getLogRatios

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

getLogRatios function

<dl><dt>cdcr</dt>
<dd>A codacore object</dd>
<dt>x</dt>
<dd>A set of (possibly unseen) compositional data. 
The covariates must be passed in the same order as 
for the original codacore() call.</dd></dl>

Arguments

getLogRatios — getLogRatios

<dl>

<dt>cdcr</dt>
<dd>A codacore object</dd>


<dt>x</dt>
<dd>A set of (possibly unseen) compositional data. 
The covariates must be passed in the same order as 
for the original codacore() call.</dd>

</dl>

getLogRatios: getLogRatios

Description

Usage

Value

Arguments