rfCMA-methods: Classification based on Random Forests

In CMA: Synthesis of microarray-based classification

Description

Random Forests were proposed by Breiman (2001) and are implemented in the package randomForest.

In this package, they can as well be used to rank variables according to their importance, s. GeneSelection.

Methods

X = "matrix", y = "numeric", f = "missing"

signature 1

X = "matrix", y = "factor", f = "missing"

signature 2

X = "data.frame", y = "missing", f = "formula"

signature 3

X = "ExpressionSet", y = "character", f = "missing"

signature 4

For references, further argument and output information, consult rfCMA

CMA documentation built on Nov. 8, 2020, 5:02 p.m.