @@ -1,104 +1,116 @@ #' Convert \code{run_mcmc} Output to \code{draws_df} Format #' -#' Converts MCMC output from \code{run_mcmc} call to a -#' \code{draws_df} format of the \code{posterior} package. This enables the use -#' of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} -#' packages. -#' -#' @note The jump chain representation is automatically expanded by -#' \code{as_draws}, but if \code{run_mcmc} used IS-MCMC method, the output -#' contains additional \code{weight} column corresponding to the IS-weights -#' (without counts), which is ignored by \code{posterior} and \code{bayesplot}, +#' Converts MCMC output from \code{run_mcmc} call to a +#' \code{draws_df} format of the \code{posterior} package. This enables the use +#' of diagnostics and plotting methods of \code{posterior} and \code{bayesplot} +#' packages. +#' +#' @note The jump chain representation is automatically expanded by +#' \code{as_draws}, but if \code{run_mcmc} used IS-MCMC method, the output +#' contains additional \code{weight} column corresponding to the IS-weights +#' (without counts), which is ignored by \code{posterior} and \code{bayesplot}, #' i.e. those results correspond to approximate MCMC. -#' +#' #' @param x An object of class \code{mcmc_output}. -#' @param times A vector of indices defining which time points to return? -#' Default is all. -#' @param states A vector of indices defining which states to return. -#' Default is all. +#' @param times A vector of indices defining which time points to return? +#' Default is all. If 0, no samples for the states are extracted. +#' @param states A vector of indices defining which states to return. +#' Default is all. If 0, no samples for the states are extracted. #' @param ... Ignored. #' @return A \code{draws_df} object. #' @importFrom posterior as_draws as_draws_df #' @importFrom tidyr pivot_wider -#' @aliases as_draws as_draws_df +#' @aliases as_draws as_draws_df #' @export #' @export as_draws_df #' @rdname as_draws-mcmc_output #' @method as_draws_df mcmc_output -#' @examples -#' -#' model <- bsm_lg(Nile, +#' @examples +#' +#' model <- bsm_lg(Nile, #' sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), #' sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), #' a1 = 1000, P1 = 500^2) -#' +#' #' fit1 <- run_mcmc(model, iter = 2000) #' draws <- as_draws(fit1) #' head(draws, 4) #' estimate_ess(draws$sd_y) #' summary(fit1, return_se = TRUE) -#' +#' #' # More chains: #' model$theta[] <- c(50, 150) # change initial value #' fit2 <- run_mcmc(model, iter = 2000, verbose = FALSE) #' model$theta[] <- c(150, 50) # change initial value #' fit3 <- run_mcmc(model, iter = 2000, verbose = FALSE) -#' -#' # it is actually enough to transform first mcmc_output to draws object, +#' +#' # it is actually enough to transform first mcmc_output to draws object, #' # rest are transformed automatically inside bind_draws #' draws <- posterior::bind_draws(as_draws(fit1), #' as_draws(fit2), as_draws(fit3), along = "chain") -#' +#' #' posterior::rhat(draws$sd_y) -#' +#' as_draws_df.mcmc_output <- function(x, times, states, ...) { - + d_theta <- as.data.frame(x, variable = "theta", expand = TRUE) - + if (missing(times)) { times <- seq_len(nrow(x$alpha)) } else { - if (!test_integerish(times, lower = 1, upper = nrow(x$alpha), - any.missing = FALSE, unique = TRUE)) - stop(paste0("Argument 'times' should contain indices between 1 and ", - nrow(x$alpha),".")) + if (!identical(times, 0)) { + if (!test_integerish(times, lower = 1, upper = nrow(x$alpha), + any.missing = FALSE, unique = TRUE)) { + stop("Argument 'times' should contain indices between 1 and ", + nrow(x$alpha), ", or it should be a scalar 0.") + } + } } if (missing(states)) { states <- seq_len(ncol(x$alpha)) } else { - if (!test_integerish(states, lower = 1, upper = ncol(x$alpha), - any.missing = FALSE, unique = TRUE)) - stop(paste0("Argument 'states' should contain indices between 1 and ", - ncol(x$alpha),".")) + if (!identical(states, 0)) { + if (!test_integerish(states, lower = 1, upper = ncol(x$alpha), + any.missing = FALSE, unique = TRUE)) + stop("Argument 'states' should contain indices between 1 and ", + ncol(x$alpha)," or it should be a scalar 0.") + } } - d_states <- as.data.frame(x, variable = "states", expand = TRUE, - times = times, states = states, use_times = FALSE) - - d <- cbind( - tidyr::pivot_wider(d_theta, - values_from = .data$value, - names_from = .data$variable), - tidyr::pivot_wider(d_states, - values_from = .data$value, - names_from = c(.data$variable, .data$time), - names_glue = "{variable}[{time}]")[, -(1:2)]) + if (identical(times, 0) || identical(states, 0)) { + d <- + tidyr::pivot_wider(d_theta, + values_from = .data$value, + names_from = .data$variable) + } else { + d_states <- as.data.frame(x, variable = "states", expand = TRUE, + times = times, states = states, use_times = FALSE) + d <- cbind( + tidyr::pivot_wider(d_theta, + values_from = .data$value, + names_from = .data$variable), + tidyr::pivot_wider(d_states, + values_from = .data$value, + names_from = c(.data$variable, .data$time), + names_glue = "{variable}[{time}]")[, -(1:2)]) + } + names(d)[1] <- ".iteration" - + if (x$mcmc_type %in% paste0("is", 1:3)) { - warning(paste("Input is based on a IS-MCMC and the output column 'weight'", - "contains the IS-weights. These are not used for example in the", + warning(paste("Input is based on a IS-MCMC and the output column 'weight'", + "contains the IS-weights. These are not used for example in the", "diagnostic methods by 'posterior' package, i.e. these are based", "on approximate MCMC chains.")) } else { d$weight <- NULL } - + as_draws(d) -} +} #' @export #' @export as_draws #' @rdname as_draws-mcmc_output #' @method as_draws mcmc_output as_draws.mcmc_output <- function(x, times, states, ...) { - as_draws_df.mcmc_output(x, times, ...) + as_draws_df.mcmc_output(x, times, states, ...) } diff --git a/R/bssm-package.R b/R/bssm-package.R index e24b93c4..51bf5169 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -52,10 +52,12 @@ #' The output of the `run_mcmc` can be analysed by extracting the posterior #' samples of the latent states and hyperparameters using `as.data.frame`, #' `as_draws`, `expand_sample`, and `summary` methods, as well as `fitted` and -#' `predict` methods. Functionality of the `ggplot2`, `bayesplot`, can be used -#' to visualize the posterior draws or their summary statistics, and further -#' diagnostics checks can be performed with the help of the `posterior` for -#' example. +#' `predict` methods. Some MCMC diagnostics checks are available via +#' `check_diagnostics` function, some of which are also provided via the print +#' method of the `run_mcmc` output. Functionality of the `ggplot2` and +#' `bayesplot`, can be used to visualize the posterior draws or their summary +#' statistics, and further diagnostics checks can be performed with the help of +#' the `posterior` and `coda` packages. #' #' @references #' Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and @@ -75,6 +77,10 @@ #' Bürkner P, Gabry J, Kay M, Vehtari A (2022). “posterior: Tools for Working #' with Posterior Distributions.” R package version 1.2.1, #' https://mc-stan.org/posterior. +#' +#' Martyn Plummer, Nicky Best, Kate Cowles and Karen Vines (2006). CODA: +#' Convergence Diagnosis and Output Analysis for MCMC, R News, vol 6, 7-11. +#' #' @docType package #' @name bssm #' @aliases bssm @@ -97,9 +103,15 @@ #' # comply with the CRAN's check requirements) #' fit <- run_mcmc(model, iter = 2000) #' +#' # Some diagnostics checks: +#' check_diagnostics(fit) +#' #' # print some summary information: #' fit #' +#' # traceplots: +#' plot(fit) +#' #' # extract the summary statistics for state variable #' sumr <- summary(fit,variable = "states") #' @@ -109,6 +121,7 @@ #' geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`),alpha = 0.25) + #' geom_line() + #' theme_bw() +#' NULL #' Deaths by drowning in Finland in 1969-2019 #' diff --git a/R/plot_mcmc.R b/R/plot_mcmc.R new file mode 100644 index 00000000..96ca0d06 --- /dev/null +++ b/R/plot_mcmc.R @@ -0,0 +1,38 @@ +#' Trace and Density Plots for `mcmc_output` +#' +#' Plots the trace and density plots of the hyperparameters theta from the MCMC +#' run by \code{\link{run_mcmc}}. +#' +#' For further visualization (of the states), you can extract the posterior +#' samples with `as.data.frame` and `as_draws` methods to be used for example +#' with the `bayesplot` or `ggplot2` packages. +#' +#' +#' @note For IS-MCMC, these plots correspond to the approximate (non-weighted) +#' samples +#' . +#' @method plot mcmc_output +#' @importFrom bayesplot mcmc_combo +#' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. +#' @param ... Further arguments to \code{\link{bayesplot::mcmc_combo}}. +#' @return The output object from \code{bayesplot::mcmc_combo}. +#' @seealso \code{\link{check_diagnostics}} for a quick diagnostics statistics +#' of the model. +#' @export +#' @examples +#' data("negbin_model") +#' # Note the very small number of iterations, so the plots look bad +#' plot(negbin_model) +plot.mcmc_output <- function(x, ...) { + + # suppress the duplicate warning about the IS-MCMC + out <- suppressWarnings(as_draws(x, states = 0)) + + if (x$mcmc_type %in% paste0("is", 1:3)) { + warning("Input is based on a IS-weighted MCMC, the plots ", + "correspond to the approximate MCMC.") + # remove the weight variable + out <- out[, -1] + } + mcmc_combo(out, ...) +} diff --git a/man/as_draws-mcmc_output.Rd b/man/as_draws-mcmc_output.Rd index da04b33a..c75bff58 100644 --- a/man/as_draws-mcmc_output.Rd +++ b/man/as_draws-mcmc_output.Rd @@ -15,10 +15,10 @@ \item{x}{An object of class \code{mcmc_output}.} \item{times}{A vector of indices defining which time points to return? -Default is all.} +Default is all. If 0, no samples for the states are extracted.} \item{states}{A vector of indices defining which states to return. -Default is all.} +Default is all. If 0, no samples for the states are extracted.} \item{...}{Ignored.} } @@ -40,7 +40,7 @@ i.e. those results correspond to approximate MCMC. } \examples{ -model <- bsm_lg(Nile, +model <- bsm_lg(Nile, sd_y = tnormal(init = 100, mean = 100, sd = 100, min = 0), sd_level = tnormal(init = 50, mean = 50, sd = 100, min = 0), a1 = 1000, P1 = 500^2) @@ -57,7 +57,7 @@ fit2 <- run_mcmc(model, iter = 2000, verbose = FALSE) model$theta[] <- c(150, 50) # change initial value fit3 <- run_mcmc(model, iter = 2000, verbose = FALSE) -# it is actually enough to transform first mcmc_output to draws object, +# it is actually enough to transform first mcmc_output to draws object, # rest are transformed automatically inside bind_draws draws <- posterior::bind_draws(as_draws(fit1), as_draws(fit2), as_draws(fit3), along = "chain") diff --git a/man/bssm.Rd b/man/bssm.Rd index 7c1923e4..463b8ec2 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -55,10 +55,12 @@ Helske, Franks (2020), and the package vignettes. The output of the \code{run_mcmc} can be analysed by extracting the posterior samples of the latent states and hyperparameters using \code{as.data.frame}, \code{as_draws}, \code{expand_sample}, and \code{summary} methods, as well as \code{fitted} and -\code{predict} methods. Functionality of the \code{ggplot2}, \code{bayesplot}, can be used -to visualize the posterior draws or their summary statistics, and further -diagnostics checks can be performed with the help of the \code{posterior} for -example. +\code{predict} methods. Some MCMC diagnostics checks are available via +\code{check_diagnostics} function, some of which are also provided via the print +method of the \code{run_mcmc} output. Functionality of the \code{ggplot2} and +\code{bayesplot}, can be used to visualize the posterior draws or their summary +statistics, and further diagnostics checks can be performed with the help of +the \code{posterior} and \code{coda} packages. } \examples{ # Create a local level model (latent random walk + noise) to the Nile @@ -75,9 +77,15 @@ model <- bsm_lg(Nile, # comply with the CRAN's check requirements) fit <- run_mcmc(model, iter = 2000) +# Some diagnostics checks: +check_diagnostics(fit) + # print some summary information: fit +# traceplots: +plot(fit) + # extract the summary statistics for state variable sumr <- summary(fit,variable = "states") @@ -87,6 +95,7 @@ ggplot(sumr, aes(time, Mean)) + geom_ribbon(aes(ymin = `2.5\%`, ymax = `97.5\%`),alpha = 0.25) + geom_line() + theme_bw() + } \references{ Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and @@ -106,4 +115,7 @@ version 1.9.0, https://mc-stan.org/bayesplot. Bürkner P, Gabry J, Kay M, Vehtari A (2022). “posterior: Tools for Working with Posterior Distributions.” R package version 1.2.1, https://mc-stan.org/posterior. + +Martyn Plummer, Nicky Best, Kate Cowles and Karen Vines (2006). CODA: +Convergence Diagnosis and Output Analysis for MCMC, R News, vol 6, 7-11. } diff --git a/man/plot.mcmc_output.Rd b/man/plot.mcmc_output.Rd new file mode 100644 index 00000000..c4b9c896 --- /dev/null +++ b/man/plot.mcmc_output.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_mcmc.R +\name{plot.mcmc_output} +\alias{plot.mcmc_output} +\title{Trace and Density Plots for \code{mcmc_output}} +\usage{ +\method{plot}{mcmc_output}(x, ...) +} +\arguments{ +\item{x}{Object of class \code{mcmc_output} from \code{\link{run_mcmc}}.} + +\item{...}{Further arguments to \code{\link{bayesplot::mcmc_combo}}.} +} +\value{ +The output object from \code{bayesplot::mcmc_combo}. +} +\description{ +Plots the trace and density plots of the hyperparameters theta from the MCMC +run by \code{\link{run_mcmc}}. +} +\details{ +For further visualization (of the states), you can extract the posterior +samples with \code{as.data.frame} and \code{as_draws} methods to be used for example +with the \code{bayesplot} or \code{ggplot2} packages. +} +\note{ +For IS-MCMC, these plots correspond to the approximate (non-weighted) +samples +. +} +\examples{ +data("negbin_model") +# Note the very small number of iterations, so the plots look bad +plot(negbin_model) +} +\seealso{ +\code{\link{check_diagnostics}} for a quick diagnostics statistics +of the model. +} From eaec160336fa966f34ff82c8c2066d90f83bcdb9 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 11:46:04 +0300 Subject: [PATCH 167/180] point to the other vignettes regarding nlg and sde models --- vignettes/bssm.