shrinkldaCMA-methods

shrinkldaCMA,matrix,numeric,missing-method

shrinkldaCMA,matrix,factor,missing-method

shrinkldaCMA,data.frame,missing,formula-method

shrinkldaCMA,ExpressionSet,character,missing-method

Linear Discriminant Analysis combined with the
             James-Stein-Shrinkage approach of Schaefer and Strimmer (2005)
             for the covariance matrix.<p></p><p>             Currently still an experimental version.
             For <code>S4</code> method information, see <a rd-options="" href="/link/shrinkldaCMA-methods?package=CMA&version=1.30.0" data-mini-rdoc="CMA::shrinkldaCMA-methods">shrinkldaCMA-methods</a></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

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

Methods

Linear Discriminant Analysis combined with the
             James-Stein-Shrinkage approach of Schaefer and Strimmer (2005)
             for the covariance matrix.

             Currently still an experimental version.
             For <code>S4</code> method information, see <a href="shrinkldaCMA-methods" rd-options="">shrinkldaCMA-methods</a>

shrinkldaCMA-methods: Shrinkage linear discriminant analysis

Description

Arguments

Methods