binomial_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

binomial_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>probit.HaarPXDA</dt>
<dd>only used for binomial models with probit link. If supported,
<code>probit.HaarPXDA=TRUE</code> and the Haar PX-DA sandwich step is added to the Albert-Chib
data augmentation scheme for probit multilevel models. This will usually result in
a faster mixing MCMC algorithm. Currently supported when all coefficients are sampled
in a single Gibbs block and all coefficients' prior means equal zero.</dd></dl>

Arguments

Set computational options for the sampling algorithms — binomial_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>probit.HaarPXDA</dt>
<dd>only used for binomial models with probit link. If supported,
<code>probit.HaarPXDA=TRUE</code> and the Haar PX-DA sandwich step is added to the Albert-Chib
data augmentation scheme for probit multilevel models. This will usually result in
a faster mixing MCMC algorithm. Currently supported when all coefficients are sampled
in a single Gibbs block and all coefficients' prior means equal zero.</dd>

</dl>

binomial_control: Set computational options for the sampling algorithms

Description

Usage

Value

Arguments