R/mcmcplot.R

In xsswang/remiod: Reference-Based Multiple Imputation for Ordinal/Binary Response

#' Visualizing the posterior sample
#' Creates a set of plots for visually evaluating convergence and mixing of the chains
#' from the MCMC sample of an object of class 'remiod'.
#'
#' @inheritParams JointAI::densplot
#' @inheritParams base::plot
#' @inheritDotParams graphics::matplot -x -y -type -xlab -ylab -pch -log -xlim -ylim
#' @param object an object inheriting from class 'remoid'
#' @param what select either trace or density plots from MCMC samples
#' @param mi.setting a list of arguments for extracting MI data set, which
#'                   will be used to update the one in `remoid` object.
#'                   Default is NULL, meaning no update to the mi.setting
#'                   in `remoid` object.
#' @param ... optional arguments.
#'
#' @return plots of traces or densities of MCMC samples for selected parameters in
#'         imputation models.
#'
#' @examples
#' \donttest{
#' # data(schizow)
#'
#' test = remiod(formula = y6 ~ tx + y0 + y1 + y3, data = schizow,
#'               trtvar = 'tx', algorithm = 'jags', method="MAR",
#'               ord_cov_dummy = FALSE, n.adapt = 10, n.chains = 1,
#'               n.iter = 10, thin = 2, warn = FALSE, seed = 1234)
#'
#' p1 = mcmcplot(object=test, what="trace")
#' }
#'
#' @import ggplot2
#' @importFrom JointAI traceplot densplot
#' @export
#'
mcmcplot <- function(object, what=c("trace","density"), subset = c(analysis_main=TRUE),
                     outcome = NULL, mi.setting=NULL, nrow=NULL, ncol=NULL,
                     use_ggplot=TRUE, mess=TRUE, warn=FALSE,...){
  if (!inherits(object, "remiod"))
    errormsg("Use only with objects of class remiod")

  old = object[['mi.setting']]
  if (!is.null(mi.setting)) miset = list_update(old, mi.setting)
  else miset = old

  ## MI setting args
  start = miset[['start']]
  end = miset[['end']]
  thin = miset[['thin']]
  seed = miset[['seed']]
  exclude_chains = miset[['exclude_chains']]

  what <- match.arg(what)
  plotfun <- switch(what,
                    "trace" = JointAI::traceplot,
                    "density" = JointAI::densplot)

  obj = object[['mc.mar']]
  attr(obj, "class") <- "JointAI"
  plotfun(object = obj, subset=subset, outcome=outcome, use_ggplot=use_ggplot,
          exclude_chains=exclude_chains, start=start, end=end, thin=thin,
          nrow=nrow, ncol=ncol, mess=mess, warn=warn,...)

}