pr_invwishart

Degrees of freedom parameter. This should be a scalar numeric value.
The default value is the dimension (<code>n</code>) plus one.

Either a (known) scale matrix, or
<code>scale="modeled"</code> or <code>scale="modelled"</code>, which puts default
chi-squared priors on the diagonal elements of the inverse Wishart scale matrix.
For more control on these chi-squared priors a list can be passed with some of the
following components:<dl class="dl-horizontal">
 <dt>df</dt><dd>degrees of freedom (scalar or vector) of the chi-squared distribution(s)</dd>
 <dt>scale</dt><dd>scale parameter(s) of the chi-squared distribution(s)</dd>
 <dt>common</dt><dd>whether the modeled scale parameter of the inverse chi-squared
 distribution is (a scalar parameter) common to all <code>n</code> diagonal elements.</dd>
</dl>

scale

dimension, if known. For internal use only.

Create an object containing information about an inverse Wishart prior,
possibly with modeled scale matrix

Fit multi-level models with possibly correlated
random effects using Markov Chain Monte Carlo simulation.
Such models allow smoothing over space and time and are useful in,
for example, small area estimation.

Harm Jan Boonstra

mcmcsae

Markov Chain Monte Carlo Small Area Estimation

Grzegorz Baltissen

pr_invwishart function

Either a (known) scale matrix, or
<code>scale="modeled"</code> or <code>scale="modelled"</code>, which puts default
chi-squared priors on the diagonal elements of the inverse Wishart scale matrix.
For more control on these chi-squared priors a list can be passed with some of the
following components:<dl class='dl-horizontal'>
 <dt>df</dt><dd>degrees of freedom (scalar or vector) of the chi-squared distribution(s)</dd>
 <dt>scale</dt><dd>scale parameter(s) of the chi-squared distribution(s)</dd>
 <dt>common</dt><dd>whether the modeled scale parameter of the inverse chi-squared
 distribution is (a scalar parameter) common to all <code>n</code> diagonal elements.</dd>
</dl>

pr_invwishart: Create an object containing information about an inverse Wishart prior, possibly with modeled scale matrix

Description

Usage

Arguments

Value

References