Shao2019

The Shao2019 longitudinal microbiome dataset as a three-dimensional array,
with subjects in mode 1, microbial abundances in mode 2 and time in mode 3.
Note: only time points 4, 7, 21 and Infancy are used.
Note: all-zero microbial abundances have been removed to save disk space.

datasets

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 

Shao2019 function

<div class="section" id="shao-">
<h3><code>Shao2019</code></h3>
A list object with three elements:<dl>
<dt>data</dt>
<dd>Array object of the data cube</dd><dt>mode1</dt>
<dd>Dataframe with all the subject metadata, ordered the same as the rows in the data cube.</dd><dt>mode2</dt>
<dd>Taxonomic classification of the microbiota, ordered the same as the columns in the data cube.</dd><dt>mode3</dt>
<dd>Dataframe with the time metadata, ordered the same as the third dimension in the array.</dd>...</dl></div>

Format

Shao2019 longitudinal microbiome data — Shao2019

<div class='section' id='shao-'>
<h3><code>Shao2019</code></h3>


A list object with three elements:<dl>
<dt>data</dt>
<dd>Array object of the data cube</dd>

<dt>mode1</dt>
<dd>Dataframe with all the subject metadata, ordered the same as the rows in the data cube.</dd>

<dt>mode2</dt>
<dd>Taxonomic classification of the microbiota, ordered the same as the columns in the data cube.</dd>

<dt>mode3</dt>
<dd>Dataframe with the time metadata, ordered the same as the third dimension in the array.</dd>

...

</dl>

</div>

Shao2019: Shao2019 longitudinal microbiome data

Description

Usage

Arguments

Format

`Shao2019`