scdaCMA-methods

scdaCMA,matrix,numeric,missing-method

scdaCMA,matrix,factor,missing-method

scdaCMA,data.frame,missing,formula-method

scdaCMA,ExpressionSet,character,missing-method


  The nearest shrunken centroid classification algorithm is
  detailly described in Tibshirani et al. (2002).<p></p><p>  It is widely known under the name PAM (prediction analysis for microarrays),
  which can also be found in the package <code>pamr</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

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

Methods

The nearest shrunken centroid classification algorithm is
  detailly described in Tibshirani et al. (2002).

  It is widely known under the name PAM (prediction analysis for microarrays),
  which can also be found in the package <code>pamr</code>.

Last chance! 50% off unlimited learning

scdaCMA-methods: Shrunken Centroids Discriminant Analysis

Description

Arguments

Methods