calcVarExpPerComponent

Calculate the variance explained of a PARAFAC model, per component

Creation and selection of PARAllel FACtor Analysis (PARAFAC)
models of longitudinal microbiome data. You can import your own data with
our import functions or use one of the example datasets to create your own
PARAFAC models. Selection of the optimal number of components can be done
using assessModelQuality() and assessModelStability(). The selected model can
then be plotted using plotPARAFACmodel(). The Parallel Factor
Analysis method was originally described by Caroll and Chang (1970)
<doi:10.1007/BF02310791> and Harshman (1970)
<https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

Geert Roelof van der Ploeg

parafac4microbiome

Parallel Factor Analysis Modelling of Longitudinal Microbiome
Data

Johan Westerhuis

Anna Heintz-Buschart

Age Smilde

University of Amsterdam 

calcVarExpPerComponent function

<dl><dt>Fac</dt>
<dd>Fac object output of a model</dd>
<dt>X</dt>
<dd>Input dataset</dd></dl>

Arguments

Calculate the variance explained of a PARAFAC model, per component — calcVarExpPerComponent

<dl>

<dt>Fac</dt>
<dd>Fac object output of a model</dd>


<dt>X</dt>
<dd>Input dataset</dd>

</dl>

calcVarExpPerComponent: Calculate the variance explained of a PARAFAC model, per component

Description

Usage

Value

Arguments

Examples