transformPARAFACloadings

Transform PARAFAC loadings to an orthonormal basis.
Note: this function only works for 3-way PARAFAC models.

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 

transformPARAFACloadings function

<dl><dt>Fac</dt>
<dd>Fac object from a PARAFAC object, see <code>parafac()</code>.</dd>
<dt>modeToCorrect</dt>
<dd>Correct the subject (1), feature (2) or time mode (3).</dd>
<dt>moreOutput</dt>
<dd>Give orthonormal basis and transformation matrices as part of output (default FALSE).</dd></dl>

Arguments

Transform PARAFAC loadings to an orthonormal basis.
Note: this function only works for 3-way PARAFAC models. — transformPARAFACloadings

<dl>

<dt>Fac</dt>
<dd>Fac object from a PARAFAC object, see <code>parafac()</code>.</dd>


<dt>modeToCorrect</dt>
<dd>Correct the subject (1), feature (2) or time mode (3).</dd>


<dt>moreOutput</dt>
<dd>Give orthonormal basis and transformation matrices as part of output (default FALSE).</dd>

</dl>

transformPARAFACloadings: Transform PARAFAC loadings to an orthonormal basis. Note: this function only works for 3-way PARAFAC models.

Description

Usage

Value

Arguments

Examples