samplePriorLabels

Generate labels from the stick-breaking prior.

Semi-supervised and unsupervised Bayesian mixture models that
simultaneously infer the cluster/class structure and a batch correction.
Densities available are the multivariate normal and the multivariate t.
The model sampler is implemented in C++. This package is aimed at analysis of
low-dimensional data generated across several batches. See Coleman et al.
(2022) <doi:10.1101/2022.01.14.476352> for details of the model.

Stephen Coleman

batchmix

Semi-Supervised Bayesian Mixture Models Incorporating Batch
Correction

Paul Kirk

Chris Wallace

samplePriorLabels function

<dl><dt>alpha</dt>
<dd>The concentration parameter for the stick-breaking prior.</dd>
<dt>K</dt>
<dd>The number of components to include (the upper bound on the number of unique labels generated).</dd>
<dt>N</dt>
<dd>The number of labels to generate.</dd></dl>

Arguments

Sample prior labels — samplePriorLabels

<dl>

<dt>alpha</dt>
<dd>The concentration parameter for the stick-breaking prior.</dd>


<dt>K</dt>
<dd>The number of components to include (the upper bound on the number of unique labels generated).</dd>


<dt>N</dt>
<dd>The number of labels to generate.</dd>

</dl>

samplePriorLabels: Sample prior labels

Description

Usage

Value

Arguments

Examples