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index e4160dee..f2d52069 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -140,7 +140,7 @@ Here we use helper function `halfnormal` which defines half-Normal prior distrib For non-Gaussian models, function `bsm_ng` can be used for constructing an BSM model where the observations are assumed to be distributed according to Poisson, binomial, negative binomial, or Gamma distribution. The syntax is nearly identical as in case of `bsm_lg`, but we now define also the distribution via argument `distribution`, and depending on the model, we can also define parameters `u` and `phi`. For Poisson and negative binomial models, the known parameter `u` corresponds to the offset term, whereas in case of binomial model `u` defines the number of trials. For negative binomial model, argument `phi` defines the dispersion term, which can be given as a fixed value, or as a prior function. For same observational densities, a model where the state equation follows a first order autoregressive process can be defined using the function `ng_ar1`. Finally, a stochastic volatility model can be defined using a function `svm`, and an arbitrary linear-Gaussian state model with Poisson, binomial or negative binomial distributed observations can be defined with `ssm_ung` and `ssm_mng` for univariate and multivariate models respectively. -For models where the state equation is no longer linear-Gaussian, we can use our pointer-based C++ interface with the function `ssm_nlg`. Diffusion models can be defined with the function `ssm_sde`. +For models where the state equation is no longer linear-Gaussian, we can use our pointer-based C++ interface with the function `ssm_nlg`. Diffusion models can be defined with the function `ssm_sde`. For details regarding these types of models, see the corresponding vignettes `growth_model` and `sde_model` respectively. ## Filtering and smoothing From ec77f8c085572cfdac8a70b1aa05a29991b69ec8 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 12:28:16 +0300 Subject: [PATCH 168/180] link to mcmc_combo --- R/plot_mcmc.R | 2 +- man/plot.mcmc_output.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/plot_mcmc.R b/R/plot_mcmc.R index 96ca0d06..df3aa9ca 100644 --- a/R/plot_mcmc.R +++ b/R/plot_mcmc.R @@ -15,7 +15,7 @@ #' @importFrom bayesplot mcmc_combo #' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. #' @param ... Further arguments to \code{\link{bayesplot::mcmc_combo}}. -#' @return The output object from \code{bayesplot::mcmc_combo}. +#' @return The output object from \code{\link{bayesplot::mcmc_combo}}. #' @seealso \code{\link{check_diagnostics}} for a quick diagnostics statistics #' of the model. #' @export diff --git a/man/plot.mcmc_output.Rd b/man/plot.mcmc_output.Rd index c4b9c896..58ab7b81 100644 --- a/man/plot.mcmc_output.Rd +++ b/man/plot.mcmc_output.Rd @@ -12,7 +12,7 @@ \item{...}{Further arguments to \code{\link{bayesplot::mcmc_combo}}.} } \value{ -The output object from \code{bayesplot::mcmc_combo}. +The output object from \code{\link{bayesplot::mcmc_combo}}. } \description{ Plots the trace and density plots of the hyperparameters theta from the MCMC From 0d6b6761062acc9dee0f345ee0395cba3235b458 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Wed, 6 Jul 2022 13:20:30 +0300 Subject: [PATCH 169/180] fix the link --- R/plot_mcmc.R | 4 ++-- man/plot.mcmc_output.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/plot_mcmc.R b/R/plot_mcmc.R index df3aa9ca..241a7b63 100644 --- a/R/plot_mcmc.R +++ b/R/plot_mcmc.R @@ -14,8 +14,8 @@ #' @method plot mcmc_output #' @importFrom bayesplot mcmc_combo #' @param x Object of class \code{mcmc_output} from \code{\link{run_mcmc}}. -#' @param ... Further arguments to \code{\link{bayesplot::mcmc_combo}}. -#' @return The output object from \code{\link{bayesplot::mcmc_combo}}. +#' @param ... Further arguments to [bayesplot::mcmc_combo]. +#' @return The output object from [bayesplot::mcmc_combo]. #' @seealso \code{\link{check_diagnostics}} for a quick diagnostics statistics #' of the model. #' @export diff --git a/man/plot.mcmc_output.Rd b/man/plot.mcmc_output.Rd index 58ab7b81..b6ee2d38 100644 --- a/man/plot.mcmc_output.Rd +++ b/man/plot.mcmc_output.Rd @@ -9,10 +9,10 @@ \arguments{ \item{x}{Object of class \code{mcmc_output} from \code{\link{run_mcmc}}.} -\item{...}{Further arguments to \code{\link{bayesplot::mcmc_combo}}.} +\item{...}{Further arguments to \link[bayesplot:MCMC-combos]{bayesplot::mcmc_combo}.} } \value{ -The output object from \code{\link{bayesplot::mcmc_combo}}. +The output object from \link[bayesplot:MCMC-combos]{bayesplot::mcmc_combo}. } \description{ Plots the trace and density plots of the hyperparameters theta from the MCMC From 809dd3a58a818c7868106972ffe9e01fb2c4a6e0 Mon Sep 17 00:00:00 2001 From: helske <jouni.helske@iki.fi> Date: Tue, 7 Feb 2023 15:25:50 +0200 Subject: [PATCH 170/180] small tweaks to Rmds, update gha workflow --- .github/workflows/R-CMD-check.yaml | 6 ++--- vignettes/bssm.Rmd | 3 ++- vignettes/growth_model.Rmd | 8 ++++--- vignettes/psi_pf.Rmd | 38 +++++++++++++++++++----------- 4 files changed, 34 insertions(+), 21 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 64d34922..8faf20e0 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -30,15 +30,15 @@ jobs: steps: - uses: actions/checkout@v2 - - uses: r-lib/actions/setup-pandoc@v1 + - uses: r-lib/actions/setup-pandoc@v2 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} http-user-agent: ${{ matrix.config.http-user-agent }} use-public-rspm: true - - uses: r-lib/actions/setup-r-dependencies@v1 + - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: rcmdcheck, covr diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index f2d52069..f60ead6a 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -16,7 +16,8 @@ vignette: | ```{r, echo = FALSE} if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { - warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") + warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version", + "1.12.3. These were not found. Older versions will not work.") knitr::knit_exit() } ``` diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 6ac3628c..29a8ab6e 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -110,7 +110,7 @@ This takes a few seconds. let's define our initial guess for $\theta$, the logar ```{r theta} initial_theta <- c(log_H = 0, log_R1 = log(0.05), log_R2 = 0) -# dT, K, a1 and the prior variances of first and second state (logit r and and p) +# dT, K, a1 and the prior variances of 1st and 2nd state (logit r and and p) known_params <- c(dT = dT, K = K, a11 = -1, a12 = 50, P11 = 1, P12 = 100) ``` @@ -136,8 +136,10 @@ Let's first run Extended Kalman filter and smoother using our initial guess for ```{r ekf} out_filter <- ekf(model) out_smoother <- ekf_smoother(model) -ts.plot(cbind(y, out_filter$att[, 2], out_smoother$alphahat[, 2]), col = 1:3) -ts.plot(plogis(cbind(out_filter$att[, 1], out_smoother$alphahat[, 1])), col = 1:2) +ts.plot(cbind(y, out_filter$att[, 2], + out_smoother$alphahat[, 2]), col = 1:3) +ts.plot(plogis(cbind(out_filter$att[, 1], + out_smoother$alphahat[, 1])), col = 1:2) ``` ## Markov chain Monte Carlo diff --git a/vignettes/psi_pf.Rmd b/vignettes/psi_pf.Rmd index e93754ac..99c368a8 100644 --- a/vignettes/psi_pf.Rmd +++ b/vignettes/psi_pf.Rmd @@ -146,7 +146,8 @@ growth_model_experiment <- function(n_cores, nsim, particles) { p <- numeric(n) p[1] <- p1 for(i in 2:n) - p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / (K + p[i-1] * (exp(r[i-1] * dT) - 1)), 1) + p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / + (K + p[i-1] * (exp(r[i-1] * dT) - 1)), 1) # observations y <- p + rnorm(n, 0, 1) @@ -158,7 +159,8 @@ growth_model_experiment <- function(n_cores, nsim, particles) { cl<-makeCluster(n_cores) registerDoParallel(cl) - results <- foreach (j = 1:n_cores, .combine = "rbind", .packages = "bssm") %dopar% { + results <- foreach (j = 1:n_cores, .combine = "rbind", + .packages = "bssm") %dopar% { Rcpp::sourceCpp("growth_model_functions.cpp") pntrs <- create_xptrs() @@ -171,17 +173,20 @@ growth_model_experiment <- function(n_cores, nsim, particles) { bsf <- ekpf <- psi <- matrix(NA, 10, nsim / n_cores) - for(i in 1:ncol(bsf)) { + for(i in seq_len(ncol(bsf))) { - time <- system.time(out <- particle_smoother(model, particles = particles, method = "bsf"))[3] + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "bsf"))[3] bsf[, i] <- c(out$logLik, out$alphahat[1, ], diag(out$Vt[, , 1]), out$alphahat[n, ], diag(out$Vt[, , n]), time) - time <- system.time(out <- particle_smoother(model, particles = particles, method = "psi"))[3] + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "psi"))[3] psi[, i] <- c(out$logLik, out$alphahat[1, ], diag(out$Vt[, , 1]), out$alphahat[n, ], diag(out$Vt[, , n]), time) - time <- system.time(out <- particle_smoother(model, particles = particles, method = "ekf"))[3] + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "ekf"))[3] ekpf[, i] <- c(out$logLik, out$alphahat[1, ], diag(out$Vt[, , 1]), out$alphahat[n, ], diag(out$Vt[, , n]), time) } @@ -222,7 +227,8 @@ r <- plogis(cumsum(c(-1.5, rnorm(n - 1, sd = 0.05)))) p <- numeric(n) p[1] <- p1 for(i in 2:n) - p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / (K + p[i-1] * (exp(r[i-1] * dT) - 1)), 1) + p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / + (K + p[i-1] * (exp(r[i-1] * dT) - 1)), 1) # observations y <- p + rnorm(n, 0, 1) @@ -269,7 +275,7 @@ sumr <- results %>% group_by(method, N) %>% summarise(mean = mean(logLik), SD = sd(logLik), IRE = IRE(logLik, time), time = mean(time)) table1 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, - caption = "Results for the log-likelihood estimates of the growth model. ") + caption = "Results for the log-likelihood estimates of the growth model. ") saveRDS(table1, file = "psi_pf_experiments/table1.rds") ``` ```{r tabl21, echo = FALSE} @@ -316,7 +322,8 @@ ar_exp_model_experiment <- function(n_cores, nsim, particles, theta) { cl<-makeCluster(n_cores) registerDoParallel(cl) - results <- foreach (j = 1:n_cores, .combine = "rbind", .packages = "bssm") %dopar% { + results <- foreach (j = 1:n_cores, .combine = "rbind", + .packages = "bssm") %dopar% { Rcpp::sourceCpp("ar_exp_model_functions.cpp") pntrs <- create_xptrs() @@ -331,16 +338,19 @@ ar_exp_model_experiment <- function(n_cores, nsim, particles, theta) { bsf <- ekpf <- psi <- matrix(NA, 6, nsim / n_cores) - for(i in 1:ncol(bsf)) { - time <- system.time(out <- particle_smoother(model, particles = particles, method = "bsf"))[3] + for(i in seq_len(ncol(bsf))) { + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "bsf"))[3] bsf[, i] <- c(out$logLik, out$alphahat[1, ], out$Vt[, , 1], out$alphahat[n, ], out$Vt[, , n], time) - time <- system.time(out <- particle_smoother(model, particles = particles, method = "psi"))[3] + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "psi"))[3] psi[, i] <- c(out$logLik, out$alphahat[1, ], out$Vt[, , 1], out$alphahat[n, ], out$Vt[, , n], time) - time <- system.time(out <- particle_smoother(model, particles = particles, method = "ekf"))[3] + time <- system.time(out <- particle_smoother(model, + particles = particles, method = "ekf"))[3] ekpf[, i] <- c(out$logLik, out$alphahat[1, ], out$Vt[, , 1], out$alphahat[n, ], out$Vt[, , n], time) } @@ -409,7 +419,7 @@ sumr <- results %>% group_by(method, N) %>% summarise(mean = mean(logLik), SD = sd(logLik), IRE = IRE(logLik, time), time = mean(time)) table3 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, - caption = "Results for the log-likelihood estimates of the AR model. ") + caption = "Results for the log-likelihood estimates of the AR model. ") saveRDS(table3, file = "psi_pf_experiments/table3.rds") ``` ```{r table3, echo = FALSE} From 7fd79101cc3793fd2494c52377720601a12f74c1 Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Mon, 4 Sep 2023 13:37:46 +0300 Subject: [PATCH 171/180] update aliases --- DESCRIPTION | 6 +++--- NEWS.md | 6 ++++-- R/bssm-package.R | 2 +- man/bssm.Rd | 1 + 4 files changed, 9 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8cab6f3d..7a894637 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.1.3 +Version: 2.0.2 Authors@R: c(person(given = "Jouni", family = "Helske", @@ -48,13 +48,13 @@ Imports: rlang, tidyr LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo -SystemRequirements: C++11, pandoc (>= 1.12.3, needed for vignettes) +SystemRequirements: pandoc (>= 1.12.3, needed for vignettes) VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues URL: https://github.com/helske/bssm ByteCompile: true Encoding: UTF-8 NeedsCompilation: yes -RoxygenNote: 7.2.0 +RoxygenNote: 7.2.3 Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd", "srr::srr_stats_roclet")) diff --git a/NEWS.md b/NEWS.md index 24a41113..64244be1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,12 +1,14 @@ -bssm 2.0.1.3 (2022-) +bssm 2.0.2 (Release date: 2023-09-04) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future changes in the package. * Added more details to the `?bssm` help page. * Added more details to the `?bssm_prior` help page. * Added option to extract only hyperparameters in `as_draws` method. Also - fixed a but in `as_draws` which caused the it to ignore `states` argument. + fixed a bug in `as_draws` which caused the it to ignore `states` argument. * Added a default plot method for the `run_mcmc` output. + * Fixed the aliases of the main help page to accomodate changes in roxygen2. + * Removed explicit C++ version requirement as required by new CRAN policies. