R: Computing Highest Posterior Density (HPD) Intervals

hpd {BayesX}

R Documentation

Computing Highest Posterior Density (HPD) Intervals

Description

Compute approximate HPD intervals out of MCMC-samples in BayesX

Usage

hpd(data, alpha = 0.05, ...)
hpd.coda(data, alpha = 0.05, ...)

Arguments

`data`	Either the name of a file or a data frame containing the sample.
`alpha`	A numeric scalar in the interval (0,1) such that 1 - `alpha` is the target probability contained in the intervals.
`...`	Further parameters to be passed to the internal call of `optim` such has `start` and `integrate` such as `lower`.

Details

hpd computes the HPD interval based on a kernel density estimate of the samples. hpd.coda computes the HPD interval with the function HPDinterval available in package coda.

Author(s)

Nadja Klein

Examples

res <- read.table(system.file("examples/nonparametric_f_x_pspline_sample.raw",
                              package="BayesX"), header=TRUE)
hpd(res)
hpd.coda(res)

[Package BayesX version 0.2-9 Index]