sampleViewPriorLabels

Generate labels from the stick-breaking prior.

Integrative clustering and semi-supervised prediction.
This package includes a 'C++' implementation of both semi-supervised and
unsupervised Multiple Dataset Integration (MDI; Kirk et al., 2012
<https://doi-org.ezp.lib.cam.ac.uk/10.1093/bioinformatics/bts595>), an
extension of Bayesian mixture models to the integrative (multiple dataset or
multi-modal) setting such as multi-omics analysis. The package also includes
Bayesian mixture models. Densities allowed within MDI and the mixture model
are Gaussian with full and diagonal covariance matrices, categorical, Gaussian
with a Gaussian process (GP) prior on the mean parameter. The GP model
implemented canonly handle data with common time points of equal separation.
The Gaussian and GP models can be augmented with a global multivariate t
distribution to handle outliers.

Stephen Coleman

mdir

Bayesian Model-Based Clustering of Multi-Modal Data

Paul Kirk

Chris Wallace

Oliver Crook

sampleViewPriorLabels 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 view prior labels — sampleViewPriorLabels

<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>

sampleViewPriorLabels: Sample view prior labels

Description

Usage

Value

Arguments

Examples