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/bssm-package.R b/R/bssm-package.R index 51bf5169..ad0b8fd1 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -83,7 +83,7 @@ #' #' @docType package #' @name bssm -#' @aliases bssm +#' @aliases bssm bssm-package #' @importFrom Rcpp evalCpp #' @importFrom stats as.ts dnorm end frequency is.ts logLik quantile start #' time ts ts.union tsp tsp<- sd na.omit diff --git a/man/bssm.Rd b/man/bssm.Rd index 463b8ec2..09d3377c 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -3,6 +3,7 @@ \docType{package} \name{bssm} \alias{bssm} +\alias{bssm-package} \title{Bayesian Inference of State Space Models} \description{ This package contains functions for efficient Bayesian inference of state From 3c55c2c34d4fe0ed8d769a24c5c944dadfd7a93e Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Mon, 4 Sep 2023 14:49:47 +0300 Subject: [PATCH 172/180] update links --- R/bssm-package.R | 2 +- man/drownings.Rd | 2 +- vignettes/growth_model.Rmd | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/R/bssm-package.R b/R/bssm-package.R index ad0b8fd1..4916aec3 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -135,7 +135,7 @@ NULL #' @docType data #' @format A time series object containing 51 observations. #' @source Statistics Finland -#' \url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. +#' \url{https://stat.fi/tup/tilastotietokannat/index_en.html}. #' @keywords datasets #' @examples #' data("drownings") diff --git a/man/drownings.Rd b/man/drownings.Rd index b29c9c68..93dacc08 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -9,7 +9,7 @@ A time series object containing 51 observations. } \source{ Statistics Finland -\url{https://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/}. +\url{https://stat.fi/tup/tilastotietokannat/index_en.html}. } \description{ Dataset containing number of deaths by drowning in Finland in 1969-2019, diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 29a8ab6e..4138d0d7 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -96,7 +96,7 @@ arma::vec T_fn(const unsigned int t, const arma::vec& alpha, const arma::vec& th } ``` -The name of this function does not matter, but it should always return Armadillo vector (`arma::vec`), and have the same signature (i.e. the order and types of the function's parameters) should always be like above, even though some of the parameters were not used in the body of the function. Note that all of these functions can also depend on some known parameters, given as `known_params` (vector) and `known_tv_params` (matrix) arguments to `ssm_nlg` function (which are then passed to individual `C++` snippets). For details of using Armadillo, see [Armadillo documentation](http://arma.sourceforge.net/docs.html). After defining the appropriate model functions, the `cpp` file should also contain a function for creating external pointers for the aforementioned functions. Why this is needed is more technical issue, but fortunately you can just copy the function from the example file without any modifications. +The name of this function does not matter, but it should always return Armadillo vector (`arma::vec`), and have the same signature (i.e. the order and types of the function's parameters) should always be like above, even though some of the parameters were not used in the body of the function. Note that all of these functions can also depend on some known parameters, given as `known_params` (vector) and `known_tv_params` (matrix) arguments to `ssm_nlg` function (which are then passed to individual `C++` snippets). For details of using Armadillo, see [Armadillo documentation](https://arma.sourceforge.net/docs.html). After defining the appropriate model functions, the `cpp` file should also contain a function for creating external pointers for the aforementioned functions. Why this is needed is more technical issue, but fortunately you can just copy the function from the example file without any modifications. After creating the file for `C++` functions, you need to compile the file using `Rcpp`^[As repeated calls to compile same `cpp` file can sometimes lead to memory issues, it is good practice to define unique cache directory using the `cacheDir` argument([see issue in Github](https://github.com/helske/crashtest/issues/1)). But the CRAN does not like this approach so we do not use it here.]: From 97b489eab709173512ca6f7341a028eccaed98a2 Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Mon, 16 Oct 2023 13:42:49 +0300 Subject: [PATCH 173/180] remove magrittr dependency --- DESCRIPTION | 3 +- NAMESPACE | 1 - NEWS.md | 2 + R/fitted.R | 5 +- R/models.R | 4 +- R/post_correction.R | 24 ++++---- R/predict.R | 38 ++++++------ R/run_mcmc.R | 12 ++-- R/summary.R | 12 ++-- README.Rmd | 18 +++--- README.md | 120 ++++++++++++++++-------------------- benchmarks/replications.Rmd | 2 +- man/bsm_ng.Rd | 4 +- man/post_correct.Rd | 24 ++++---- man/predict.mcmc_output.Rd | 38 ++++++------ man/run_mcmc.Rd | 12 ++-- vignettes/bssm.Rmd | 6 +- vignettes/growth_model.Rmd | 12 ++-- vignettes/psi_pf.Rmd | 16 ++--- vignettes/sde_model.Rmd | 4 +- 20 files changed, 172 insertions(+), 185 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 7a894637..8e2bd0f3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -24,7 +24,7 @@ Description: Efficient methods for Bayesian inference of state space models models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details. License: GPL (>= 2) -Depends: R (>= 3.5.0) +Depends: R (>= 4.1.0) Suggests: covr, ggplot2 (>= 2.0.0), @@ -37,7 +37,6 @@ Suggests: sitmo, testthat Imports: - magrittr, bayesplot, checkmate, coda (>= 0.18-1), diff --git a/NAMESPACE b/NAMESPACE index 87b4acff..d8021a1c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -101,7 +101,6 @@ importFrom(dplyr,as_tibble) importFrom(dplyr,group_by) importFrom(dplyr,summarise) importFrom(dplyr,ungroup) -importFrom(magrittr,"%>%") importFrom(posterior,as_draws) importFrom(posterior,as_draws_df) importFrom(posterior,default_convergence_measures) diff --git a/NEWS.md b/NEWS.md index 64244be1..94b70dd4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -9,6 +9,8 @@ bssm 2.0.2 (Release date: 2023-09-04) * Added a default plot method for the `run_mcmc` output. * Fixed the aliases of the main help page to accomodate changes in roxygen2. * Removed explicit C++ version requirement as required by new CRAN policies. + * Removed `magrittr` dependency and switched to native pipe, leading to + requirement for R 4.1.0+. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/fitted.R b/R/fitted.R index b6d1e0db..f86964d1 100644 --- a/R/fitted.R +++ b/R/fitted.R @@ -5,7 +5,6 @@ #' #' @export #' @importFrom stats fitted -#' @importFrom magrittr %>% #' @importFrom dplyr group_by ungroup summarise as_tibble #' @importFrom diagis weighted_quantile weighted_var weighted_mean weighted_se #' @name fitted.mcmc_output @@ -110,13 +109,13 @@ fitted.mcmc_output <- function(object, model, Variable = variables, Time = rep(time(model$y), each = nrow(pred))) - d %>% dplyr::group_by(.data$Variable, .data$Time) %>% + d |> dplyr::group_by(.data$Variable, .data$Time) |> dplyr::summarise( Mean = weighted_mean(.data$value, w), SD = sqrt(weighted_var(.data$value, w)), dplyr::as_tibble(as.list(weighted_quantile(.data$value, w, probs = probs))), - "SE(Mean)" = as.numeric(sqrt(asymptotic_var(.data$value, w)))) %>% + "SE(Mean)" = as.numeric(sqrt(asymptotic_var(.data$value, w)))) |> dplyr::ungroup() } diff --git a/R/models.R b/R/models.R index 946b3fbe..22956edd 100644 --- a/R/models.R +++ b/R/models.R @@ -898,8 +898,8 @@ bsm_lg <- function(y, sd_y, sd_level, sd_slope, sd_seasonal, #' #' # Traceplot using as.data.frame method for MCMC output #' library("dplyr") -#' as.data.frame(mcmc_out) %>% -#' filter(variable == "sd_level") %>% +#' as.data.frame(mcmc_out) |> +#' filter(variable == "sd_level") |> #' ggplot(aes(y = value, x = iter)) + geom_line() #' #' } diff --git a/R/post_correction.R b/R/post_correction.R index a0642dd7..3e060a15 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -210,34 +210,34 @@ suggest_N <- function(model, theta, #' # latent state #' library("dplyr") #' library("ggplot2") -#' state_approx <- as.data.frame(out_approx, variable = "states") %>% -#' group_by(time) %>% +#' state_approx <- as.data.frame(out_approx, variable = "states") |> +#' group_by(time) |> #' summarise(mean = mean(value)) #' -#' state_exact <- as.data.frame(out_is2, variable = "states") %>% -#' group_by(time) %>% +#' state_exact <- as.data.frame(out_is2, variable = "states") |> +#' group_by(time) |> #' summarise(mean = weighted.mean(value, weight)) #' #' dplyr::bind_rows(approx = state_approx, -#' exact = state_exact, .id = "method") %>% -#' filter(time > 200) %>% +#' exact = state_exact, .id = "method") |> +#' filter(time > 200) |> #' ggplot(aes(time, mean, colour = method)) + #' geom_line() + #' theme_bw() #' #' # posterior means #' p_approx <- predict(out_approx, model, type = "mean", -#' nsim = 1000, future = FALSE) %>% -#' group_by(time) %>% +#' nsim = 1000, future = FALSE) |> +#' group_by(time) |> #' summarise(mean = mean(value)) #' p_exact <- predict(out_is2, model, type = "mean", -#' nsim = 1000, future = FALSE) %>% -#' group_by(time) %>% +#' nsim = 1000, future = FALSE) |> +#' group_by(time) |> #' summarise(mean = mean(value)) #' #' dplyr::bind_rows(approx = p_approx, -#' exact = p_exact, .id = "method") %>% -#' filter(time > 200) %>% +#' exact = p_exact, .id = "method") |> +#' filter(time > 200) |> #' ggplot(aes(time, mean, colour = method)) + #' geom_line() + #' theme_bw() diff --git a/R/predict.R b/R/predict.R index 31d7ffcc..b9b468cc 100644 --- a/R/predict.R +++ b/R/predict.R @@ -52,35 +52,35 @@ #' nsim = 1000) #' #' library("dplyr") -#' sumr_fit <- as.data.frame(mcmc_results, variable = "states") %>% -#' group_by(time, iter) %>% +#' sumr_fit <- as.data.frame(mcmc_results, variable = "states") |> +#' group_by(time, iter) |> #' mutate(signal = #' value[variable == "level"] + -#' value[variable == "seasonal_1"]) %>% -#' group_by(time) %>% +#' value[variable == "seasonal_1"]) |> +#' group_by(time) |> #' summarise(mean = mean(signal), #' lwr = quantile(signal, 0.025), #' upr = quantile(signal, 0.975)) #' -#' sumr_pred <- pred %>% -#' group_by(time, sample) %>% +#' sumr_pred <- pred |> +#' group_by(time, sample) |> #' mutate(signal = #' value[variable == "level"] + -#' value[variable == "seasonal_1"]) %>% -#' group_by(time) %>% +#' value[variable == "seasonal_1"]) |> +#' group_by(time) |> #' summarise(mean = mean(signal), #' lwr = quantile(signal, 0.025), #' upr = quantile(signal, 0.975)) #' #' # If we used type = "mean", we could do -#' # sumr_pred <- pred %>% -#' # group_by(time) %>% +#' # sumr_pred <- pred |> +#' # group_by(time) |> #' # summarise(mean = mean(value), #' # lwr = quantile(value, 0.025), #' # upr = quantile(value, 0.975)) #' #' library("ggplot2") -#' rbind(sumr_fit, sumr_pred) %>% +#' rbind(sumr_fit, sumr_pred) |> #' ggplot(aes(x = time, y = mean)) + #' geom_ribbon(aes(ymin = lwr, ymax = upr), #' fill = "#92f0a8", alpha = 0.25) + @@ -96,23 +96,23 @@ #' meanrep <- predict(mcmc_results, model = model, type = "mean", #' future = FALSE, nsim = 1000) #' -#' sumr_yrep <- yrep %>% -#' group_by(time) %>% +#' sumr_yrep <- yrep |> +#' group_by(time) |> #' summarise(earnings = mean(value), #' lwr = quantile(value, 0.025), -#' upr = quantile(value, 0.975)) %>% +#' upr = quantile(value, 0.975)) |> #' mutate(interval = "Observations") #' -#' sumr_meanrep <- meanrep %>% -#' group_by(time) %>% +#' sumr_meanrep <- meanrep |> +#' group_by(time) |> #' summarise(earnings = mean(value), #' lwr = quantile(value, 0.025), -#' upr = quantile(value, 0.975)) %>% +#' upr = quantile(value, 0.975)) |> #' mutate(interval = "Mean") #' -#' rbind(sumr_meanrep, sumr_yrep) %>% +#' rbind(sumr_meanrep, sumr_yrep) |> #' mutate(interval = -#' factor(interval, levels = c("Observations", "Mean"))) %>% +#' factor(interval, levels = c("Observations", "Mean"))) |> #' ggplot(aes(x = time, y = earnings)) + #' geom_ribbon(aes(ymin = lwr, ymax = upr, fill = interval), #' alpha = 0.75) + diff --git a/R/run_mcmc.R b/R/run_mcmc.R index 470d86a6..e9904c4e 100644 --- a/R/run_mcmc.R +++ b/R/run_mcmc.R @@ -325,9 +325,9 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", #' library("ggplot2") #' #' # compute summary statistics -#' level_sumr <- d_states %>% -#' filter(variable == "level") %>% -#' group_by(time) %>% +#' level_sumr <- d_states |> +#' filter(variable == "level") |> +#' group_by(time) |> #' summarise(mean = diagis::weighted_mean(value, weight), #' lwr = diagis::weighted_quantile(value, weight, #' 0.025), @@ -335,7 +335,7 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", #' 0.975)) #' #' # visualize -#' level_sumr %>% ggplot(aes(x = time, y = mean)) + +#' level_sumr |> ggplot(aes(x = time, y = mean)) + #' geom_line() + #' geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + #' geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + @@ -376,8 +376,8 @@ run_mcmc.lineargaussian <- function(model, iter, output_type = "full", #' # Note small number of iterations for CRAN checks #' out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") #' -#' sumr <- as.data.frame(out, variable = "states") %>% -#' group_by(time) %>% mutate(value = exp(value)) %>% +#' sumr <- as.data.frame(out, variable = "states") |> +#' group_by(time) |> mutate(value = exp(value)) |> #' summarise(mean = mean(value), #' ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) #' ggplot(sumr, aes(time, mean)) + diff --git a/R/summary.R b/R/summary.R index 2c9b11ed..c8466821 100644 --- a/R/summary.R +++ b/R/summary.R @@ -87,9 +87,9 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", } if (variable %in% c("theta", "both")) { sumr_theta <- - as.data.frame(object, variable = "theta", expand = TRUE) %>% - group_by(.data$variable) %>% - summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) %>% + as.data.frame(object, variable = "theta", expand = TRUE) |> + group_by(.data$variable) |> + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) |> as.data.frame() if (variable == "theta") return(sumr_theta) } @@ -117,9 +117,9 @@ summary.mcmc_output <- function(object, return_se = FALSE, variable = "theta", sumr_states <- as.data.frame(object, variable = "states", expand = TRUE, - times = times, states = states, use_times = use_times) %>% - group_by(.data$variable, .data$time) %>% - summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) %>% + times = times, states = states, use_times = use_times) |> + group_by(.data$variable, .data$time) |> + summarise(as_tibble(as.list(summary_f(.data$value, .data$weight)))) |> as.data.frame() if (variable == "states") return(sumr_states) } diff --git a/README.Rmd b/README.Rmd index ec17b4ab..efcde640 100644 --- a/README.Rmd +++ b/README.Rmd @@ -137,7 +137,7 @@ set.seed(1) data("airquality", package = "datasets") # Covariates as matrix. For complex cases, check out as_bssm function -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() +xreg <- airquality |> select(Wind, Temp) |> as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, @@ -152,10 +152,10 @@ fit <- run_mcmc(model, iter = 20000, burnin = 5000) fit obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) |> filter(!is.na(Ozone)) pred <- fitted(fit, model) -pred %>% +pred |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + @@ -185,7 +185,7 @@ Comparison: ```{r compare} pred2 <- fitted(fit2, model2) -bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% +bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), alpha = 0.25) + @@ -214,7 +214,7 @@ We combine these two models as a bivariate Gaussian model with `ssm_mlg`: ```{r missing-values} # predictors (not including solar radiation) for ozone -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() +xreg <- airquality |> select(Wind, Temp) |> as.matrix() # Function which outputs new model components given the parameter vector theta update_fn <- function(theta) { @@ -252,9 +252,9 @@ Draw predictions: pred <- fitted(fit, model) obs <- data.frame(Time = 1:nrow(airquality), - Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) + Solar = airquality$Solar.R) |> filter(!is.na(Solar)) -pred %>% filter(Variable == "Solar") %>% +pred |> filter(Variable == "Solar") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + @@ -265,9 +265,9 @@ pred %>% filter(Variable == "Solar") %>% obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) |> filter(!is.na(Ozone)) -pred %>% filter(Variable == "Ozone") %>% +pred |> filter(Variable == "Ozone") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + diff --git a/README.md b/README.md index dfecfa0d..cf245fd4 100644 --- a/README.md +++ b/README.md @@ -28,22 +28,22 @@ models and discretely observed latent diffusion processes are supported. For details, see -- [The