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,type=1,scale=FALSE,importance=TRUE, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
WARNING: The class labels will be re-coded to
range from |
f |
A two-sided formula, if |
learnind |
An index vector specifying the observations that
belong to the learning set. May be |
varimp |
Should additional information for variable selection be provided ? Defaults to |
seed |
Fix Random number generator seed to |
models |
a logical value indicating whether the model object shall be returned |
type |
Parameter passed to function |
scale |
Parameter passed to function |
importance |
Parameter passed to function |
... |
Further arguments to be passed to |
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)