rfCMA-methods

rfCMA,matrix,numeric,missing-method

rfCMA,matrix,factor,missing-method

rfCMA,data.frame,missing,formula-method

rfCMA,ExpressionSet,character,missing-method

Random Forests were proposed by Breiman (2001)
             and are implemented in the package <code>randomForest</code>.<p></p><p>             In this package, they can as well be used to rank variables
             according to their importance, s. <code>GeneSelection</code>.</p>

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

rfCMA-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 references, further argument and output information, consult
<code><a rd-options="" href="/link/rfCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::rfCMA">rfCMA</a></code>

Methods

Random Forests were proposed by Breiman (2001)
             and are implemented in the package <code>randomForest</code>.

             In this package, they can as well be used to rank variables
             according to their importance, s. <code>GeneSelection</code>.

rfCMA-methods: Classification based on Random Forests

Description

Arguments

Methods