reconstruction

<code>reconstruction</code> returns the full data reconstruction based on given
posterior 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

reconstruction function

<dl><dt>res</dt>
<dd>The sampled model from function <code>gfa</code></dd>
<dt>average</dt>
<dd>If TRUE (default), averages the reconstruction over the
posterior predictive samples. If set to
FALSE, the output may require a large amount of memory. In case of large
input data, we recommend acquiring the posterior predictive samples for
subsets of data at a time, based on this implementation.</dd></dl>

Arguments

Full data reconstruction based on posterior samples — reconstruction

<dl>

<dt>res</dt>
<dd>The sampled model from function <code>gfa</code></dd>


<dt>average</dt>
<dd>If TRUE (default), averages the reconstruction over the
posterior predictive samples. If set to
FALSE, the output may require a large amount of memory. In case of large
input data, we recommend acquiring the posterior predictive samples for
subsets of data at a time, based on this implementation.</dd>

</dl>

reconstruction: Full data reconstruction based on posterior samples

Description

Usage

Value

Arguments