getNumeratorParts

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

getNumeratorParts function

<dl><dt>cdcr</dt>
<dd>A codacore object.</dd>
<dt>baseLearnerIndex</dt>
<dd>An integer indicating which of the 
(possibly multiple) log-ratios learned by codacore to be used.</dd>
<dt>boolean</dt>
<dd>Whether to return the parts in boolean form
(a vector of TRUE/FALSE) or to return the column names of
those parts directly.</dd></dl>

Arguments

getNumeratorParts — getNumeratorParts

<dl>

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


<dt>baseLearnerIndex</dt>
<dd>An integer indicating which of the 
(possibly multiple) log-ratios learned by codacore to be used.</dd>


<dt>boolean</dt>
<dd>Whether to return the parts in boolean form
(a vector of TRUE/FALSE) or to return the column names of
those parts directly.</dd>

</dl>

getNumeratorParts: getNumeratorParts

Description

Usage

Value

Arguments