rfCMA {CMA}R Documentation

Classification based on Random Forests

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.

For S4 method information, see rfCMA-methods

Usage

rfCMA(X, y, f, learnind, varimp = TRUE, seed = 111, ...)

Arguments

X Gene expression data. Can be one of the following:
  • A matrix. Rows correspond to observations, columns to variables.
  • A data.frame, when f is not missing (s. below).
  • An object of class ExpressionSet.
y Class labels. Can be one of the following:
  • A numeric vector.
  • A factor.
  • A character if X is an ExpressionSet that specifies the phenotype variable.
  • missing, if X is a data.frame and a proper formula f is provided.
WARNING: The class labels will be re-coded to range from 0 to K-1, where K is the total number of different classes in the learning set.
f A two-sided formula, if X is a data.frame. The left part correspond to class labels, the right to variables.
learnind An index vector specifying the observations that belong to the learning set. May be missing; in that case, the learning set consists of all observations and predictions are made on the learning set.
varimp Should variable importance measures be computed ? Defauls to TRUE.
seed Fix Random number generator seed to seed. This is useful to guarantee reproducibility of the results.
... Further arguments to be passed to randomForest from the package of the same name.

Value

If varimp, then an object of class clvarseloutput is returned, otherwise an object of class cloutput

Author(s)

Martin Slawski martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

References

Breiman, L. (2001)

Random Forest.

Machine Learning, 45:5-32.

See Also

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, scdaCMA, shrinkldaCMA, svmCMA

Examples

 ### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
### run random Forest
rfresult <- rfCMA(X=khanX, y=khanY, learnind=learnind, varimp = FALSE)
### show results
show(rfresult)
ftable(rfresult)
plot(rfresult)

[Package CMA version 1.0.0 Index]