plrCMA-methods

plrCMA,matrix,numeric,missing-method

plrCMA,matrix,factor,missing-method

plrCMA,data.frame,missing,formula-method

plrCMA,ExpressionSet,character,missing-method


 High dimensional logistic regression combined with an L2-type (Ridge-)penalty.
 Multiclass case is also possible.


multivariate

This package provides a comprehensive collection of various microarray-based classification algorithms
both from Machine Learning and Statistics.
Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed
combined or stepwise in a user-friendly environment.

Christoph Bernau

Synthesis of microarray-based classification

plrCMA-methods function

<dl> <dt>X = "matrix", y = "numeric", f = "missing"</dt><dd>signature 1</dd> <dt>X = "matrix", y = "factor", f = "missing"</dt><dd>signature 2</dd> <dt>X = "data.frame", y = "missing", f = "formula"</dt><dd>signature 3</dd> <dt>X = "ExpressionSet", y = "character", f = "missing"</dt><dd>signature 4</dd> </dl> For further argument and output information, consult
<code><a rd-options="" href="/link/plrCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::plrCMA">plrCMA</a></code>.

Methods

High dimensional logistic regression combined with an L2-type (Ridge-)penalty.
 Multiclass case is also possible.

plrCMA-methods: L2 penalized logistic regression

Description

Arguments

Methods