bssm paper on The R - Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), -- [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) -- Paper on [Importance sampling type estimators based on approximate - marginal Markov chain Monte - Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) +- [The bssm paper on The R + Journal](https://journal.r-project.org/archive/2021/RJ-2021-103/index.html), +- [Package vignettes at CRAN](https://CRAN.R-project.org/package=bssm) +- Paper on [Importance sampling type estimators based on approximate + marginal Markov chain Monte + Carlo](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12492) There are also couple posters and a talk related to IS-correction methodology and bssm package: -- [UseR!2021 talk - slides](https://jounihelske.netlify.app/talk/user2021/) -- [SMC 2017 workshop: Accelerating MCMC with an - approximation](http://users.jyu.fi/~jovetale/posters/SMC2017) -- [UseR!2017: Bayesian non-Gaussian state space models in - R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) +- [UseR!2021 talk + slides](https://jounihelske.netlify.app/talk/user2021/) +- [SMC 2017 workshop: Accelerating MCMC with an + approximation](http://users.jyu.fi/~jovetale/posters/SMC2017) +- [UseR!2017: Bayesian non-Gaussian state space models in + R](http://users.jyu.fi/~jovetale/posters/user2017.pdf) The `bssm` package was originally developed with the support of Academy of Finland grants 284513, 312605, 311877, and 331817. Current @@ -94,14 +94,13 @@ allowed); ``` r library("bssm") -#> Warning: package 'bssm' was built under R version 4.1.3 +#> Warning: package 'bssm' was built under R version 4.3.1 #> #> Attaching package: 'bssm' #> The following object is masked from 'package:base': #> #> gamma library("dplyr") -#> Warning: package 'dplyr' was built under R version 4.1.3 #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': @@ -111,13 +110,13 @@ library("dplyr") #> #> intersect, setdiff, setequal, union library("ggplot2") -#> Warning: package 'ggplot2' was built under R version 4.1.3 +#> Warning: package 'ggplot2' was built under R version 4.3.1 set.seed(1) data("airquality", package = "datasets") # Covariates as matrix. For complex cases, check out as_bssm function -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() +xreg <- airquality |> select(Wind, Temp) |> as.matrix() model <- bsm_lg(airquality$Ozone, xreg = xreg, @@ -143,11 +142,11 @@ fit #> Summary for theta: #> #> variable Mean SE SD 2.5% 97.5% ESS +#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 +#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 #> sd_level 6.3731836 0.113153715 2.8013937 1.52958636 12.403961 613 #> sd_slope 0.3388712 0.010355574 0.2833955 0.04210885 1.070284 749 #> sd_y 20.8618647 0.068145131 1.9369381 17.08728231 24.722309 808 -#> Temp 1.0265846 0.007497538 0.2064343 0.60226671 1.400436 758 -#> Wind -2.5183269 0.020978488 0.5764833 -3.68987992 -1.327578 755 #> #> Summary for alpha_154: #> @@ -157,13 +156,13 @@ fit #> #> Run time: #> user system elapsed -#> 1.00 0.03 1.03 +#> 0.57 0.02 0.63 obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) |> filter(!is.na(Ozone)) pred <- fitted(fit, model) -pred %>% +pred |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + @@ -178,6 +177,7 @@ pred %>% Same model but now assuming observations are from Gamma distribution: ``` r + model2 <- bsm_ng(airquality$Ozone, xreg = xreg, beta = normal(rep(0, ncol(xreg)), 0, 1), @@ -202,17 +202,17 @@ fit2 #> Summary for theta: #> #> variable Mean SE SD 2.5% 97.5% ESS +#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 +#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 #> phi 4.006977632 0.0159088062 0.536273508 3.0263444882 5.15527365 1136 #> sd_level 0.057158663 0.0020138200 0.035366227 0.0083794202 0.14651419 308 #> sd_slope 0.003894013 0.0001752319 0.003654978 0.0004250207 0.01374575 435 -#> Temp 0.052808820 0.0002404538 0.008701489 0.0353736458 0.06992423 1310 -#> Wind -0.057351094 0.0004338213 0.015411504 -0.0873384757 -0.02700112 1262 #> SE_IS ESS_IS +#> 7.128104e-05 14485 +#> 1.263047e-04 13905 #> 4.411840e-03 14611 #> 2.927386e-04 10591 #> 3.031489e-05 7766 -#> 7.128104e-05 14485 -#> 1.263047e-04 13905 #> #> Summary for alpha_154: #> @@ -225,7 +225,7 @@ fit2 #> #> Run time: #> user system elapsed -#> 11.20 0.11 11.25 +#> 7.50 0.01 7.71 ``` Comparison: @@ -233,7 +233,7 @@ Comparison: ``` r pred2 <- fitted(fit2, model2) -bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") %>% +bind_rows(list(Gaussian = pred, Gamma = pred2), .id = "Model") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`, fill = Model), alpha = 0.25) + @@ -250,45 +250,32 @@ as predictor for ozone. As it contains few missing values, we cannot use it directly. As the number of missing time points is very small, simple imputation would likely be acceptable, but let’s consider more another approach. For simplicity, the slope terms of the previous models are now -omitted, and we focus on the Gaussian case. Let -![\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_t "\mu_t") -be the true solar radiation at time -![t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;t "t"). -Now for ozone -![O_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;O_t "O_t") -we assume following model: - -![O_t = D_t + \\alpha_t + \\beta_S \\mu_t + \\sigma\_\\epsilon \\epsilon_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;O_t%20%3D%20D_t%20%2B%20%5Calpha_t%20%2B%20%5Cbeta_S%20%5Cmu_t%20%2B%20%5Csigma_%5Cepsilon%20%5Cepsilon_t "O_t = D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t") -![\\alpha\_{t+1} = \\alpha_t + \\sigma\_\\eta\\eta_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_%7Bt%2B1%7D%20%3D%20%5Calpha_t%20%2B%20%5Csigma_%5Ceta%5Ceta_t "\alpha_{t+1} = \alpha_t + \sigma_\eta\eta_t") -![\\alpha_1 \\sim N(0, 100^2\\textrm{I})](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_1%20%5Csim%20N%280%2C%20100%5E2%5Ctextrm%7BI%7D%29 "\alpha_1 \sim N(0, 100^2\textrm{I})"), -wheere -![D_t = \\beta X_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;D_t%20%3D%20%5Cbeta%20X_t "D_t = \beta X_t") -contains regression terms related to wind and temperature, -![\\alpha_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Calpha_t "\alpha_t") -is the time varying intercept term, and -![\\beta_S](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cbeta_S "\beta_S") -is the effect of solar radiation -![\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_t "\mu_t"). - -Now for the observed solar radiation -![S_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t "S_t") -we assume - -![S_t = \\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t%20%3D%20%5Cmu_t "S_t = \mu_t") -![\\mu\_{t+1} = \\mu_t + \\sigma\_\\xi\\xi_t,](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_%7Bt%2B1%7D%20%3D%20%5Cmu_t%20%2B%20%5Csigma_%5Cxi%5Cxi_t%2C "\mu_{t+1} = \mu_t + \sigma_\xi\xi_t,") -![\\mu_1 \\sim N(0, 100^2)](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu_1%20%5Csim%20N%280%2C%20100%5E2%29 "\mu_1 \sim N(0, 100^2)"), -i.e. we assume as simple random walk for the -![\\mu](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Cmu "\mu") -which we observe without error or not at all (there is no error term in -the observation equation -![S_t=\\mu_t](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;S_t%3D%5Cmu_t "S_t=\mu_t")). +omitted, and we focus on the Gaussian case. Let $\mu_t$ be the true +solar radiation at time $t$. Now for ozone $O_t$ we assume following +model: + +$O_t = D_t + \alpha_t + \beta_S \mu_t + \sigma_\epsilon \epsilon_t$ +$\alpha_{t+1} = \alpha_t + \sigma_\eta\eta_t$ +$\alpha_1 \sim N(0, 100^2\textrm{I})$, +wheere $D_t = \beta X_t$ contains regression terms related to wind and +temperature, $\alpha_t$ is the time varying intercept term, and +$\beta_S$ is the effect of solar radiation $\mu_t$. + +Now for the observed solar radiation $S_t$ we assume + +$S_t = \mu_t$ +$\mu_{t+1} = \mu_t + \sigma_\xi\xi_t,$ +$\mu_1 \sim N(0, 100^2)$, +i.e. we assume as simple random walk for the $\mu$ which we observe +without error or not at all (there is no error term in the observation +equation $S_t=\mu_t$). We combine these two models as a bivariate Gaussian model with `ssm_mlg`: ``` r # predictors (not including solar radiation) for ozone -xreg <- airquality %>% select(Wind, Temp) %>% as.matrix() +xreg <- airquality |> select(Wind, Temp) |> as.matrix() # Function which outputs new model components given the parameter vector theta update_fn <- function(theta) { @@ -336,7 +323,7 @@ fit #> theta_2 0.98712126 0.0051506907 0.18819758 0.5917823 1.3475147 1335 #> theta_3 0.06324657 0.0004672314 0.02417334 0.0141425 0.1100184 2677 #> theta_4 0.82577262 0.0165661049 0.67134723 -0.6840637 1.9160168 1642 -#> theta_5 4.75567622 0.0010887250 0.05858454 4.6446809 4.8704036 2895 +#> theta_5 4.75567621 0.0010887250 0.05858454 4.6446809 4.8704036 2895 #> theta_6 3.05462451 0.0014803971 0.07640392 2.9032635 3.2028023 2664 #> #> Summary for alpha_154: @@ -347,7 +334,7 @@ fit #> #> Run time: #> user system elapsed -#> 12.26 0.12 12.30 +#> 7.41 0.11 7.83 ``` Draw predictions: @@ -356,9 +343,9 @@ Draw predictions: pred <- fitted(fit, model) obs <- data.frame(Time = 1:nrow(airquality), - Solar = airquality$Solar.R) %>% filter(!is.na(Solar)) + Solar = airquality$Solar.R) |> filter(!is.na(Solar)) -pred %>% filter(Variable == "Solar") %>% +pred |> filter(Variable == "Solar") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + @@ -372,10 +359,11 @@ pred %>% filter(Variable == "Solar") %>% ``` r + obs <- data.frame(Time = 1:nrow(airquality), - Ozone = airquality$Ozone) %>% filter(!is.na(Ozone)) + Ozone = airquality$Ozone) |> filter(!is.na(Ozone)) -pred %>% filter(Variable == "Ozone") %>% +pred |> filter(Variable == "Ozone") |> ggplot(aes(x = Time, y = Mean)) + geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), alpha = 0.5, fill = "steelblue") + diff --git a/benchmarks/replications.Rmd b/benchmarks/replications.Rmd index d2b204e7..3d785225 100644 --- a/benchmarks/replications.Rmd +++ b/benchmarks/replications.Rmd @@ -80,7 +80,7 @@ for(i in seq(1, nrow(d), by = 4)) { Results: ```{r} library(dplyr) -d %>% +d |> arrange(local_approx, variable, mcmc_type, sampling_method) ``` diff --git a/man/bsm_ng.Rd b/man/bsm_ng.Rd index be855f0a..d8459765 100644 --- a/man/bsm_ng.Rd +++ b/man/bsm_ng.Rd @@ -131,8 +131,8 @@ ggplot(as.data.frame(theta[,1:2]), aes(x = sd_level, y = sd_seasonal)) + # Traceplot using as.data.frame method for MCMC output library("dplyr") -as.data.frame(mcmc_out) \%>\% - filter(variable == "sd_level") \%>\% +as.data.frame(mcmc_out) |> + filter(variable == "sd_level") |> ggplot(aes(y = value, x = iter)) + geom_line() } diff --git a/man/post_correct.Rd b/man/post_correct.Rd index f6c3a2cd..a382b106 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -88,34 +88,34 @@ summary(out_is2, return_se = TRUE) # latent state library("dplyr") library("ggplot2") -state_approx <- as.data.frame(out_approx, variable = "states") \%>\% - group_by(time) \%>\% +state_approx <- as.data.frame(out_approx, variable = "states") |> + group_by(time) |> summarise(mean = mean(value)) -state_exact <- as.data.frame(out_is2, variable = "states") \%>\% - group_by(time) \%>\% +state_exact <- as.data.frame(out_is2, variable = "states") |> + group_by(time) |> summarise(mean = weighted.mean(value, weight)) dplyr::bind_rows(approx = state_approx, - exact = state_exact, .id = "method") \%>\% - filter(time > 200) \%>\% + exact = state_exact, .id = "method") |> + filter(time > 200) |> ggplot(aes(time, mean, colour = method)) + geom_line() + theme_bw() # posterior means p_approx <- predict(out_approx, model, type = "mean", - nsim = 1000, future = FALSE) \%>\% - group_by(time) \%>\% + nsim = 1000, future = FALSE) |> + group_by(time) |> summarise(mean = mean(value)) p_exact <- predict(out_is2, model, type = "mean", - nsim = 1000, future = FALSE) \%>\% - group_by(time) \%>\% + nsim = 1000, future = FALSE) |> + group_by(time) |> summarise(mean = mean(value)) dplyr::bind_rows(approx = p_approx, - exact = p_exact, .id = "method") \%>\% - filter(time > 200) \%>\% + exact = p_exact, .id = "method") |> + filter(time > 200) |> ggplot(aes(time, mean, colour = method)) + geom_line() + theme_bw() diff --git a/man/predict.mcmc_output.Rd b/man/predict.mcmc_output.Rd index acb9f8ad..606a6076 100644 --- a/man/predict.mcmc_output.Rd +++ b/man/predict.mcmc_output.Rd @@ -74,35 +74,35 @@ pred <- predict(mcmc_results, model = future_model, type = "state", nsim = 1000) library("dplyr") -sumr_fit <- as.data.frame(mcmc_results, variable = "states") \%>\% - group_by(time, iter) \%>\% +sumr_fit <- as.data.frame(mcmc_results, variable = "states") |> + group_by(time, iter) |> mutate(signal = value[variable == "level"] + - value[variable == "seasonal_1"]) \%>\% - group_by(time) \%>\% + value[variable == "seasonal_1"]) |> + group_by(time) |> summarise(mean = mean(signal), lwr = quantile(signal, 0.025), upr = quantile(signal, 0.975)) -sumr_pred <- pred \%>\% - group_by(time, sample) \%>\% +sumr_pred <- pred |> + group_by(time, sample) |> mutate(signal = value[variable == "level"] + - value[variable == "seasonal_1"]) \%>\% - group_by(time) \%>\% + value[variable == "seasonal_1"]) |> + group_by(time) |> summarise(mean = mean(signal), lwr = quantile(signal, 0.025), upr = quantile(signal, 0.975)) # If we used type = "mean", we could do -# sumr_pred <- pred \%>\% -# group_by(time) \%>\% +# sumr_pred <- pred |> +# group_by(time) |> # summarise(mean = mean(value), # lwr = quantile(value, 0.025), # upr = quantile(value, 0.975)) library("ggplot2") -rbind(sumr_fit, sumr_pred) \%>\% +rbind(sumr_fit, sumr_pred) |> ggplot(aes(x = time, y = mean)) + geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "#92f0a8", alpha = 0.25) + @@ -118,23 +118,23 @@ yrep <- predict(mcmc_results, model = model, type = "response", meanrep <- predict(mcmc_results, model = model, type = "mean", future = FALSE, nsim = 1000) -sumr_yrep <- yrep \%>\% - group_by(time) \%>\% +sumr_yrep <- yrep |> + group_by(time) |> summarise(earnings = mean(value), lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) \%>\% + upr = quantile(value, 0.975)) |> mutate(interval = "Observations") -sumr_meanrep <- meanrep \%>\% - group_by(time) \%>\% +sumr_meanrep <- meanrep |> + group_by(time) |> summarise(earnings = mean(value), lwr = quantile(value, 0.025), - upr = quantile(value, 0.975)) \%>\% + upr = quantile(value, 0.975)) |> mutate(interval = "Mean") -rbind(sumr_meanrep, sumr_yrep) \%>\% +rbind(sumr_meanrep, sumr_yrep) |> mutate(interval = - factor(interval, levels = c("Observations", "Mean"))) \%>\% + factor(interval, levels = c("Observations", "Mean"))) |> ggplot(aes(x = time, y = earnings)) + geom_ribbon(aes(ymin = lwr, ymax = upr, fill = interval), alpha = 0.75) + diff --git a/man/run_mcmc.Rd b/man/run_mcmc.Rd index 2ff583c8..960cf687 100644 --- a/man/run_mcmc.Rd +++ b/man/run_mcmc.Rd @@ -299,9 +299,9 @@ library("dplyr") library("ggplot2") # compute summary statistics -level_sumr <- d_states \%>\% - filter(variable == "level") \%>\% - group_by(time) \%>\% +level_sumr <- d_states |> + filter(variable == "level") |> + group_by(time) |> summarise(mean = diagis::weighted_mean(value, weight), lwr = diagis::weighted_quantile(value, weight, 0.025), @@ -309,7 +309,7 @@ level_sumr <- d_states \%>\% 0.975)) # visualize -level_sumr \%>\% ggplot(aes(x = time, y = mean)) + +level_sumr |> ggplot(aes(x = time, y = mean)) + geom_line() + geom_line(aes(y = lwr), linetype = "dashed", na.rm = TRUE) + geom_line(aes(y = upr), linetype = "dashed", na.rm = TRUE) + @@ -350,8 +350,8 @@ model <- ssm_mng(y = y, Z = matrix(1,2,1), T = 1, # Note small number of iterations for CRAN checks out <- run_mcmc(model, iter = 4000, mcmc_type = "approx") -sumr <- as.data.frame(out, variable = "states") \%>\% - group_by(time) \%>\% mutate(value = exp(value)) \%>\% +sumr <- as.data.frame(out, variable = "states") |> + group_by(time) |> mutate(value = exp(value)) |> summarise(mean = mean(value), ymin = quantile(value, 0.05), ymax = quantile(value, 0.95)) ggplot(sumr, aes(time, mean)) + diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index f60ead6a..c236b7ec 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -179,9 +179,9 @@ ggplot(d, aes(x = value)) + ```{r trend, dev.args=list(pointsize = 10), fig.cap="Smoothed trend component with 95% intervals."