HPDregionplot {emdbook} | R Documentation |
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.
HPDregionplot(x, vars = 1:2, h = c(1, 1), n = 50, lump = TRUE, prob = 0.95, xlab = NULL, ylab = NULL, ...)
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 |
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).
Draws a plot on the current device, and
invisibly returns a list of contour lines (contourLines
).
Accuracy may be limited by density estimation; you may
need to tinker with h
and n
(see kde2d
in the MASS
package).
Ben Bolker
HPDinterval
in the \coda
package,
ellipse
package
library(MASS) library(coda) z <- mvrnorm(1000,mu=c(0,0),Sigma=matrix(c(2,1,1,2),nrow=2)) HPDregionplot(mcmc(z))