sequentialGfaPrediction

<code>sequentialGfaPrediction</code> returns predictions for unobserved data
sources of a new set of data samples.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

Eemeli Lepp<e4>aho

Group Factor Analysis

Eemeli Leppäaho

Seppo Virtanen

Muhammad Ammad-ud-din

Suleiman A Khan

Tommi Suvitaival

Inka Saarinen

Samuel Kaski

sequentialGfaPrediction function

<dl><dt>Y</dt>
<dd>The new data samples, in a similar format as in function
<code>gfa</code></dd>
<dt>model</dt>
<dd>The sampled model from function <code>gfa</code>, with
model$opts$predict being a logical vector with the length matching the
length of Y, describing which data views will be predicted (TRUE), and
which have been observed (FALSE).</dd></dl>

Arguments

Sequential prediction of new samples from observed data views to unobserved — sequentialGfaPrediction

<dl>

<dt>Y</dt>
<dd>The new data samples, in a similar format as in function
<code>gfa</code></dd>


<dt>model</dt>
<dd>The sampled model from function <code>gfa</code>, with
model$opts$predict being a logical vector with the length matching the
length of Y, describing which data views will be predicted (TRUE), and
which have been observed (FALSE).</dd>

</dl>

sequentialGfaPrediction: Sequential prediction of new samples from observed data views to unobserved

Description

Usage

Value

Arguments