<p>The data was simulated under a Poisson Generalized linear mixed model with four candidate covariates, a vector of spatial random effects, and a vector of overdispersion random effects.
The matrix of candidate covariates, the design matrices for two types of random effects, and the covariance matrices for two types of random effects are provide in the package.</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

Y function


<div class="section" id="y">
<h3><code>Y</code></h3><p></p><p>
</p><p>A vector with 100 observations.</p>
</div>


Format

The vector of observations — Y


<div class='section' id='y'>
<h3><code>Y</code></h3>


<p>A vector with 100 observations.</p>
</div>


Y: The vector of observations

Description

Usage

Arguments

Format

`Y`