<p>This is a list of two design matrices for two types of random effects.
The first component is for spatial random effects. The second componet is for overdispersion random effects.</p>

datasets

A Bayesian model selection approach for generalized linear mixed models.
Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously.
Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix.
'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics.
'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.

Shuangshuang Xu

GLMMselect

Bayesian Model Selection for Generalized Linear Mixed Models

Marco Ferreira

Erica Porter

Christopher Franck

Z function


<div class="section" id="z">
<h3><code>Z</code></h3><p></p><p>
</p><p>A list of design matrices for random effects.</p>
</div>


Format

A list of design matrices — Z


<div class='section' id='z'>
<h3><code>Z</code></h3>


<p>A list of design matrices for random effects.</p>
</div>


Z: A list of design matrices

Description

Usage

Arguments

Format

`Z`