print.codacore

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

print.codacore function

<dl><dt>x</dt>
<dd>A codacore object.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

print — print.codacore

<dl>

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


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

</dl>

print.codacore: print

Description

Usage

Arguments