plot.pglmm_fit

Plot method for pglmm_fit continuous covariates cluster characteristics

Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) <doi:10.48550/arXiv.2510.08304>.

Matteo Amestoy

ProfileGLMM

Bayesian Profile Regression using Generalised Linear Mixed
Models

Mark van de Wiel

Wessel van Wieringen

plot.pglmm_fit function

<dl><dt>x</dt>
<dd>An object of class <code>pglmm_fit</code></dd>
<dt>...</dt>
<dd>Additional arguments<ul>
<li>title : main title of the plot</li>
<li>color : palette to be used</li>
</ul></dd></dl>

Arguments

Plot method for pglmm_fit continuous covariates cluster characteristics — plot.pglmm_fit

<dl>

<dt>x</dt>
<dd>An object of class <code>pglmm_fit</code></dd>


<dt>...</dt>
<dd>Additional arguments<ul>
<li>title : main title of the plot</li>
<li>color : palette to be used</li>
</ul></dd>

</dl>

plot.pglmm_fit: Plot method for pglmm_fit continuous covariates cluster characteristics

Description

Usage

Arguments