} suppressMessages(library("dplyr")) d <- as.data.frame(mcmc_bsm, variable = "states") -level_fit <- d %>% - filter(variable == "level") %>% - group_by(time) %>% +level_fit <- d |> + filter(variable == "level") |> + group_by(time) |> summarise(consumption = mean(value), lwr = quantile(value, 0.025), upr = quantile(value, 0.975)) diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 4138d0d7..191dde35 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -167,17 +167,17 @@ d2 <- as.data.frame(mcmc_ekf, variable = "states") d1$method <- "is2-psi" d2$method <- "approx ekf" -r_summary <- rbind(d1, d2) %>% - filter(variable == "logit_r") %>% - group_by(time, method) %>% +r_summary <- rbind(d1, d2) |> + filter(variable == "logit_r") |> + group_by(time, method) |> summarise( mean = weighted_mean(plogis(value), weight), lwr = weighted_quantile(plogis(value), weight, 0.025), upr = weighted_quantile(plogis(value), weight, 0.975)) -p_summary <- rbind(d1, d2) %>% - filter(variable == "p") %>% - group_by(time, method) %>% +p_summary <- rbind(d1, d2) |> + filter(variable == "p") |> + group_by(time, method) |> summarise( mean = weighted_mean(value, weight), lwr = weighted_quantile(value, weight, 0.025), diff --git a/vignettes/psi_pf.Rmd b/vignettes/psi_pf.Rmd index 99c368a8..95c6cf88 100644 --- a/vignettes/psi_pf.Rmd +++ b/vignettes/psi_pf.Rmd @@ -271,10 +271,10 @@ IRE <- function(x, time) { mean((x - truth)^2) * mean(time) } truth <- reference["logLik"] -sumr <- results %>% group_by(method, N) %>% +sumr <- results|> group_by(method, N)|> summarise(mean = mean(logLik), SD = sd(logLik), IRE = IRE(logLik, time), time = mean(time)) -table1 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, +table1 <- sumr|> arrange(N)|> knitr::kable(digit = 4, caption = "Results for the log-likelihood estimates of the growth model. ") saveRDS(table1, file = "psi_pf_experiments/table1.rds") ``` @@ -285,11 +285,11 @@ readRDS("psi_pf_experiments/table1.rds") Similar table for the smoothed estimate of $p_1$ show again the superiority of the $\psi$-PF, with no clear differences between BSF and EKPF. ```{r alpha, echo = FALSE, eval = FALSE} truth <- reference["alpha_11"] -sumr <- results %>% group_by(method, N) %>% +sumr <- results|> group_by(method, N)|> summarise(mean = mean(alpha_11), SD = sd(alpha_11), IRE = IRE(alpha_11, time), time = mean(time)) -table2 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, +table2 <- sumr|> arrange(N)|> knitr::kable(digit = 4, caption = "Results for the p_1 estimates of the growth model. ") saveRDS(table2, file = "psi_pf_experiments/table2.rds") ``` @@ -415,10 +415,10 @@ Table 4 shows the means and standard deviations of the log-likelihood estimates ```{r loglik_ar, echo = FALSE, eval = FALSE} reference <- readRDS("psi_pf_experiments/ar_truth.rds") truth <- reference["logLik"] -sumr <- results %>% group_by(method, N) %>% +sumr <- results|> group_by(method, N)|> summarise(mean = mean(logLik), SD = sd(logLik), IRE = IRE(logLik, time), time = mean(time)) -table3 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, +table3 <- sumr|> arrange(N)|> knitr::kable(digit = 4, caption = "Results for the log-likelihood estimates of the AR model. ") saveRDS(table3, file = "psi_pf_experiments/table3.rds") ``` @@ -430,10 +430,10 @@ Although with fixed number of particles the $\psi$-APF produces smaller standard ```{r state1_ar, echo = FALSE, eval = FALSE} truth <- reference["alpha_1"] -sumr <- results %>% group_by(method, N) %>% +sumr <- results|> group_by(method, N)|> summarise(mean = mean(alpha_1), SD = sd(alpha_1), IRE = IRE(alpha_1, time)) -table4 <- sumr %>% arrange(N) %>% knitr::kable(digit = 4, +table4 <- sumr|> arrange(N)|> knitr::kable(digit = 4, caption = "Results for the alpha_1 estimates of the AR model. ") saveRDS(table4, file = "psi_pf_experiments/table4.rds") ``` diff --git a/vignettes/sde_model.Rmd b/vignettes/sde_model.Rmd index a8a9b6a0..de8fd670 100644 --- a/vignettes/sde_model.Rmd +++ b/vignettes/sde_model.Rmd @@ -86,8 +86,8 @@ suppressMessages(library("diagis")) d <- as.data.frame(out, variable = "states") -state_fit <- d %>% - group_by(time) %>% +state_fit <- d |> + group_by(time) |> summarise(state = weighted_mean(value, weight), lwr = weighted_quantile(value, weight, 0.025), upr = weighted_quantile(value, weight, 0.975)) From 6eff0a8fe7a5ecc2d7120884022c98a0d6ac9bfe Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Mon, 16 Oct 2023 13:43:23 +0300 Subject: [PATCH 174/180] update metadata --- codemeta.json | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/codemeta.json b/codemeta.json index 3209680f..5ea2691d 100644 --- a/codemeta.json +++ b/codemeta.json @@ -7,13 +7,13 @@ "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "2.0.1.1", + "version": "2.0.2", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", "url": "https://r-project.org" }, - "runtimePlatform": "R version 4.1.2 (2021-11-01)", + "runtimePlatform": "R version 4.3.0 (2023-04-21 ucrt)", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", @@ -175,19 +175,19 @@ "@type": "SoftwareApplication", "identifier": "R", "name": "R", - "version": ">= 3.5.0" + "version": ">= 4.1.0" }, "2": { "@type": "SoftwareApplication", - "identifier": "magrittr", - "name": "magrittr", + "identifier": "bayesplot", + "name": "bayesplot", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=magrittr" + "sameAs": "https://CRAN.R-project.org/package=bayesplot" }, "3": { "@type": "SoftwareApplication", @@ -287,9 +287,9 @@ }, "sameAs": "https://CRAN.R-project.org/package=tidyr" }, - "SystemRequirements": "C++11, pandoc (>= 1.12.3, needed for vignettes)" + "SystemRequirements": "pandoc (>= 1.12.3, needed for vignettes)" }, - "fileSize": "11958.877KB", + "fileSize": "129081.465KB", "citation": [ { "@type": "ScholarlyArticle", @@ -359,7 +359,7 @@ } ], "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS.md", - "readme": "https://github.com/helske/bssm/blob/master/README.md", + "readme": "https://github.com/helske/bssm/blob/main/README.md", "contIntegration": ["https://github.com/helske/bssm/actions", "https://app.codecov.io/gh/helske/bssm?branch=master"], "developmentStatus": "https://www.repostatus.org/#active", "review": { From 3b5c6c1753dccb3045b7e2d750157d16a0d3c71c Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Tue, 24 Oct 2023 13:36:41 +0300 Subject: [PATCH 175/180] omp thread limit --- NEWS.md | 4 +++- R/approx.R | 1 + R/bssm-package.R | 5 +++++ R/ekpf_filter.R | 1 + R/kfilter.R | 2 ++ R/models.R | 1 + R/post_correction.R | 1 + man/bssm.Rd | 1 + man/drownings.Rd | 1 + man/ekf.Rd | 1 + man/ekpf_filter.Rd | 1 + man/exchange.Rd | 1 + man/gaussian_approx.Rd | 1 + man/negbin_model.Rd | 1 + man/poisson_series.Rd | 1 + man/post_correct.Rd | 1 + man/ssm_ulg.Rd | 1 + man/ukf.Rd | 1 + tests/test_all.R | 2 ++ vignettes/bssm.Rmd | 1 + vignettes/growth_model.Rmd | 1 + vignettes/psi_pf.Rmd | 1 + vignettes/sde_model.Rmd | 1 + 23 files changed, 31 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 94b70dd4..8cc2c553 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -bssm 2.0.2 (Release date: 2023-09-04) +bssm 2.0.2 (Release date: 2023-10-18) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future changes in the package. @@ -11,6 +11,8 @@ bssm 2.0.2 (Release date: 2023-09-04) * Removed explicit C++ version requirement as required by new CRAN policies. * Removed `magrittr` dependency and switched to native pipe, leading to requirement for R 4.1.0+. + * Added Sys.setenv("OMP_THREAD_LIMIT" = 2) to fix weird CRAN issues with + parallelisation on Debian. bssm 2.0.1 (Release date: 2022-05-02) ============== diff --git a/R/approx.R b/R/approx.R index d140ca91..80106827 100644 --- a/R/approx.R +++ b/R/approx.R @@ -31,6 +31,7 @@ #' @export #' @rdname gaussian_approx #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' data("poisson_series") #' model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, #' distribution = "poisson") diff --git a/R/bssm-package.R b/R/bssm-package.R index 4916aec3..fb0515dd 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -89,6 +89,7 @@ #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' # Create a local level model (latent random walk + noise) to the Nile #' # dataset using the bsm_lg function: #' model <- bsm_lg(Nile, @@ -138,6 +139,7 @@ NULL #' \url{https://stat.fi/tup/tilastotietokannat/index_en.html}. #' @keywords datasets #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' data("drownings") #' model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], #' xreg = drownings[, "summer_temp"], distribution = "poisson", @@ -165,6 +167,7 @@ NULL #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), #' sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) @@ -188,6 +191,7 @@ NULL #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' # The data was generated as follows: #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) @@ -241,6 +245,7 @@ NULL #' https://doi.org/10.32614/RJ-2021-103 #' #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' # reproducing the model: #' data("negbin_series") #' # Construct model for bssm diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 654f1ebc..4c79e756 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,6 +21,7 @@ ekpf_filter <- function(model, particles, ...) { #' @rdname ekpf_filter #' @examples #' \donttest{ # Takes a while +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) diff --git a/R/kfilter.R b/R/kfilter.R index ed28b8ff..3ab258fc 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -69,6 +69,7 @@ kfilter.nongaussian <- function(model, ...) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -138,6 +139,7 @@ ekf <- function(model, iekf_iter = 0) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 diff --git a/R/models.R b/R/models.R index 22956edd..fa0f6da1 100644 --- a/R/models.R +++ b/R/models.R @@ -61,6 +61,7 @@ default_update_fn <- function(theta) { #' @return An object of class \code{ssm_ulg}. #' @export #' @examples +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' #' # Regression model with time-varying coefficients #' set.seed(1) diff --git a/R/post_correction.R b/R/post_correction.R index 3e060a15..813c785e 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -174,6 +174,7 @@ suggest_N <- function(model, theta, #' @export #' @examples #' \donttest{ +#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/bssm.Rd b/man/bssm.Rd index 09d3377c..e578258a 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -64,6 +64,7 @@ statistics, and further diagnostics checks can be performed with the help of the \code{posterior} and \code{coda} packages. } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN # Create a local level model (latent random walk + noise) to the Nile # dataset using the bsm_lg function: model <- bsm_lg(Nile, diff --git a/man/drownings.Rd b/man/drownings.Rd index 93dacc08..ebc26a1a 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -19,6 +19,7 @@ unweighted average of three weather stations: Helsinki (Southern Finland), Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN data("drownings") model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], xreg = drownings[, "summer_temp"], distribution = "poisson", diff --git a/man/ekf.Rd b/man/ekf.Rd index 342de6be..c215ed81 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -27,6 +27,7 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 65432d22..86217633 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -37,6 +37,7 @@ with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ \donttest{ # Takes a while +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN set.seed(1) n <- 50 x <- y <- numeric(n) diff --git a/man/exchange.Rd b/man/exchange.Rd index 97687b7f..6e02533c 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -16,6 +16,7 @@ Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and Koopman (2012). } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN data("exchange") model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 88325b77..d762410c 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -43,6 +43,7 @@ This function is rarely needed itself, and is mainly available for testing and debugging purposes. } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN data("poisson_series") model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, distribution = "poisson") diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd index b95a0928..f41c9305 100644 --- a/man/negbin_model.Rd +++ b/man/negbin_model.Rd @@ -13,6 +13,7 @@ iterations. Here only 2000 iterations were used in order to reduce the size of the model object in CRAN. } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN # reproducing the model: data("negbin_series") # Construct model for bssm diff --git a/man/poisson_series.Rd b/man/poisson_series.Rd index 99187048..3e20cc2c 100644 --- a/man/poisson_series.Rd +++ b/man/poisson_series.Rd @@ -12,6 +12,7 @@ See example for code for reproducing the data. This was used in Vihola, Helske, Franks (2020). } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN # The data was generated as follows: set.seed(321) slope <- cumsum(c(0, rnorm(99, sd = 0.01))) diff --git a/man/post_correct.Rd b/man/post_correct.Rd index a382b106..ac7dad5c 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -52,6 +52,7 @@ weighted posterior, and returns updated MCMC output where components } \examples{ \donttest{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index 932a0c80..b6edba09 