<p>This is a matrix with four candidate covariates. The covariates in the first two columns are in the true model.</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

X function


<div class="section" id="x">
<h3><code>X</code></h3><p></p><p>
</p><p>A matrix with 100 observations and 4 covariates.</p>
</div>


Format

The matrix of candidate covariates — X


<div class='section' id='x'>
<h3><code>X</code></h3>


<p>A matrix with 100 observations and 4 covariates.</p>
</div>


X: The matrix of candidate covariates

Description

Usage

Arguments

Format

`X`