generateInitialSemiSupervisedLabels

For a dataset with some observed labels, generate a vector of
initial labels based on the proportion of each class in the observed subset.

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

generateInitialSemiSupervisedLabels function

<dl><dt>labels</dt>
<dd>The vector of labels, represented by integers, for the initial
labelling in a dataset.</dd>
<dt>fixed</dt>
<dd>The vector of 0s and 1s indicating which labels are observed /
to be held fixed.</dd></dl>

Arguments

Generate initial semi-sueprvised labels — generateInitialSemiSupervisedLabels

<dl>

<dt>labels</dt>
<dd>The vector of labels, represented by integers, for the initial
labelling in a dataset.</dd>


<dt>fixed</dt>
<dd>The vector of 0s and 1s indicating which labels are observed /
to be held fixed.</dd>

</dl>

generateInitialSemiSupervisedLabels: Generate initial semi-sueprvised labels

Description

Usage

Value

Arguments