plot.codacore

Plots a summary of a fitted codacore model.
Credit to the authors of the selbal package (Rivera-Pinto et al., 2018),
from whose package these plots were inspired.

In the context of high-throughput genetic data,
CoDaCoRe identifies a set of sparse biomarkers that are
predictive of a response variable of interest (Gordon-Rodriguez
et al., 2021) <doi:10.1093/bioinformatics/btab645>. More
generally, CoDaCoRe can be applied to any regression problem
where the independent variable is Compositional (CoDa), to
derive a set of scale-invariant log-ratios (ILR or SLR) that
are maximally associated to a dependent variable.

Elliott Gordon-Rodriguez

codacore

Learning Sparse Log-Ratios for Compositional Data

Thomas Quinn

plot.codacore function

<dl><dt>x</dt>
<dd>A codacore object.</dd>
<dt>index</dt>
<dd>The index of the log-ratio to plot.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

plot — plot.codacore

<dl>

<dt>x</dt>
<dd>A codacore object.</dd>


<dt>index</dt>
<dd>The index of the log-ratio to plot.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

plot.codacore: plot

Description

Usage

Arguments