supervisedPCA

Performs supervised principle component analysis (PCA) after filtering
dataset to help determine whether filtering has been useful for separating
samples according to the outcome variable.

Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.

Myles Lewis

nestedcv

Nested Cross-Validation with 'glmnet' and 'caret'

Athina Spiliopoulou

Cankut Cubuk

David Rios Santini

Katriona Goldmann

Ryan C. Thompson

supervisedPCA function

<dl><dt>y</dt>
<dd>Response vector</dd>
<dt>x</dt>
<dd>Matrix of predictors</dd>
<dt>filterFUN</dt>
<dd>Filter function, e.g. ttest_filter or relieff_filter.
Any function can be provided and is passed <code>y</code> and <code>x</code>. Must return a
character vector with names of filtered predictors.</dd>
<dt>filter_options</dt>
<dd>List of additional arguments passed to the filter
function specified by <code>filterFUN</code>.</dd>
<dt>plot</dt>
<dd>Logical whether to plot a ggplot2 object or return the PC scores</dd>
<dt>...</dt>
<dd>Optional arguments passed to <code><a href="/link/princomp()?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::princomp()">princomp()</a></code></dd></dl>

Arguments

Supervised PCA plot — supervisedPCA

<dl>

<dt>y</dt>
<dd>Response vector</dd>


<dt>x</dt>
<dd>Matrix of predictors</dd>


<dt>filterFUN</dt>
<dd>Filter function, e.g. ttest_filter or relieff_filter.
Any function can be provided and is passed <code>y</code> and <code>x</code>. Must return a
character vector with names of filtered predictors.</dd>


<dt>filter_options</dt>
<dd>List of additional arguments passed to the filter
function specified by <code>filterFUN</code>.</dd>


<dt>plot</dt>
<dd>Logical whether to plot a ggplot2 object or return the PC scores</dd>


<dt>...</dt>
<dd>Optional arguments passed to <code><a href='https://rdrr.io/r/stats/princomp.html'>princomp()</a></code></dd>

</dl>

supervisedPCA: Supervised PCA plot

Description

Usage

Value

Arguments