pls_lrCMA-methods

pls_lrCMA,matrix,numeric,missing-method

pls_lrCMA,matrix,factor,missing-method

pls_lrCMA,data.frame,missing,formula-method

pls_lrCMA,ExpressionSet,character,missing-method


 This method constructs a classifier that extracts
  Partial Least Squares components that form the the covariates
  in a binary logistic regression model.
  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_lrCMA-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_lrCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::pls_lrCMA">pls_lrCMA</a></code>

Methods

This method constructs a classifier that extracts
  Partial Least Squares components that form the the covariates
  in a binary logistic regression model.
  The Partial Least Squares components are computed by the package
  <code>plsgenomics</code>.

pls_lrCMA-methods: Partial Least Squares followed by logistic regression

Description

Arguments

Methods