HPDint

Adapts code from <code>coda::HPDinterval()</code> to compute a highest posterior density interval from posterior samples for a single parameter. Quoting from the <code>coda</code> help file, for each parameter the interval is constructed from the empirical cdf of the sample as the shortest interval for which the difference in the ecdf values of the endpoints is the nominal probability. Assuming that the distribution is not severely multimodal, this is the HPD interval.

A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics. It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'. The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) <https://www.jstor.org/stable/2347760>.

Frank Harrell Jr

rmsb

Bayesian Regression Modeling Strategies

Frank Harrell

Ben Goodrich

Ben Bolker

Doug Bates

HPDint function

<dl><dt>x</dt>
<dd>a vector of posterior draws</dd>
<dt>prob</dt>
<dd>desired probability coverage</dd></dl>

Arguments

Author

Highest Posterior Density Interval — HPDint

<dl>

<dt>x</dt>
<dd>a vector of posterior draws</dd>


<dt>prob</dt>
<dd>desired probability coverage</dd>

</dl>

HPDint: Highest Posterior Density Interval

Description

Usage

Value

Arguments

Author