ElasticNetCMA-methods

ElasticNetCMA,matrix,numeric,missing-method

ElasticNetCMA,matrix,factor,missing-method

ElasticNetCMA,data.frame,missing,formula-method

ElasticNetCMA,ExpressionSet,character,missing-method


 Zou and Hastie (2004) proposed a combined L1/L2 penalty
 for regularization and variable selection.
 The Elastic Net penalty encourages a grouping
 effect, where strongly correlated predictors tend to be in or out of the model together.
 The computation is done with the function <code>glmpath</code> from the package
 of the same name.


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

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

Methods

Zou and Hastie (2004) proposed a combined L1/L2 penalty
 for regularization and variable selection.
 The Elastic Net penalty encourages a grouping
 effect, where strongly correlated predictors tend to be in or out of the model together.
 The computation is done with the function <code>glmpath</code> from the package
 of the same name.

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ElasticNetCMA-methods: Classfication and variable selection by the ElasticNet

Description

Arguments

Methods