compBoostCMA-methods

compBoostCMA,matrix,numeric,missing-method

compBoostCMA,matrix,factor,missing-method

compBoostCMA,data.frame,missing,formula-method

compBoostCMA,ExpressionSet,character,missing-method

Roughly speaking, Boosting combines 'weak learners'
 in a weighted manner in a stronger ensemble. 'Weak learners' here consist of linear functions
 in one component (variable), as proposed by
 Buehlmann and Yu (2003). It also generates sparsity and can as well be as
 used for variable selection alone. (s. <code><a rd-options="" href="/link/GeneSelection?package=CMA&version=1.30.0" data-mini-rdoc="CMA::GeneSelection">GeneSelection</a></code>.)

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

compBoostCMA-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/compBoostCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::compBoostCMA">compBoostCMA</a>.</code>

Methods

Roughly speaking, Boosting combines 'weak learners'
 in a weighted manner in a stronger ensemble.

 'Weak learners' here consist of linear functions
 in one component (variable), as proposed by
 Buehlmann and Yu (2003).

 It also generates sparsity and can as well be as
 used for variable selection alone. (s. <code><a href="GeneSelection" rd-options="">GeneSelection</a></code>.)

compBoostCMA-methods: Componentwise Boosting

Description

Arguments

Methods