negbinomial_control

Set computational options for the sampling algorithms

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

negbinomial_control function

<dl><dt>PG.approx</dt>
<dd>whether Polya-Gamma draws for logistic binomial models are
approximated by a hybrid gamma convolution approach. If not, <code>BayesLogit::rpg</code>
is used, which is exact for some values of the shape parameter.</dd>
<dt>PG.approx.m</dt>
<dd>if <code>PG.approx=TRUE</code>, the number of explicit gamma draws in the
sum-of-gammas representation of the Polya-Gamma distribution. The remainder (infinite)
convolution is approximated by a single moment-matching gamma draw. Special values are:
<code>-2L</code> for a default choice depending on the value of the shape parameter
balancing performance and accuracy, <code>-1L</code> for a moment-matching normal approximation,
and <code>0L</code> for a moment-matching gamma approximation.</dd>
<dt>CRT.approx.m</dt>
<dd>scalar integer specifying the degree of approximation to sampling
from a Chinese Restaurant Table distribution. The approximation is based on Le Cam's theorem.
Larger values yield a slower but more accurate sampler.</dd></dl>

Arguments

Set computational options for the sampling algorithms — negbinomial_control

<dl>

<dt>PG.approx</dt>
<dd>whether Polya-Gamma draws for logistic binomial models are
approximated by a hybrid gamma convolution approach. If not, <code>BayesLogit::rpg</code>
is used, which is exact for some values of the shape parameter.</dd>


<dt>PG.approx.m</dt>
<dd>if <code>PG.approx=TRUE</code>, the number of explicit gamma draws in the
sum-of-gammas representation of the Polya-Gamma distribution. The remainder (infinite)
convolution is approximated by a single moment-matching gamma draw. Special values are:
<code>-2L</code> for a default choice depending on the value of the shape parameter
balancing performance and accuracy, <code>-1L</code> for a moment-matching normal approximation,
and <code>0L</code> for a moment-matching gamma approximation.</dd>


<dt>CRT.approx.m</dt>
<dd>scalar integer specifying the degree of approximation to sampling
from a Chinese Restaurant Table distribution. The approximation is based on Le Cam's theorem.
Larger values yield a slower but more accurate sampler.</dd>

</dl>

negbinomial_control: Set computational options for the sampling algorithms

Description

Usage

Value

Arguments