R/ggs_density.R

In ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

#' Density plots of the chains
#'
#' Density plots with the parameter distribution. For multiple chains, use colours to differentiate the distributions.
#'
#' @references Fernández-i-Marín, Xavier (2016) ggmcmc: Analysis of MCMC Samples and Bayesian Inference. Journal of Statistical Software, 70(9), 1-20. doi:10.18637/jss.v070.i09
#' @param D Data frame whith the simulations.
#' @param family Name of the family of parameters to plot, as given by a character vector or a regular expression. A family of parameters is considered to be any group of parameters with the same name but different numerical value between square brackets (as beta[1], beta[2], etc). 
#' @param rug Logical indicating whether a rug must be added to the plot. It is FALSE by default, since in large chains it may use lot of resources and it is not central to the plot.
#' @param hpd Logical indicating whether HPD intervals (using the defaults from ci()) must be added to the plot. It is FALSE by default.
#' @param greek Logical value indicating whether parameter labels have to be parsed to get Greek letters. Defaults to false.
#' @return A \code{ggplot} object.
#' @export
#' @examples
#' data(linear)
#' ggs_density(ggs(s))
ggs_density <- function(D, family = NA, rug = FALSE, hpd = FALSE, greek = FALSE) {
  # Manage subsetting a family of parameters
  if (!is.na(family)) {
    D <- get_family(D, family=family)
  }
  # Plot
  if (attributes(D)$nChains <= 1) {
    f <- ggplot(D, aes(x=value))
  } else {
    f <- ggplot(D, aes(x=value, colour=as.factor(Chain), fill=as.factor(Chain)))
  }
  f <- f + geom_density(alpha=0.3) + 
      scale_fill_discrete(name="Chain") + scale_colour_discrete(name="Chain")
  if (!greek) {
      f <- f + facet_wrap(~ Parameter, ncol=1, scales="free")
  } else {
      f <- f + facet_wrap(~ Parameter, ncol=1, scales="free", labeller = label_parsed)
  }
  if (rug) f <- f + geom_rug(alpha=0.1)
  if (hpd) {
    ciD <- ci(D)
    f <- f + geom_segment(data = ciD, size = 0.5, inherit.aes = FALSE,
                          aes(x = low, xend = high, y = 0, yend = 0)) +
             geom_segment(data = ciD, size = 1, inherit.aes = FALSE,
                          aes(x = Low, xend = High, y = 0, yend = 0))
  }
  return(f)
}