truth

<p>These data record the simulation truth in the paper "BAREB: A Bayesian Repulsive Biclustering Model for Periodontal Data".
It includes the true simulated parameters.</p>
<p>The variables are:</p><table class="table">
<tr><td><code>S</code></td><td>the number of patient level clusters</td></tr>
<tr><td><code>E</code></td><td>the clustering membership of patient level</td></tr>
<tr><td><code>K</code></td><td>the numbers of site level clusters</td></tr>
<tr><td><code>R</code></td><td>the site level clustering membership</td></tr>
<tr><td><code>Beta</code></td><td>the patient level linear coefficients</td></tr>
<tr><td><code>Gamma</code></td><td>the site level linear coefficients</td></tr>
<tr><td><code>mu</code></td><td>the underlying mean for CAL values</td></tr>
<tr><td><code>sigma_square</code></td><td>the variance of noise for CAL values</td></tr>
<tr><td><code>noise</code></td><td>the noise for CAL values</td></tr>
<tr><td><code>c</code></td><td>the parameter for missingness model</td></tr>
<tr><td><code>mu.star</code></td><td>the mean of latent values for missingness model</td></tr>
<tr><td><code>z.star</code></td><td>the latent values for missingness model</td></tr>
</table><p></p><p></p>

datasets

Simultaneously clusters the Periodontal diseases (PD) patients
and their tooth sites after taking the patient- and site-level covariates into consideration.
'BAREB' uses the determinantal point process (DPP) prior to induce diversity
among different biclusters to facilitate parsimony and interpretability.
Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.

Qingyang Liu

BAREB

A Bayesian Repulsive Biclustering Model for Periodontal Data

Yuliang Li

Yanxun Xu

Dipankar Bandyopadhyay

John Burkardt

truth function

<p>These data record the simulation truth in the paper "BAREB: A Bayesian Repulsive Biclustering Model for Periodontal Data".
It includes the true simulated parameters.</p>
<p>The variables are:</p><table class='table'>
<tr><td><code>S</code></td><td>the number of patient level clusters</td></tr>
<tr><td><code>E</code></td><td>the clustering membership of patient level</td></tr>
<tr><td><code>K</code></td><td>the numbers of site level clusters</td></tr>
<tr><td><code>R</code></td><td>the site level clustering membership</td></tr>
<tr><td><code>Beta</code></td><td>the patient level linear coefficients</td></tr>
<tr><td><code>Gamma</code></td><td>the site level linear coefficients</td></tr>
<tr><td><code>mu</code></td><td>the underlying mean for CAL values</td></tr>
<tr><td><code>sigma_square</code></td><td>the variance of noise for CAL values</td></tr>
<tr><td><code>noise</code></td><td>the noise for CAL values</td></tr>
<tr><td><code>c</code></td><td>the parameter for missingness model</td></tr>
<tr><td><code>mu.star</code></td><td>the mean of latent values for missingness model</td></tr>
<tr><td><code>z.star</code></td><td>the latent values for missingness model</td></tr>
</table>



The simulation truth — truth

The simulation truth

truth: The simulation truth

Description

Usage

Arguments

Examples

`S`	the number of patient level clusters
`E`	the clustering membership of patient level
`K`	the numbers of site level clusters
`R`	the site level clustering membership
`Beta`	the patient level linear coefficients
`Gamma`	the site level linear coefficients
`mu`	the underlying mean for CAL values
`sigma_square`	the variance of noise for CAL values
`noise`	the noise for CAL values
`c`	the parameter for missingness model
`mu.star`	the mean of latent values for missingness model
`z.star`	the latent values for missingness model