hpd: Computing Highest Posterior Density (HPD) Intervals

In BayesX: R Utilities Accompanying the Software Package BayesX

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 content of the intervals.. The default is alpha = 0.05.
...	Further parameters to be passed to the internal call of optim and integrate.

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)

BayesX documentation built on Oct. 20, 2023, 9:11 a.m.