poisson_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

poisson_control function

<dl><dt>nb.shape</dt>
<dd>shape parameter of the negative binomial distribution used
internally to approximate the Poisson distribution. This should be set to a relatively
large value (default is 100), corresponding to negligible overdispersion, to obtain a
good approximation to the Poisson sampling distribution. However, note that very large
values may cause slow MCMC exploration of the posterior distribution.</dd></dl>

Arguments

Set computational options for the sampling algorithms — poisson_control

<dl>

<dt>nb.shape</dt>
<dd>shape parameter of the negative binomial distribution used
internally to approximate the Poisson distribution. This should be set to a relatively
large value (default is 100), corresponding to negligible overdispersion, to obtain a
good approximation to the Poisson sampling distribution. However, note that very large
values may cause slow MCMC exploration of the posterior distribution.</dd>

</dl>

poisson_control: Set computational options for the sampling algorithms

Description

Usage

Value

Arguments