HPDregionplot {emdbook}R Documentation

Plot highest posterior density region

Description

Given a sample from a posterior distribution (an mcmc object from the coda package), plot the bivariate region of highest marginal posterior density for two variables, using kde2d from MASS to calculate a bivariate density.

Usage

HPDregionplot(x, vars = 1:2, h = c(1, 1), n = 50, lump = TRUE, prob = 0.95, xlab = NULL, ylab = NULL, ...)

Arguments

x an mcmc or mcmc.list object
vars which variables to plot: numeric or character vector
h bandwidth of 2D kernel smoother
n number of points at which to evaluate the density grid
lump if x is an mcmc.list object, lump the chains together for plotting?
prob probability level
xlab x axis label
ylab y axis label
... other arguments to contour

Details

Uses kde2d to calculate a bivariate density, then normalizes the plot and calculates the contour corresponding to a contained volume of prob of the total volume under the surface (a two-dimensional Bayesian credible region).

Value

Draws a plot on the current device, and invisibly returns a list of contour lines (contourLines).

Note

Accuracy may be limited by density estimation; you may need to tinker with h and n (see kde2d in the MASS package).

Author(s)

Ben Bolker

See Also

HPDinterval in the \coda package, ellipse package

Examples

library(MASS)
library(coda)
z <- mvrnorm(1000,mu=c(0,0),Sigma=matrix(c(2,1,1,2),nrow=2))
HPDregionplot(mcmc(z))

[Package emdbook version 1.1.1.1 Index]