R/bootstrap_filter.R In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Documented in bootstrap_filterbootstrap_filter.gaussianbootstrap_filter.nongaussianbootstrap_filter.ssm_nlgbootstrap_filter.ssm_sde

#' Bootstrap Filtering
#'
#' 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 ... 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}).
#' @export
#' @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.
#' @rdname bootstrap_filter
bootstrap_filter <- function(model, particles, ...) {
UseMethod("bootstrap_filter", model)
}
#' @method bootstrap_filter gaussian
#' @rdname bootstrap_filter
#' @export
#' @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$att), col = 1:3) #' bootstrap_filter.gaussian <- function(model, particles, seed = sample(.Machine$integer.max, size = 1), ...) {

if (missing(particles)) {
nsim <- eval(match.call(expand.dots = TRUE)$nsim) if (!is.null(nsim)) { warning(paste0("Argument nsim is deprecated. Use argument particles", "instead.", sep = " ")) particles <- nsim } } particles <- check_integer(particles, "particles") seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max)

nsamples <- ifelse(!is.null(nrow(model$y)), nrow(model$y), length(model$y)) * length(model$a1) * particles
if (particles > 100 & nsamples > 1e12) {
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) <-
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$a1) out$alpha <- aperm(out$alpha, c(2, 1, 3)) out } #' @method bootstrap_filter nongaussian #' @rdname bootstrap_filter #' @export #' @examples #' data("poisson_series") #' model <- bsm_ng(poisson_series, sd_level = 0.1, sd_slope = 0.01, #' P1 = diag(1, 2), distribution = "poisson") #' #' out <- bootstrap_filter(model, particles = 100) #' ts.plot(cbind(poisson_series, exp(out$att[, 1])), col = 1:2)
#'
bootstrap_filter.nongaussian <- function(model, particles,
seed = sample(.Machine$integer.max, size = 1), ...) { if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim)
if (!is.null(nsim)) {
warning(paste0("Argument nsim is deprecated. Use argument particles",
particles <- nsim
}
}
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$a1) * particles if (particles > 100 & nsamples > 1e12) { warning(paste("Trying to sample ", nsamples, "particles, you might run out of memory.")) } model$distribution <-
pmatch(model$distribution, c("svm", "poisson", "binomial", "negative binomial", "gamma", "gaussian"), duplicates.ok = TRUE) - 1 out <- bsf(model, particles, seed, FALSE, model_type(model)) colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <-
colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <- names(model$a1)
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)
out$alpha <- aperm(out$alpha, c(2, 1, 3))
out
}
#' @method bootstrap_filter ssm_nlg
#' @rdname bootstrap_filter
#' @export
bootstrap_filter.ssm_nlg <- function(model, particles,
seed = sample(.Machine$integer.max, size = 1), ...) { if (missing(particles)) { nsim <- eval(match.call(expand.dots = TRUE)$nsim)
if (!is.null(nsim)) {
warning(paste("Argument nsim is deprecated. Use argument particles",
particles <- nsim
}
}
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."))
}
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,
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) <- 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$alpha, c(2, 1, 3)) out } #' @method bootstrap_filter ssm_sde #' @rdname bootstrap_filter #' @param L Positive integer defining the discretization level for SDE models. #' @export bootstrap_filter.ssm_sde <- function(model, particles, L, seed = sample(.Machine$integer.max, size = 1), ...) {

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)$nsim) if (!is.null(nsim)) { warning(paste("Argument nsim is deprecated. Use argument particles", "instead.", sep = " ")) particles <- nsim } } particles <- check_integer(particles, "particles") nsamples <- length(model$y) * particles
if (particles > 100 & nsamples > 1e12) {
warning(paste("Trying to sample ", nsamples,
"particles, you might run out of memory."))
}
seed <- check_integer(seed, "seed", FALSE, max = .Machine$integer.max) out <- bsf_sde(model$y, model$x0, model$positive,
model$drift, model$diffusion, model$ddiffusion, model$prior_pdf, model$obs_pdf, model$theta,
particles, round(L), seed)
colnames(out$at) <- colnames(out$att) <- colnames(out$Pt) <- colnames(out$Ptt) <- rownames(out$Pt) <- rownames(out$Ptt) <-
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
}


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bssm documentation built on Sept. 21, 2021, 5:09 p.m.