#' @title Plots \code{PMCMC} objects
#'
#' @description Plot method for \code{PMCMC} objects.
#'
#' @param x A \code{PMCMC} object.
#' @param type Takes the value \code{"post"} if you want to plot posterior distributions.
#' Takes the value \code{"trace"} if you want to plot the trace plots.
#' @param joint A logical describing whether joint or marginal distributions are wanted.
#' @param transfunc Is a \code{function} object where the arguments to the function must
#' match all or a subset of the parameters in the model. This function needs
#' to return a \code{data.frame} object with columns containing the transformed
#' parameters.
#' @param ask Should the user ask before moving onto next trace plot.
#' @param ... Not used here.
#'
#' @return A plot of the (approximate) posterior distributions obtained from fitting a particle
#' Markov chain Monte Carlo algorithm, or provides corresponding trace plots.
#'
#' @method plot PMCMC
#'
#' @export
#'
#' @seealso \code{\link{PMCMC}}, \code{\link{print.PMCMC}}, \code{\link{predict.PMCMC}}, \code{\link{summary.PMCMC}}
#' \code{\link{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)
#' }
#'
plot.PMCMC <- function(x, type = c("post", "trace"), joint = FALSE, transfunc = NA, ask = TRUE, ...) {
## check x
if(class(x) != "PMCMC"){
stop("'x' not a PMCMC object")
}
## check type
type <- type[1]
checkInput(type, "character", inSet = c("post", "trace"))
## check joint
checkInput(joint, c("vector", "logical"), 1)
if(joint) {
if(!requireNamespace("GGally", quietly = TRUE)) {
stop("'GGally' package required for joint distribution plots")
}
}
## check ask
checkInput(ask, c("vector", "logical"), 1)
## extract levels
collev <- colnames(x$pars)
if(type == "post") {
p <- as.matrix(x$pars) %>% as.data.frame()
## check for transformations if required
checkInput(transfunc, length = 1, naAllow = TRUE)
if(is.function(transfunc)) {
## check function arguments
fargs <- formals(transfunc)
checkInput(names(fargs), inSet = colnames(p))
## perform transformations if required
temppars <- p[, match(names(fargs), colnames(p))]
temppars <- as.list(temppars)
names(temppars) <- names(fargs)
temp <- do.call("transfunc", temppars)
checkInput(temp, "data.frame", nrow = nrow(p))
## bind to current posterior samples
p <- cbind(p, temp)
collev <- colnames(p)
} else {
if(!is.na(transfunc)) {
stop("'transfunc' incorrectly specified")
}
}
## plot posteriors
if(!joint) {
p <- p %>%
gather(Parameter, value) %>%
mutate(Parameter = factor(Parameter, levels = collev)) %>%
ggplot(aes(x = value)) +
geom_density() +
xlab("Parameter value") + ylab("Density") +
facet_wrap(~ Parameter, scales = "free")
} else {
p <- GGally::ggpairs(p,
diag = list(continuous = GGally::wrap("densityDiag", alpha = 0.8)),
lower = list(continuous = "density"),
upper = list(continuous = "blank"))
}
return(p)
} else {
plot(x$pars, density = FALSE, ask = ask)
}
}
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