simulateHTS

This function simulates a set of (x, y) pairs.
The covariates x are compositional, meaning they only
carry relative information.
The response y is a binary indicator.
The rule linking x and y can be a balance or an amalgamation.

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

simulateHTS function

<dl><dt>n</dt>
<dd>Number of observations.</dd>
<dt>p</dt>
<dd>Number of covariates.</dd>
<dt>outputType</dt>
<dd>A string indicating 'binary' or 'continuous'.</dd>
<dt>logratio</dt>
<dd>A string indicating 'simple', 'balance', or 
'amalgamation'.</dd></dl>

Arguments

simulateHTS — simulateHTS

<dl>

<dt>n</dt>
<dd>Number of observations.</dd>


<dt>p</dt>
<dd>Number of covariates.</dd>


<dt>outputType</dt>
<dd>A string indicating 'binary' or 'continuous'.</dd>


<dt>logratio</dt>
<dd>A string indicating 'simple', 'balance', or 
'amalgamation'.</dd>

</dl>

simulateHTS: simulateHTS

Description

Usage

Value

Arguments