hpd: Compute Highest Posterior Density Intervals

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/hpd.R

Description

Compute the Highest Posterior Density Interval (HPD) from an inverse density function (hpd) or a vector of realizations of the distribution (emp.hpd).

Usage

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hpd(posterior.icdf, conf=0.95, tol=0.00000001,...)

emp.hpd(x, conf=0.95)

Arguments

posterior.icdf

Function, the inverse cdf of the posterior distribution (usually a function whose name starts with 'q').

x

A vector of realizations from the posterior distribution.

conf

Scalar, the confidence level desired.

tol

Scalar, the tolerance for optimize.

...

Additional arguments to posterior.icdf.

Details

These functions compute the highest posterior density intervals (sometimes called minimum length confidence intervals) for a Bayesian posterior distribution. The hpd function is used when you have a function representing the inverse cdf (the common case with conjugate families). The emp.hpd function is used when you have realizations of the posterior (when you have results from an MCMC run).

Value

A vector of length 2 with the lower and upper limits of the interval.

Note

These functions assume that the posterior distribution is unimodal, they compute only 1 interval, not the set of intervals that are appropriate for multimodal distributions.

Author(s)

Greg Snow [email protected]

See Also

hdr in the hdrcde package.

Examples

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hpd(qbeta, shape1=50, shape2=250)

tmp <- rbeta(10000, 50, 250)
emp.hpd(tmp)

TeachingDemos documentation built on May 29, 2017, 11:33 a.m.