--- title: "bayesSSM" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{bayesSSM} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ggplot2} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6, fig.height = 4 ) ``` ```{r setup} library(bayesSSM) library(ggplot2) ``` We will show how to fit the following SSM model using the `bayesSSM` package: \begin{align*} X_1 &\sim N(0,\, 1) \\ X_t&=\phi X_{t-1}+\sin(X_{t-1})+\sigma_x V_t, \quad V_t \sim N(0, \, 1) \\ Y_t&=X_t+\sigma_y W_t, \quad W_t \sim N(0, \, 1), \end{align*} that is $X_t$ is a latent state and $Y_t$ is an observed value. The parameters of the model are $\phi$, $\sigma_x$, and $\sigma_y$. First, we will simulate some data from this model: ```{r} set.seed(1405) t_val <- 50 phi_true <- 0.8 sigma_x_true <- 1 sigma_y_true <- 0.5 x <- numeric(t_val) y <- numeric(t_val) x[1] <- rnorm(1) y[1] <- x[1] + sigma_y_true * rnorm(1) for (t in 2:t_val) { x[t] <- phi_true * x[t - 1] + sin(x[t - 1]) + sigma_x_true * rnorm(1) y[t] <- x[t] + sigma_y_true * rnorm(1) } ``` Let's visualize the data: ```{r} #| fig.alt: "The blue line represents the latent state, and red points represent #| observed values." ggplot() + geom_line(aes(x = 1:t_val, y = x), color = "blue", linewidth = 1) + # Latent geom_point(aes(x = 1:t_val, y = y), color = "red", size = 2) + # Observed labs( title = "Simulated Data: Latent State and Observations", x = "Time", y = "Value", caption = "Blue line: Latent state (x), Red points: Observed values (y)" ) + theme_minimal() ``` To fit the model using `pmmh` we need to specify the likelihood initialization, transition, and log-likelihood functions. It's important that they all take an argument `particles`, which is a vector of particles, and that the log-likelihood function takes an argument $y$ for the data. ```{r} init_fn <- function(particles) { rnorm(particles, mean = 0, sd = 1) } transition_fn <- function(particles, phi, sigma_x) { phi * particles + sin(particles) + rnorm(length(particles), mean = 0, sd = sigma_x) } log_likelihood_fn <- function(y, particles, sigma_y) { dnorm(y, mean = particles, sd = sigma_y, log = TRUE) } ``` Since we are interested in Bayesian inference, we need to specify the priors for our parameters. We will use a normal prior for $\phi$ and exponential priors for $\sigma_x$ and $\sigma_y$. `pmmh` needs the priors to be specified on the $\log$-scale and takes the priors as a list of functions. ```{r} log_prior_phi <- function(phi) { dunif(phi, min = 0, max = 1, log = TRUE) } log_prior_sigma_x <- function(sigma) { dexp(sigma, rate = 1, log = TRUE) } log_prior_sigma_y <- function(sigma) { dexp(sigma, rate = 1, log = TRUE) } log_priors <- list( phi = log_prior_phi, sigma_x = log_prior_sigma_x, sigma_y = log_prior_sigma_y ) ``` The `pmmh` function automatically tunes the number of particles and proposal distribution for the parameters. The tuning can be modified by the the function `default_tune_control`. We will use the default settings. We fit 2 chains with $m=1000$ iterations for each, with a burn_in of $500$. We also modify the tuning to only use a pilot run of $100$ iterations and $10$ burn-in iterations. In practice you should run more iterations and chains. To improve sampling we specify that proposals for $\sigma_x$ and $\sigma_y$ should be on the $\log$-scale. ```{r} result <- pmmh( y = y, m = 1000, init_fn = init_fn, transition_fn = transition_fn, log_likelihood_fn = log_likelihood_fn, log_priors = log_priors, pilot_init_params = list( c(phi = 0.4, sigma_x = 0.4, sigma_y = 0.4), c(phi = 0.8, sigma_x = 0.8, sigma_y = 0.8) ), burn_in = 500, num_chains = 2, seed = 1405, param_transform = list( phi = "identity", sigma_x = "log", sigma_y = "log" ), tune_control = default_tune_control(pilot_m = 100, pilot_burn_in = 10), verbose = TRUE ) ``` We see that the chains gives convergence issues, indicating that we should run it for more iterations, but we ignore this issue in this Vignette. It automatically prints data frame summarizing the results, which can be printed from any `pmmh_output` object by calling `print`. ```{r} print(result) ``` The chains are saved as `theta_chain` ```{r} chains <- result$theta_chain ``` Let's collect the chains for phi from the chains and visualize the densities ```{r} #| fig.alt: "Density plot of phi chains." ggplot(chains, aes(x = phi, fill = factor(chain))) + geom_density(alpha = 0.5) + labs( title = "Density plot of phi chains", x = "Value", y = "Density", fill = "Chain" ) + theme_minimal() ``` We have now fitted a simple SSM model using `bayesSSM`. Feel free to explore the package further and try out different models.