Predict.matrix.randwalk.smooth

Predict Methods for random walk smooth

internal

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

Predict.matrix.randwalk.smooth function

<dl><dt>object</dt>
<dd>a smooth object; see documentation for other methods in
<code>mgcv</code>.</dd>
<dt>data</dt>
<dd>a data.frame; see documentation for other methods in
<code>mgcv</code>.</dd></dl>

Arguments

Predict Methods for random walk smooth — Predict.matrix.randwalk.smooth

<dl>

<dt>object</dt>
<dd>a smooth object; see documentation for other methods in
<code>mgcv</code>.</dd>


<dt>data</dt>
<dd>a data.frame; see documentation for other methods in
<code>mgcv</code>.</dd>

</dl>

Predict.matrix.randwalk.smooth: Predict Methods for random walk smooth

Description

Usage

Arguments