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, ...)
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. |
... |
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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)