fdaCMA-methods

fdaCMA,matrix,numeric,missing-method

fdaCMA,matrix,factor,missing-method

fdaCMA,data.frame,missing,formula-method

fdaCMA,ExpressionSet,character,missing-method


 Fisher's Linear Discriminant Analysis constructs a subspace of
 'optimal projections' in which classification is performed.
 The directions of optimal projections are computed by the
 function <code>cancor</code> from the package <code>stats</code>. For
 an exhaustive treatment, see e.g. Ripley (1996).


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

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

Methods

Fisher's Linear Discriminant Analysis constructs a subspace of
 'optimal projections' in which classification is performed.
 The directions of optimal projections are computed by the
 function <code>cancor</code> from the package <code>stats</code>. For
 an exhaustive treatment, see e.g. Ripley (1996).

fdaCMA-methods: Fisher's Linear Discriminant Analysis

Description

Arguments

Methods