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -99,6 +99,7 @@ and then check the expected structure of the model components from the output. } \examples{ +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN # Regression model with time-varying coefficients set.seed(1) diff --git a/man/ukf.Rd b/man/ukf.Rd index e7344740..8c7b3d15 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -32,6 +32,7 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 diff --git a/tests/test_all.R b/tests/test_all.R index cfc7d472..c2be8f21 100644 --- a/tests/test_all.R +++ b/tests/test_all.R @@ -1,2 +1,4 @@ +Sys.setenv("OMP_THREAD_LIMIT" = 2) + library("testthat") test_check("bssm") diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index c236b7ec..11eff8ce 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -14,6 +14,7 @@ vignette: | --- ```{r, echo = FALSE} +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version", diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 191dde35..218a3755 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -14,6 +14,7 @@ vignette: | --- ```{r, echo = FALSE} +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") diff --git a/vignettes/psi_pf.Rmd b/vignettes/psi_pf.Rmd index 95c6cf88..de5a61f9 100644 --- a/vignettes/psi_pf.Rmd +++ b/vignettes/psi_pf.Rmd @@ -16,6 +16,7 @@ bvignette: | ``` ```{r, echo = FALSE} +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") diff --git a/vignettes/sde_model.Rmd b/vignettes/sde_model.Rmd index de8fd670..86a5d03a 100644 --- a/vignettes/sde_model.Rmd +++ b/vignettes/sde_model.Rmd @@ -14,6 +14,7 @@ vignette: | --- ```{r, echo = FALSE} +Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") From 232b2fe37f94c29c05ddc11580dda795c02b210d Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Wed, 25 Oct 2023 14:18:33 +0300 Subject: [PATCH 176/180] change OMP env variable --- NEWS.md | 2 +- R/approx.R | 2 +- R/bssm-package.R | 10 +++++----- R/ekpf_filter.R | 2 +- R/kfilter.R | 4 ++-- R/models.R | 2 +- R/post_correction.R | 2 +- man/bssm.Rd | 2 +- man/drownings.Rd | 2 +- man/ekf.Rd | 2 +- man/ekpf_filter.Rd | 2 +- man/exchange.Rd | 2 +- man/gaussian_approx.Rd | 2 +- man/negbin_model.Rd | 2 +- man/poisson_series.Rd | 2 +- man/post_correct.Rd | 2 +- man/ssm_ulg.Rd | 2 +- man/ukf.Rd | 2 +- tests/test_all.R | 2 +- vignettes/bssm.Rmd | 2 +- vignettes/growth_model.Rmd | 2 +- vignettes/psi_pf.Rmd | 2 +- vignettes/sde_model.Rmd | 2 +- 23 files changed, 28 insertions(+), 28 deletions(-) diff --git a/NEWS.md b/NEWS.md index 8cc2c553..458d214f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -11,7 +11,7 @@ bssm 2.0.2 (Release date: 2023-10-18) * Removed explicit C++ version requirement as required by new CRAN policies. * Removed `magrittr` dependency and switched to native pipe, leading to requirement for R 4.1.0+. - * Added Sys.setenv("OMP_THREAD_LIMIT" = 2) to fix weird CRAN issues with + * Added Sys.setenv("OMP_NUM_THREADS" = 2) to fix weird CRAN issues with parallelisation on Debian. bssm 2.0.1 (Release date: 2022-05-02) diff --git a/R/approx.R b/R/approx.R index 80106827..0c6a940d 100644 --- a/R/approx.R +++ b/R/approx.R @@ -31,7 +31,7 @@ #' @export #' @rdname gaussian_approx #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' data("poisson_series") #' model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, #' distribution = "poisson") diff --git a/R/bssm-package.R b/R/bssm-package.R index fb0515dd..27504a4d 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -89,7 +89,7 @@ #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # Create a local level model (latent random walk + noise) to the Nile #' # dataset using the bsm_lg function: #' model <- bsm_lg(Nile, @@ -139,7 +139,7 @@ NULL #' \url{https://stat.fi/tup/tilastotietokannat/index_en.html}. #' @keywords datasets #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' data("drownings") #' model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], #' xreg = drownings[, "summer_temp"], distribution = "poisson", @@ -167,7 +167,7 @@ NULL #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), #' sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) @@ -191,7 +191,7 @@ NULL #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # The data was generated as follows: #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) @@ -245,7 +245,7 @@ NULL #' https://doi.org/10.32614/RJ-2021-103 #' #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # reproducing the model: #' data("negbin_series") #' # Construct model for bssm diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index 4c79e756..a3484968 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,7 +21,7 @@ ekpf_filter <- function(model, particles, ...) { #' @rdname ekpf_filter #' @examples #' \donttest{ # Takes a while -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) diff --git a/R/kfilter.R b/R/kfilter.R index 3ab258fc..4316ec2b 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -69,7 +69,7 @@ kfilter.nongaussian <- function(model, ...) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -139,7 +139,7 @@ ekf <- function(model, iekf_iter = 0) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 diff --git a/R/models.R b/R/models.R index fa0f6da1..85c69f3b 100644 --- a/R/models.R +++ b/R/models.R @@ -61,7 +61,7 @@ default_update_fn <- function(theta) { #' @return An object of class \code{ssm_ulg}. #' @export #' @examples -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' #' # Regression model with time-varying coefficients #' set.seed(1) diff --git a/R/post_correction.R b/R/post_correction.R index 813c785e..7455fe60 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -174,7 +174,7 @@ suggest_N <- function(model, theta, #' @export #' @examples #' \donttest{ -#' Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/bssm.Rd b/man/bssm.Rd index e578258a..9aec2167 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -64,7 +64,7 @@ statistics, and further diagnostics checks can be performed with the help of the \code{posterior} and \code{coda} packages. } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # Create a local level model (latent random walk + noise) to the Nile # dataset using the bsm_lg function: model <- bsm_lg(Nile, diff --git a/man/drownings.Rd b/man/drownings.Rd index ebc26a1a..cdfb618b 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -19,7 +19,7 @@ unweighted average of three weather stations: Helsinki (Southern Finland), Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN data("drownings") model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], xreg = drownings[, "summer_temp"], distribution = "poisson", diff --git a/man/ekf.Rd b/man/ekf.Rd index c215ed81..80142cdd 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -27,7 +27,7 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 86217633..7ff169f1 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -37,7 +37,7 @@ with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ \donttest{ # Takes a while -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) n <- 50 x <- y <- numeric(n) diff --git a/man/exchange.Rd b/man/exchange.Rd index 6e02533c..3e47d815 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -16,7 +16,7 @@ Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and Koopman (2012). } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN data("exchange") model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index d762410c..222cae82 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -43,7 +43,7 @@ This function is rarely needed itself, and is mainly available for testing and debugging purposes. } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN data("poisson_series") model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, distribution = "poisson") diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd index f41c9305..64abb17e 100644 --- a/man/negbin_model.Rd +++ b/man/negbin_model.Rd @@ -13,7 +13,7 @@ iterations. Here only 2000 iterations were used in order to reduce the size of the model object in CRAN. } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # reproducing the model: data("negbin_series") # Construct model for bssm diff --git a/man/poisson_series.Rd b/man/poisson_series.Rd index 3e20cc2c..caa85da2 100644 --- a/man/poisson_series.Rd +++ b/man/poisson_series.Rd @@ -12,7 +12,7 @@ See example for code for reproducing the data. This was used in Vihola, Helske, Franks (2020). } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # The data was generated as follows: set.seed(321) slope <- cumsum(c(0, rnorm(99, sd = 0.01))) diff --git a/man/post_correct.Rd b/man/post_correct.Rd index ac7dad5c..579be6aa 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -52,7 +52,7 @@ weighted posterior, and returns updated MCMC output where components } \examples{ \donttest{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index b6edba09..d5907165 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -99,7 +99,7 @@ and then check the expected structure of the model components from the output. } \examples{ -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # Regression model with time-varying coefficients set.seed(1) diff --git a/man/ukf.Rd b/man/ukf.Rd index 8c7b3d15..aa62875d 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -32,7 +32,7 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 diff --git a/tests/test_all.R b/tests/test_all.R index c2be8f21..e93928f0 100644 --- a/tests/test_all.R +++ b/tests/test_all.R @@ -1,4 +1,4 @@ -Sys.setenv("OMP_THREAD_LIMIT" = 2) +Sys.setenv("OMP_NUM_THREADS" = 2) library("testthat") test_check("bssm") diff --git a/vignettes/bssm.Rmd b/vignettes/bssm.Rmd index 11eff8ce..c14c068b 100644 --- a/vignettes/bssm.Rmd +++ b/vignettes/bssm.Rmd @@ -14,7 +14,7 @@ vignette: | --- ```{r, echo = FALSE} -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version", diff --git a/vignettes/growth_model.Rmd b/vignettes/growth_model.Rmd index 218a3755..2120303f 100644 --- a/vignettes/growth_model.Rmd +++ b/vignettes/growth_model.Rmd @@ -14,7 +14,7 @@ vignette: | --- ```{r, echo = FALSE} -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") diff --git a/vignettes/psi_pf.Rmd b/vignettes/psi_pf.Rmd index de5a61f9..e5cfc46c 100644 --- a/vignettes/psi_pf.Rmd +++ b/vignettes/psi_pf.Rmd @@ -16,7 +16,7 @@ bvignette: | ``` ```{r, echo = FALSE} -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") diff --git a/vignettes/sde_model.Rmd b/vignettes/sde_model.Rmd index 86a5d03a..2fd74d7b 100644 --- a/vignettes/sde_model.Rmd +++ b/vignettes/sde_model.Rmd @@ -14,7 +14,7 @@ vignette: | --- ```{r, echo = FALSE} -Sys.setenv("OMP_THREAD_LIMIT" = 2) # For CRAN +Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN if (!requireNamespace("rmarkdown") || !rmarkdown::pandoc_available("1.12.3")) { warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc version 1.12.3. These were not found. Older versions will not work.") From a317fb772fbc1acc9e55976cf5da6202c9ff7dd1 Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Fri, 27 Oct 2023 16:00:27 +0300 Subject: [PATCH 177/180] dont run exchange example --- NEWS.md | 2 +- R/approx.R | 1 - R/bssm-package.R | 7 ++----- R/ekpf_filter.R | 1 - R/kfilter.R | 2 -- R/models.R | 1 - R/post_correction.R | 1 - man/bssm.Rd | 1 - man/drownings.Rd | 1 - man/ekf.Rd | 1 - man/ekpf_filter.Rd | 1 - man/exchange.Rd | 3 ++- man/gaussian_approx.Rd | 1 - man/negbin_model.Rd | 1 - man/poisson_series.Rd | 1 - man/post_correct.Rd | 1 - man/ssm_ulg.Rd | 1 - man/ukf.Rd | 1 - 18 files changed, 5 insertions(+), 23 deletions(-) diff --git a/NEWS.md b/NEWS.md index 458d214f..12601f0e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -11,7 +11,7 @@ bssm 2.0.2 (Release date: 2023-10-18) * Removed explicit C++ version requirement as required by new CRAN policies. * Removed `magrittr` dependency and switched to native pipe, leading to requirement for R 4.1.0+. - * Added Sys.setenv("OMP_NUM_THREADS" = 2) to fix weird CRAN issues with + * Added Sys.setenv("OMP_NUM_THREADS" = 2) to (partially) fix CRAN issues with parallelisation on Debian. bssm 2.0.1 (Release date: 2022-05-02) diff --git a/R/approx.R b/R/approx.R index 0c6a940d..d140ca91 100644 --- a/R/approx.R +++ b/R/approx.R @@ -31,7 +31,6 @@ #' @export #' @rdname gaussian_approx #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' data("poisson_series") #' model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, #' distribution = "poisson") diff --git a/R/bssm-package.R b/R/bssm-package.R index 27504a4d..4478dc0a 100644 --- a/R/bssm-package.R +++ b/R/bssm-package.R @@ -89,7 +89,6 @@ #' time ts ts.union tsp tsp<- sd na.omit #' @useDynLib bssm #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # Create a local level model (latent random walk + noise) to the Nile #' # dataset using the bsm_lg function: #' model <- bsm_lg(Nile, @@ -139,7 +138,6 @@ NULL #' \url{https://stat.fi/tup/tilastotietokannat/index_en.html}. #' @keywords datasets #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' data("drownings") #' model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], #' xreg = drownings[, "summer_temp"], distribution = "poisson", @@ -167,13 +165,14 @@ NULL #' Time Series Analysis by State Space Methods. Oxford University Press. #' https://doi.org/10.1093/acprof:oso/9780199641178.001.0001 #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN +#' \donttest{ # Don't test on CRAN as complains about parallelisation #' data("exchange") #' model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), #' sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) #' #' out <- particle_smoother(model, particles = 500) #' plot.ts(cbind(model$y, exp(out$alphahat))) +#' } NULL #' Simulated Poisson Time Series Data #' @@ -191,7 +190,6 @@ NULL #' Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492 #' #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # The data was generated as follows: #' set.seed(321) #' slope <- cumsum(c(0, rnorm(99, sd = 0.01))) @@ -245,7 +243,6 @@ NULL #' https://doi.org/10.32614/RJ-2021-103 #' #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' # reproducing the model: #' data("negbin_series") #' # Construct model for bssm diff --git a/R/ekpf_filter.R b/R/ekpf_filter.R index a3484968..654f1ebc 100644 --- a/R/ekpf_filter.R +++ b/R/ekpf_filter.R @@ -21,7 +21,6 @@ ekpf_filter <- function(model, particles, ...) { #' @rdname ekpf_filter #' @examples #' \donttest{ # Takes a while -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' n <- 50 #' x <- y <- numeric(n) diff --git a/R/kfilter.R b/R/kfilter.R index 4316ec2b..ed28b8ff 100644 --- a/R/kfilter.R +++ b/R/kfilter.R @@ -69,7 +69,6 @@ kfilter.nongaussian <- function(model, ...) