pls_rfCMA-methods

pls_rfCMA,matrix,numeric,missing-method

pls_rfCMA,matrix,factor,missing-method

pls_rfCMA,data.frame,missing,formula-method

pls_rfCMA,ExpressionSet,character,missing-method


  This method constructs a classifier that extracts
  Partial Least Squares components used to generate Random Forests, s. <code><a rd-options="" href="/link/rfCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::rfCMA">rfCMA</a></code>.
  The Partial Least Squares components are computed by the package
  <code>plsgenomics</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

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

Methods

This method constructs a classifier that extracts
  Partial Least Squares components used to generate Random Forests, s. <code><a href="rfCMA" rd-options="">rfCMA</a></code>.
  The Partial Least Squares components are computed by the package
  <code>plsgenomics</code>.

pls_rfCMA-methods: Partial Least Squares followed by random forests

Description

Arguments

Methods