| rfCMA {CMA} | R Documentation |
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
rfCMA(X, y, f, learnind, varimp = TRUE, seed = 111, models=FALSE, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
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. |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments to be passed to randomForest from the
package of the same name. |
If varimp, then an object of class clvarseloutput is returned,
otherwise an object of class cloutput
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Breiman, L. (2001)
Random Forest.
Machine Learning, 45:5-32.
compBoostCMA, dldaCMA, ElasticNetCMA,
fdaCMA, flexdaCMA, gbmCMA,
knnCMA, ldaCMA, LassoCMA,
nnetCMA, pknnCMA, plrCMA,
pls_ldaCMA, pls_lrCMA, pls_rfCMA,
pnnCMA, qdaCMA,
scdaCMA, shrinkldaCMA, svmCMA
### 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)