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 @@ -139,7 +138,6 @@ ekf <- function(model, iekf_iter = 0) { #' @export #' @examples #' \donttest{ # Takes a while on CRAN -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' mu <- -0.2 #' rho <- 0.7 diff --git a/R/models.R b/R/models.R index 85c69f3b..22956edd 100644 --- a/R/models.R +++ b/R/models.R @@ -61,7 +61,6 @@ default_update_fn <- function(theta) { #' @return An object of class \code{ssm_ulg}. #' @export #' @examples -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' #' # Regression model with time-varying coefficients #' set.seed(1) diff --git a/R/post_correction.R b/R/post_correction.R index 7455fe60..3e060a15 100644 --- a/R/post_correction.R +++ b/R/post_correction.R @@ -174,7 +174,6 @@ suggest_N <- function(model, theta, #' @export #' @examples #' \donttest{ -#' Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN #' set.seed(1) #' n <- 300 #' x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/bssm.Rd b/man/bssm.Rd index 9aec2167..09d3377c 100644 --- a/man/bssm.Rd +++ b/man/bssm.Rd @@ -64,7 +64,6 @@ statistics, and further diagnostics checks can be performed with the help of the \code{posterior} and \code{coda} packages. } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # Create a local level model (latent random walk + noise) to the Nile # dataset using the bsm_lg function: model <- bsm_lg(Nile, diff --git a/man/drownings.Rd b/man/drownings.Rd index cdfb618b..93dacc08 100644 --- a/man/drownings.Rd +++ b/man/drownings.Rd @@ -19,7 +19,6 @@ unweighted average of three weather stations: Helsinki (Southern Finland), Jyvaskyla (Central Finland), and Sodankyla (Northern Finland). } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN data("drownings") model <- bsm_ng(drownings[, "deaths"], u = drownings[, "population"], xreg = drownings[, "summer_temp"], distribution = "poisson", diff --git a/man/ekf.Rd b/man/ekf.Rd index 80142cdd..342de6be 100644 --- a/man/ekf.Rd +++ b/man/ekf.Rd @@ -27,7 +27,6 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 diff --git a/man/ekpf_filter.Rd b/man/ekpf_filter.Rd index 7ff169f1..65432d22 100644 --- a/man/ekpf_filter.Rd +++ b/man/ekpf_filter.Rd @@ -37,7 +37,6 @@ with stratification resampling, based on Van Der Merwe et al (2001). } \examples{ \donttest{ # Takes a while -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) n <- 50 x <- y <- numeric(n) diff --git a/man/exchange.Rd b/man/exchange.Rd index 3e47d815..87f1e1d5 100644 --- a/man/exchange.Rd +++ b/man/exchange.Rd @@ -16,7 +16,7 @@ Dataset containing daily log-returns from 1/10/81-28/6/85 as in Durbin and Koopman (2012). } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN +\donttest{ # Don't test on CRAN as complains about parallelisation data("exchange") model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), sd_ar = halfnormal(0.175, 2), mu = normal(-0.87, 0, 2)) @@ -24,6 +24,7 @@ model <- svm(exchange, rho = uniform(0.97,-0.999,0.999), out <- particle_smoother(model, particles = 500) plot.ts(cbind(model$y, exp(out$alphahat))) } +} \references{ James Durbin, Siem Jan Koopman (2012). Time Series Analysis by State Space Methods. Oxford University Press. diff --git a/man/gaussian_approx.Rd b/man/gaussian_approx.Rd index 222cae82..88325b77 100644 --- a/man/gaussian_approx.Rd +++ b/man/gaussian_approx.Rd @@ -43,7 +43,6 @@ This function is rarely needed itself, and is mainly available for testing and debugging purposes. } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN data("poisson_series") model <- bsm_ng(y = poisson_series, sd_slope = 0.01, sd_level = 0.1, distribution = "poisson") diff --git a/man/negbin_model.Rd b/man/negbin_model.Rd index 64abb17e..b95a0928 100644 --- a/man/negbin_model.Rd +++ b/man/negbin_model.Rd @@ -13,7 +13,6 @@ iterations. Here only 2000 iterations were used in order to reduce the size of the model object in CRAN. } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # reproducing the model: data("negbin_series") # Construct model for bssm diff --git a/man/poisson_series.Rd b/man/poisson_series.Rd index caa85da2..99187048 100644 --- a/man/poisson_series.Rd +++ b/man/poisson_series.Rd @@ -12,7 +12,6 @@ See example for code for reproducing the data. This was used in Vihola, Helske, Franks (2020). } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # The data was generated as follows: set.seed(321) slope <- cumsum(c(0, rnorm(99, sd = 0.01))) diff --git a/man/post_correct.Rd b/man/post_correct.Rd index 579be6aa..a382b106 100644 --- a/man/post_correct.Rd +++ b/man/post_correct.Rd @@ -52,7 +52,6 @@ weighted posterior, and returns updated MCMC output where components } \examples{ \donttest{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) n <- 300 x1 <- sin((2 * pi / 12) * 1:n) diff --git a/man/ssm_ulg.Rd b/man/ssm_ulg.Rd index d5907165..932a0c80 100644 --- a/man/ssm_ulg.Rd +++ b/man/ssm_ulg.Rd @@ -99,7 +99,6 @@ and then check the expected structure of the model components from the output. } \examples{ -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN # Regression model with time-varying coefficients set.seed(1) diff --git a/man/ukf.Rd b/man/ukf.Rd index aa62875d..e7344740 100644 --- a/man/ukf.Rd +++ b/man/ukf.Rd @@ -32,7 +32,6 @@ states \eqn{\alpha_t} given the data up to time \eqn{t}. } \examples{ \donttest{ # Takes a while on CRAN -Sys.setenv("OMP_NUM_THREADS" = 2) # For CRAN set.seed(1) mu <- -0.2 rho <- 0.7 From 813a94ba0abfa28877de1f2ebdec54f75c54344a Mon Sep 17 00:00:00 2001 From: Jouni Helske <jouni.helske@utu.fi> Date: Sun, 8 Sep 2024 13:46:21 +0300 Subject: [PATCH 178/180] reactivate codecov --- .github/workflows/test-coverage.yaml | 61 ++++++++++++++++++++++++++++ README.Rmd | 2 +- README.md | 2 +- 3 files changed, 63 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/test-coverage.yaml diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100644 index 00000000..98822609 --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,61 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: test-coverage.yaml + +permissions: read-all + +jobs: + test-coverage: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr, any::xml2 + needs: coverage + + - name: Test coverage + run: | + cov <- covr::package_coverage( + quiet = FALSE, + clean = FALSE, + install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package") + ) + covr::to_cobertura(cov) + shell: Rscript {0} + + - uses: codecov/codecov-action@v4 + with: + fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }} + file: ./cobertura.xml + plugin: noop + disable_search: true + token: ${{ secrets.CODECOV_TOKEN }} + + - name: Show testthat output + if: always() + run: | + ## -------------------------------------------------------------------- + find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + + - name: Upload test results + if: failure() + uses: actions/upload-artifact@v4 + with: + name: coverage-test-failures + path: ${{ runner.temp }}/package diff --git a/README.Rmd b/README.Rmd index efcde640..9a31b803 100644 --- a/README.Rmd +++ b/README.Rmd @@ -70,7 +70,7 @@ knitr::opts_chunk$set( [![Project Status: Active - The project has reached a stable, usable state and is being actively developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/489_status.svg)](https://github.com/ropensci/software-review/issues/489) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) -[![Codecov test coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) +[![Codecov test coverage](https://codecov.io/gh/helske/bssm/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm) [![CRAN version](http://www.r-pkg.org/badges/version/bssm)]( https://CRAN.R-project.org/package=bssm) [![downloads](https://cranlogs.r-pkg.org/badges/bssm)](https://cranlogs.r-pkg.org/badges/bssm) diff --git a/README.md b/README.md index cf245fd4..3416f0ef 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ developed](https://www.repostatus.org/badges/latest/active.svg)](https://www.rep Review](https://badges.ropensci.org/489_status.svg)](https://github.com/ropensci/software-review/issues/489) [![R-CMD-check](https://github.com/helske/bssm/workflows/R-CMD-check/badge.svg)](https://github.com/helske/bssm/actions) [![Codecov test -coverage](https://codecov.io/gh/helske/bssm/branch/master/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm?branch=master) +coverage](https://codecov.io/gh/helske/bssm/graph/badge.svg)](https://app.codecov.io/gh/helske/bssm) [![CRAN version](http://www.r-pkg.org/badges/version/bssm)](https://CRAN.R-project.org/package=bssm) [![downloads](https://cranlogs.r-pkg.org/badges/bssm)](https://cranlogs.r-pkg.org/badges/bssm) From 316c0770f639f14b3fb7de7db461be65b17b714a Mon Sep 17 00:00:00 2001 From: Jouni Helske <jvhels@utu.fi> Date: Wed, 24 Sep 2025 11:13:11 +0300 Subject: [PATCH 179/180] fix is_finite warnings from Armadillo --- DESCRIPTION | 2 +- NEWS.md | 4 ++++ src/R_approx.cpp | 2 +- src/R_psi.cpp | 2 +- src/approx_mcmc.cpp | 8 ++++---- src/mcmc.cpp | 8 ++++---- src/model_ssm_mng.cpp | 6 +++--- src/model_ssm_nlg.cpp | 6 +++--- src/model_ssm_sde.cpp | 4 ++-- src/model_ssm_ulg.cpp | 40 ++++++++++++++++++++-------------------- src/model_ssm_ung.cpp | 22 +++++++++++----------- 11 files changed, 54 insertions(+), 50 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8e2bd0f3..5e7eba75 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models -Version: 2.0.2 +Version: 2.0.3 Authors@R: c(person(given = "Jouni", family = "Helske", diff --git a/NEWS.md b/NEWS.md index 12601f0e..a34483ff 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +bssm 2.0.3 (Release date: 2025-09-24) +===================================== + * Syntax changes in C++ for finite value checks due to changes in Armadillo. + bssm 2.0.2 (Release date: 2023-10-18) ===================================== * Switched to markdown NEWS with a plan to be more clear about the future diff --git a/src/R_approx.cpp b/src/R_approx.cpp index 558a23a3..c55d693a 100644 --- a/src/R_approx.cpp +++ b/src/R_approx.cpp @@ -68,7 +68,7 @@ Rcpp::List gaussian_approx_model_nlg(const arma::mat& y, SEXP Z, SEXP H, time_varying, 1, iekf_iter, max_iter, conv_tol); model.approximate(); - if(!arma::is_finite(model.mode_estimate)) { + if(!model.mode_estimate.is_finite()) { Rcpp::warning("Approximation did not converge. "); } return Rcpp::List::create(Rcpp::Named("y") = model.approx_model.y, diff --git a/src/R_psi.cpp b/src/R_psi.cpp index cde08c74..d249dbba 100644 --- a/src/R_psi.cpp +++ b/src/R_psi.cpp @@ -198,7 +198,7 @@ Rcpp::List psi_smoother_nlg(const arma::mat& y, SEXP Z, SEXP H, unsigned n = model.n; model.approximate(); - if(!arma::is_finite(model.mode_estimate)) { + if(!model.mode_estimate.is_finite()) { Rcpp::warning("Approximation did not converge. "); } arma::cube alpha(m, n + 1, nsim, arma::fill::zeros); diff --git a/src/approx_mcmc.cpp b/src/approx_mcmc.cpp index ed96b096..91e573f1 100644 --- a/src/approx_mcmc.cpp +++ b/src/approx_mcmc.cpp @@ -117,7 +117,7 @@ void approx_mcmc::amcmc(T model, const unsigned int method, const bool end_ram, // compute the log[p(theta)] double logprior = model.log_prior_pdf(theta, prior_fn); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } // placeholders @@ -233,7 +233,7 @@ void approx_mcmc::amcmc(ssm_sde model, const unsigned int nsim, const bool end_r unsigned n = model.n; // compute the log[p(theta)] double logprior = model.log_prior_pdf(model.theta); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } @@ -273,7 +273,7 @@ void approx_mcmc::amcmc(ssm_sde model, const unsigned int nsim, const bool end_r // compute prior double logprior_prop = model.log_prior_pdf(theta_prop); - if (arma::is_finite(logprior_prop)) { + if (std::isfinite(logprior_prop)) { // update parameters model.theta = theta_prop; @@ -1527,7 +1527,7 @@ void approx_mcmc::ekf_mcmc(ssm_nlg model, const bool end_ram) { // compute the log-likelihood double loglik = model.ekf_loglik(); - if (!arma::is_finite(loglik)) { + if (!std::isfinite(loglik)) { Rcpp::stop("Initial approximate likelihood is not finite."); } double acceptance_prob = 0.0; diff --git a/src/mcmc.cpp b/src/mcmc.cpp index 074fa8ba..2848b6d8 100644 --- a/src/mcmc.cpp +++ b/src/mcmc.cpp @@ -350,7 +350,7 @@ void mcmc::pm_mcmc( model.update_model(theta, update_fn); // just in case // compute the log[p(theta)] double logprior = model.log_prior_pdf(theta, prior_fn); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } arma::cube alpha(m, n + 1, nsim); @@ -542,7 +542,7 @@ void mcmc::da_mcmc(T model, model.update_model(theta, update_fn); // just in case // compute the log[p(theta)] double logprior = model.log_prior_pdf(theta, prior_fn); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } arma::cube alpha(m, n + 1, nsim); @@ -698,7 +698,7 @@ void mcmc::pm_mcmc( model.update_model(theta); // just in case // compute the log[p(theta)] double logprior = model.log_prior_pdf(theta); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } arma::cube alpha(m, n + 1, nsim); @@ -845,7 +845,7 @@ void mcmc::da_mcmc(ssm_sde model, model.update_model(theta); // just in case // compute the log[p(theta)] double logprior = model.log_prior_pdf(theta); - if (!arma::is_finite(logprior)) { + if (!std::isfinite(logprior)) { Rcpp::stop("Initial prior probability is not finite."); } diff --git a/src/model_ssm_mng.cpp b/src/model_ssm_mng.cpp index 11d368a1..5265106e 100644 --- a/src/model_ssm_mng.cpp +++ b/src/model_ssm_mng.cpp @@ -221,7 +221,7 @@ void ssm_mng::update_scales() { for(unsigned int t = 0; t < n; t++) { for(unsigned int i = 0; i < p; i++) { - if (arma::is_finite(y(i, t))) { + if (std::isfinite(y(i, t))) { switch(distribution(i)) { case 0 : scales(t) += -0.5 * (mode_estimate(i, t) + @@ -361,7 +361,7 @@ arma::vec ssm_mng::log_weights(const unsigned int t, const arma::cube& alpha) c for (unsigned int i = 0; i < alpha.n_slices; i++) { arma::vec simsignal = D.col(t * Dtv) + Z.slice(t * Ztv) * alpha.slice(i).col(t); for(unsigned int j = 0; j < p; j++) { - if (arma::is_finite(y(j, t))) { + if (std::isfinite(y(j, t))) { switch(distribution(j)) { // case 0 : // weights(i) += -0.5 * (simsignal(j) + std::pow(y(j,t) / phi(j), 2.0) * @@ -412,7 +412,7 @@ arma::vec ssm_mng::log_obs_density(const unsigned int t, for (unsigned int i = 0; i < alpha.n_slices; i++) { arma::vec simsignal = D.col(t * Dtv) + Z.slice(t * Ztv) * alpha.slice(i).col(t); for(unsigned int j = 0; j < p; j++) { - if (arma::is_finite(y(j, t))) { + if (std::isfinite(y(j, t))) { switch(distribution(j)) { // case 0 : // weights(i) += -0.5 * (simsignal(j) + std::pow(y(j,t) / phi(j), 2.0) * diff --git a/src/model_ssm_nlg.cpp b/src/model_ssm_nlg.cpp index ed2fa81f..68b91b2c 100644 --- a/src/model_ssm_nlg.cpp +++ b/src/model_ssm_nlg.cpp @@ -66,7 +66,7 @@ void ssm_nlg::approximate() { // initial approximation is based on EKF (at and att) approximate_by_ekf(); mode_estimate = approx_model.fast_smoother().head_cols(n); - if (!arma::is_finite(mode_estimate)) { + if (!mode_estimate.is_finite()) { return; } double ll; @@ -112,7 +112,7 @@ void ssm_nlg::approximate() { double ll_new = log_signal_pdf(mode_estimate_new); abs_diff = ll_new - ll; rel_diff = abs_diff / std::abs(ll); - if (!arma::is_finite(mode_estimate_new) || !arma::is_finite(ll_new)) { + if (!mode_estimate_new.is_finite() || !std::isfinite(ll_new)) { mode_estimate.fill(std::numeric_limits<double>::infinity()); return; } @@ -131,7 +131,7 @@ void ssm_nlg::approximate() { abs_diff = ll_new - ll; rel_diff = abs_diff / std::abs(ll); ii++; - if (!arma::is_finite(mode_estimate) || !arma::is_finite(ll_new)) { + if (!mode_estimate.is_finite() || !std::isfinite(ll_new)) { mode_estimate.fill(std::numeric_limits<double>::infinity()); return; } diff --git a/src/model_ssm_sde.cpp b/src/model_ssm_sde.cpp index e7945bc6..1f97587b 100644 --- a/src/model_ssm_sde.cpp +++ b/src/model_ssm_sde.cpp @@ -31,7 +31,7 @@ double ssm_sde::bsf_filter(const unsigned int nsim, arma::vec normalized_weights(nsim); double loglik = 0.0; - if(arma::is_finite(y(0))) { + if (std::isfinite(y(0))) { weights.col(0) = log_obs_density(y(0), alpha.tube(0, 0), theta); double max_weight = weights.col(0).max(); weights.col(0) = arma::exp(weights.col(0) - max_weight); @@ -61,7 +61,7 @@ double ssm_sde::bsf_filter(const unsigned int nsim, drift, diffusion, ddiffusion, positive, coarse_engine); } - if ((t < (n - 1)) && arma::is_finite(y(t + 1))) { + if ((t < (n - 1)) && std::isfinite(y(t + 1))) { weights.col(t + 1) = log_obs_density(y(t + 1), alpha.tube(0, t + 1), theta); double max_weight = weights.col(t + 1).max(); diff --git a/src/model_ssm_ulg.cpp b/src/model_ssm_ulg.cpp index d0a62b41..d2a558fc 100644 --- a/src/model_ssm_ulg.cpp +++ b/src/model_ssm_ulg.cpp @@ -122,7 +122,7 @@ double ssm_ulg::log_likelihood() const { for (unsigned int t = 0; t < n; t++) { double F = arma::as_scalar(Z.col(t * Ztv).t() * Pt * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && F > zero_tol) { + if (std::isfinite(y_tmp(t)) && F > zero_tol) { double v = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at); arma::vec K = Pt * Z.col(t * Ztv) / F; at = C.col(t * Ctv) + T.slice(t * Ttv) * (at + K * v); @@ -171,7 +171,7 @@ arma::cube ssm_ulg::simulate_states(const unsigned int nsim, const bool use_anti } aplus.col(0) = a1 + L_P1 * um; for (unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { y(t) = xbeta(t) + D(t * Dtv) + arma::as_scalar(Z.col(t * Ztv).t() * aplus.col(t)) + H(t * Htv) * normal(engine); @@ -199,7 +199,7 @@ arma::cube ssm_ulg::simulate_states(const unsigned int nsim, const bool use_anti } aplus.col(0) = a1 + L_P1 * um; for (unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { y(t) = xbeta(t) + D(t * Dtv) + arma::as_scalar(Z.col(t * Ztv).t() * aplus.col(t)) + H(t * Htv) * normal(engine); @@ -227,7 +227,7 @@ arma::cube ssm_ulg::simulate_states(const unsigned int nsim, const bool use_anti } asim.slice(0).col(0) = L_P1 * um; for (unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { y(t) -= arma::as_scalar(Z.col(t * Ztv).t() * asim.slice(0).col(t)) + H(t * Htv) * normal(engine); } @@ -267,7 +267,7 @@ arma::mat ssm_ulg::fast_smoother() const { for (unsigned int t = 0; t < n; t++) { Ft(t) = arma::as_scalar(Z.col(t * Ztv).t() * Pt * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol) { + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol) { Kt.col(t) = Pt * Z.col(t * Ztv) / Ft(t); vt(t) = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); at.col(t + 1) = C.col(t * Ctv) + T.slice(t * Ttv) * (at.col(t) + Kt.col(t) * vt(t)); @@ -283,14 +283,14 @@ arma::mat ssm_ulg::fast_smoother() const { arma::mat rt(m, n); rt.col(n - 1).zeros(); for (int t = (n - 1); t > 0; t--) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol){ + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol){ arma::mat L = T.slice(t * Ttv) * (arma::eye(m, m) - Kt.col(t) * Z.col(t * Ztv).t()); rt.col(t - 1) = Z.col(t * Ztv) / Ft(t) * vt(t) + L.t() * rt.col(t); } else { rt.col(t - 1) = T.slice(t * Ttv).t() * rt.col(t); } } - if (arma::is_finite(y(0)) && Ft(0) > zero_tol){ + if (std::isfinite(y(0)) && Ft(0) > zero_tol){ arma::mat L = T.slice(0) * (arma::eye(m, m) - Kt.col(0) * Z.col(0).t()); at.col(0) = a1 + P1 * (Z.col(0) / Ft(0) * vt(0) + L.t() * rt.col(0)); } else { @@ -328,7 +328,7 @@ arma::mat ssm_ulg::fast_smoother(const arma::vec& Ft, const arma::mat& Kt, } for (unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol) { + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol) { vt(t) = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); at.col(t + 1) = C.col(t * Ctv) + T.slice(t * Ttv) * (at.col(t) + Kt.col(t) * vt(t)); } else { @@ -340,13 +340,13 @@ arma::mat ssm_ulg::fast_smoother(const arma::vec& Ft, const arma::mat& Kt, rt.col(n - 1).zeros(); for (int t = (n - 1); t > 0; t--) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol){ + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol){ rt.col(t - 1) = Z.col(t * Ztv) / Ft(t) * vt(t) + Lt.slice(t).t() * rt.col(t); } else { rt.col(t - 1) = T.slice(t * Ttv).t() * rt.col(t); } } - if (arma::is_finite(y(0)) && Ft(0) > zero_tol){ + if (std::isfinite(y(0)) && Ft(0) > zero_tol){ arma::mat L = T.slice(0) * (arma::eye(m, m) - Kt.col(0) * Z.col(0).t()); at.col(0) = a1 + P1 * (Z.col(0) / Ft(0) * vt(0) + L.t() * rt.col(0)); } else { @@ -376,7 +376,7 @@ arma::mat ssm_ulg::fast_precomputing_smoother(arma::vec& Ft, arma::mat& Kt, } for (unsigned int t = 0; t < n; t++) { Ft(t) = arma::as_scalar(Z.col(t * Ztv).t() * Pt * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol) { + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol) { Kt.col(t) = Pt * Z.col(t * Ztv) / Ft(t); vt(t) = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); at.col(t + 1) = C.col(t * Ctv) + T.slice(t * Ttv) * (at.col(t) + Kt.col(t) * vt(t)); @@ -394,14 +394,14 @@ arma::mat ssm_ulg::fast_precomputing_smoother(arma::vec& Ft, arma::mat& Kt, rt.col(n - 1).zeros(); for (int t = (n - 1); t > 0; t--) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol){ + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol){ Lt.slice(t) = T.slice(t * Ttv) * (arma::eye(m, m) - Kt.col(t) * Z.col(t * Ztv).t()); rt.col(t - 1) = Z.col(t * Ztv) / Ft(t) * vt(t) + Lt.slice(t).t() * rt.col(t); } else { rt.col(t - 1) = T.slice(t * Ttv).t() * rt.col(t); } } - if (arma::is_finite(y_tmp(0)) && Ft(0) > zero_tol){ + if (std::isfinite(y_tmp(0)) && Ft(0) > zero_tol){ arma::mat L = T.slice(0) * (arma::eye(m, m) - Kt.col(0) * Z.col(0).t()); at.col(0) = a1 + P1 * (Z.col(0) / Ft(0) * vt(0) + L.t() * rt.col(0)); } else { @@ -432,7 +432,7 @@ void ssm_ulg::smoother_ccov(arma::mat& at, arma::cube& Pt, arma::cube& ccov) con for (unsigned int t = 0; t < n; t++) { Ft(t) = arma::as_scalar(Z.col(t * Ztv).t() * Pt.slice(t) * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol) { + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol) { Kt.col(t) = Pt.slice(t) * Z.col(t * Ztv) / Ft(t); vt(t) = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); at.col(t + 1) = C.col(t * Ctv) + T.slice(t * Ttv) * (at.col(t) + Kt.col(t) * vt(t)); @@ -454,7 +454,7 @@ void ssm_ulg::smoother_ccov(arma::mat& at, arma::cube& Pt, arma::cube& ccov) con arma::mat Nt(m, m, arma::fill::zeros); for (int t = (n - 1); t >= 0; t--) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol){ + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol){ arma::mat L = T.slice(t * Ttv) * (arma::eye(m, m) - Kt.col(t) * Z.col(t * Ztv).t()); //P[t+1] stored to ccov_t ccov.slice(t) = Pt.slice(t) * L.t() * (arma::eye(m, m) - Nt * ccov.slice(t)); @@ -489,7 +489,7 @@ double ssm_ulg::filter(arma::mat& at, arma::mat& att, arma::cube& Pt, for (unsigned int t = 0; t < n; t++) { double F = arma::as_scalar(Z.col(t * Ztv).t() * Pt.slice(t) * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && F > zero_tol) { + if (std::isfinite(y_tmp(t)) && F > zero_tol) { double v = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); arma::vec K = Pt.slice(t) * Z.col(t * Ztv) / F; att.col(t) = at.col(t) + K * v; @@ -524,7 +524,7 @@ void ssm_ulg::smoother(arma::mat& at, arma::cube& Pt) const { for (unsigned int t = 0; t < n; t++) { Ft(t) = arma::as_scalar(Z.col(t * Ztv).t() * Pt.slice(t) * Z.col(t * Ztv) + HH(t * Htv)); - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol) { + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol) { Kt.col(t) = Pt.slice(t) * Z.col(t * Ztv) / Ft(t); vt(t) = arma::as_scalar(y_tmp(t) - D(t * Dtv) - Z.col(t * Ztv).t() * at.col(t)); at.col(t + 1) = C.col(t * Ctv) + T.slice(t * Ttv) * (at.col(t) + Kt.col(t) * vt(t)); @@ -541,7 +541,7 @@ void ssm_ulg::smoother(arma::mat& at, arma::cube& Pt) const { arma::mat Nt(m, m, arma::fill::zeros); for (int t = (n - 1); t >= 0; t--) { - if (arma::is_finite(y_tmp(t)) && Ft(t) > zero_tol){ + if (std::isfinite(y_tmp(t)) && Ft(t) > zero_tol){ arma::mat L = T.slice(t * Ttv) * (arma::eye(m, m) - Kt.col(t) * Z.col(t * Ztv).t()); rt = Z.col(t * Ztv) / Ft(t) * vt(t) + L.t() * rt; Nt = arma::symmatu(Z.col(t * Ztv) * Z.col(t * Ztv).t() / Ft(t) + L.t() * Nt * L); @@ -572,7 +572,7 @@ double ssm_ulg::bsf_filter(const unsigned int nsim, arma::cube& alpha, arma::vec normalized_weights(nsim); double loglik = 0.0; - if(arma::is_finite(y(0))) { + if(std::isfinite(y(0))) { for (unsigned int i = 0; i < nsim; i++) { double mu = arma::as_scalar(D(0) + Z.col(0).t() * @@ -616,7 +616,7 @@ double ssm_ulg::bsf_filter(const unsigned int nsim, arma::cube& alpha, T.slice(t * Ttv) * alphatmp.col(i) + R.slice(t * Rtv) * uk; } - if ((t < (n - 1)) && arma::is_finite(y(t + 1))) { + if ((t < (n - 1)) && std::isfinite(y(t + 1))) { for (unsigned int i = 0; i < nsim; i++) { double mu = arma::as_scalar(D((t + 1) * Dtv) + Z.col(Ztv * (t + 1)).t() * alpha.slice(i).col(t + 1)); diff --git a/src/model_ssm_ung.cpp b/src/model_ssm_ung.cpp index 882e221f..6bdc6b16 100644 --- a/src/model_ssm_ung.cpp +++ b/src/model_ssm_ung.cpp @@ -245,7 +245,7 @@ void ssm_ung::update_scales() { switch(distribution) { case 0 : for(unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { scales(t) = -0.5 * (mode_estimate(t) + std::pow(y(t) / phi, 2.0) * std::exp(-mode_estimate(t))) + 0.5 * std::pow((approx_model.y(t) - mode_estimate(t)) / approx_model.H(t), 2.0); @@ -254,7 +254,7 @@ void ssm_ung::update_scales() { break; case 1 : for(unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { scales(t) = y(t) * mode_estimate(t) - u(t) * std::exp(mode_estimate(t)) + 0.5 * std::pow((approx_model.y(t) - mode_estimate(t)) / approx_model.H(t), 2.0); @@ -263,7 +263,7 @@ void ssm_ung::update_scales() { break; case 2 : for(unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { scales(t) = y(t) * mode_estimate(t) - u(t) * std::log1p(std::exp(mode_estimate(t))) + 0.5 * std::pow((approx_model.y(t) - mode_estimate(t)) / approx_model.H(t), 2.0); @@ -272,7 +272,7 @@ void ssm_ung::update_scales() { break; case 3 : for(unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { scales(t) = y(t) * mode_estimate(t) - (y(t) + phi) * std::log(phi + u(t) * std::exp(mode_estimate(t))) + @@ -282,7 +282,7 @@ void ssm_ung::update_scales() { break; case 4 : for(unsigned int t = 0; t < n; t++) { - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { scales(t) = -phi * (mode_estimate(t) + (y(t) * exp(-mode_estimate(t)) / u(t))) + 0.5 * std::pow((approx_model.y(t) - mode_estimate(t)) / approx_model.H(t), 2.0); } @@ -388,7 +388,7 @@ arma::vec ssm_ung::log_weights( arma::vec weights(alpha.n_slices, arma::fill::zeros); - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { switch(distribution) { case 0 : for (unsigned int i = 0; i < alpha.n_slices; i++) { @@ -449,7 +449,7 @@ arma::vec ssm_ung::log_obs_density(const unsigned int t, arma::vec weights(alpha.n_slices, arma::fill::zeros); - if (arma::is_finite(y(t))) { + if (std::isfinite(y(t))) { switch(distribution) { case 0 : for (unsigned int i = 0; i < alpha.n_slices; i++) { @@ -546,7 +546,7 @@ double ssm_ung::psi_filter(const unsigned int nsim, arma::cube& alpha, std::uniform_real_distribution<> unif(0.0, 1.0); arma::vec normalized_weights(nsim); double loglik = 0.0; - if(arma::is_finite(y(0))) { + if(std::isfinite(y(0))) { weights.col(0) = log_weights(0, alpha) - scales(0); double max_weight = weights.col(0).max(); @@ -586,7 +586,7 @@ double ssm_ung::psi_filter(const unsigned int nsim, arma::cube& alpha, Ct.slice(t + 1) * (alphatmp.col(i) - alphahat.col(t)) + Vt.slice(t + 1) * um; } - if ((t < (n - 1)) && arma::is_finite(y(t + 1))) { + if ((t < (n - 1)) && std::isfinite(y(t + 1))) { weights.col(t + 1) = log_weights(t + 1, alpha) - scales(t + 1); double max_weight = weights.col(t + 1).max(); @@ -630,7 +630,7 @@ double ssm_ung::bsf_filter(const unsigned int nsim, arma::cube& alpha, arma::vec normalized_weights(nsim); double loglik = 0.0; - if(arma::is_finite(y(0))) { + if(std::isfinite(y(0))) { weights.col(0) = log_obs_density(0, alpha); double max_weight = weights.col(0).max(); weights.col(0) = arma::exp(weights.col(0) - max_weight); @@ -669,7 +669,7 @@ double ssm_ung::bsf_filter(const unsigned int nsim, arma::cube& alpha, T.slice(t * Ttv) * alphatmp.col(i) + R.slice(t * Rtv) * uk; } - if ((t < (n - 1)) && arma::is_finite(y(t + 1))) { + if ((t < (n - 1)) && std::isfinite(y(t + 1))) { weights.col(t + 1) = log_obs_density(t + 1, alpha); double max_weight = weights.col(t + 1).max(); From a02a1416f7d9e80eef026606c33ff87853c1af08 Mon Sep 17 00:00:00 2001 From: Jouni Helske <jvhels@utu.fi> Date: Wed, 24 Sep 2025 14:48:27 +0300 Subject: [PATCH 180/180] update codemeta, submit to cran --- codemeta.json | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/codemeta.json b/codemeta.json index 5ea2691d..148a62cd 100644 --- a/codemeta.json +++ b/codemeta.json @@ -7,13 +7,13 @@ "codeRepository": "https://github.com/helske/bssm", "issueTracker": "https://github.com/helske/bssm/issues", "license": "https://spdx.org/licenses/GPL-2.0", - "version": "2.0.2", + "version": "2.0.3", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", "url": "https://r-project.org" }, - "runtimePlatform": "R version 4.3.0 (2023-04-21 ucrt)", + "runtimePlatform": "R version 4.4.1 (2024-06-14 ucrt)", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", @@ -289,7 +289,7 @@ }, "SystemRequirements": "pandoc (>= 1.12.3, needed for vignettes)" }, - "fileSize": "129081.465KB", + "fileSize": "2386.647KB", "citation": [ { "@type": "ScholarlyArticle", @@ -358,15 +358,13 @@ } } ], - "releaseNotes": "https://github.com/helske/bssm/blob/master/NEWS.md", - "readme": "https://github.com/helske/bssm/blob/main/README.md", - "contIntegration": ["https://github.com/helske/bssm/actions", "https://app.codecov.io/gh/helske/bssm?branch=master"], + "releaseNotes": "https://github.com/helske/bssm/blob/main/NEWS.md", + "contIntegration": ["https://github.com/helske/bssm/actions", "https://app.codecov.io/gh/helske/bssm"], "developmentStatus": "https://www.repostatus.org/#active", "review": { "@type": "Review", "url": "https://github.com/ropensci/software-review/issues/489", "provider": "https://ropensci.org" }, - "keywords": ["bayesian-inference", "markov-chain-monte-carlo", "particle-filter", "time-series", "state-space", "r", "cpp"], "relatedLink": "https://CRAN.R-project.org/package=bssm" }