#' @title Time windows for \code{PMCMC} objects
#'
#' @description \code{window} method for class \code{PMCMC}.
#'
#' @details Acts as a wrapper function for \code{\link[coda]{window.mcmc}}
#' from the \code{coda} package
#'
#' @param x a \code{PMCMC} object, usually as a result of a call to
#' \code{PMCMC}.
#' @param \dots arguments to pass to \code{\link{window.mcmc}}
#' @return a \code{PMCMC} object
#'
#' @export
#'
#' @seealso \code{\link{PMCMC}}, \code{\link{print.PMCMC}}, \code{\link{predict.PMCMC}}, \code{\link{summary.PMCMC}}
#' \code{\link{plot.PMCMC}}
#'
#' @method window PMCMC
#'
#' @examples
#' \donttest{
#' ## set up data to pass to PMCMC
#' flu_dat <- data.frame(
#' t = 1:14,
#' Robs = c(3, 8, 26, 76, 225, 298, 258, 233, 189, 128, 68, 29, 14, 4)
#' )
#'
#' ## set up observation process
#' obs <- data.frame(
#' dataNames = "Robs",
#' dist = "pois",
#' p1 = "R + 1e-5",
#' p2 = NA,
#' stringsAsFactors = FALSE
#' )
#'
#' ## set up model (no need to specify tspan
#' ## argument as it is set in PMCMC())
#' transitions <- c(
#' "S -> beta * S * I / (S + I + R + R1) -> I",
#' "I -> gamma * I -> R",
#' "R -> gamma1 * R -> R1"
#' )
#' compartments <- c("S", "I", "R", "R1")
#' pars <- c("beta", "gamma", "gamma1")
#' model <- mparseRcpp(
#' transitions = transitions,
#' compartments = compartments,
#' pars = pars,
#' obsProcess = obs
#' )
#'
#' ## set priors
#' priors <- data.frame(
#' parnames = c("beta", "gamma", "gamma1"),
#' dist = rep("unif", 3),
#' stringsAsFactors = FALSE)
#' priors$p1 <- c(0, 0, 0)
#' priors$p2 <- c(5, 5, 5)
#'
#' ## define initial states
#' iniStates <- c(S = 762, I = 1, R = 0, R1 = 0)
#'
#' set.seed(50)
#'
#' ## run PMCMC algorithm
#' post <- PMCMC(
#' x = flu_dat,
#' priors = priors,
#' func = model,
#' u = iniStates,
#' npart = 25,
#' niter = 5000,
#' nprintsum = 1000
#' )
#'
#' ## plot MCMC traces
#' plot(post, "trace")
#'
#' ## continue for some more iterations
#' post <- PMCMC(post, niter = 5000, nprintsum = 1000)
#'
#' ## plot traces and posteriors
#' plot(post, "trace")
#' plot(post)
#'
#' ## remove burn-in
#' post <- window(post, start = 5000)
#'
#' ## summarise posteriors
#' summary(post)
#' }
#'
window.PMCMC <- function(x, ...) {
if(class(x) != "PMCMC"){
stop("'x' is not a PMCMC object")
}
## extract 'mcmc' object
y <- x$pars
## extract subset
y <- window(y, ...)
## generate new PMCMC object
x$pars <- y
x
}
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