<p>These data record the simulation observation (observed value and missing indicator) and covariates in the paper "BAREB: A Bayesian Repulsive Biclustering Model for Periodontal Data".</p>
<p>The variables are:</p><table class="table">
<tr><td><code>delta</code></td><td>the missing indicator matrix</td></tr>
<tr><td><code>X</code></td><td>the patient level covariates</td></tr>
<tr><td><code>Y</code></td><td>the observed (CAL) value matrix</td></tr>
<tr><td><code>Z</code></td><td>the site level covariates</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

obs function

<p>These data record the simulation observation (observed value and missing indicator) and covariates in the paper "BAREB: A Bayesian Repulsive Biclustering Model for Periodontal Data".</p>
<p>The variables are:</p><table class='table'>
<tr><td><code>delta</code></td><td>the missing indicator matrix</td></tr>
<tr><td><code>X</code></td><td>the patient level covariates</td></tr>
<tr><td><code>Y</code></td><td>the observed (CAL) value matrix</td></tr>
<tr><td><code>Z</code></td><td>the site level covariates</td></tr>
</table>



obs: The simulation observation

Description

Usage

Arguments

Examples

`delta`	the missing indicator matrix
`X`	the patient level covariates
`Y`	the observed (CAL) value matrix
`Z`	the site level covariates