MAVB

Given a model estimated using <code>vglmer</code>, this function
 performs marginally augmented variational Bayes (MAVB) to improve the
 approximation quality.

Estimates hierarchical models using mean-field variational Bayes.
At present, it can estimate logistic, linear, and negative binomial models.
It can accommodate models with an arbitrary number of random effects and
requires no integration to estimate. It also provides the ability to improve
the quality of the approximation using marginal augmentation.
Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035>
provide details on the variational algorithms.

Max Goplerud

vglmer

Variational Inference for Hierarchical Generalized Linear Models

MAVB function

<dl><dt>object</dt>
<dd>Model fit using <code>vglmer</code>.</dd>
<dt>samples</dt>
<dd>Number of samples to draw.</dd>
<dt>verbose</dt>
<dd>Show progress in drawing samples.</dd>
<dt>var_px</dt>
<dd>Variance of working prior for marginal augmentation. Default
(<code>Inf</code>) is a flat, improper, prior.</dd></dl>

Arguments

Perform MAVB after fitting vglmer — MAVB

<dl>

<dt>object</dt>
<dd>Model fit using <code>vglmer</code>.</dd>


<dt>samples</dt>
<dd>Number of samples to draw.</dd>


<dt>verbose</dt>
<dd>Show progress in drawing samples.</dd>


<dt>var_px</dt>
<dd>Variance of working prior for marginal augmentation. Default
(<code>Inf</code>) is a flat, improper, prior.</dd>

</dl>

MAVB: Perform MAVB after fitting vglmer

Description

Usage

Value

Arguments

Details

References