pls_rfCMA {CMA} | R Documentation |
This method constructs a classifier that extracts
Partial Least Squares components used to generate Random Forests, s. rfCMA
.
For S4
method information, see pls_rfCMA-methods
.
pls_rfCMA(X, y, f, learnind, comp = 2 * nlevels(as.factor(y)), seed = 111,models=FALSE, ...)
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 |
comp |
Number of Partial Least Squares components to extract. Default ist two times the number of different classes. |
seed |
Fix Random number generator seed to |
models |
a logical value indicating whether the model object shall be returned |
... |
Further arguments to be passed to |
An object of class cloutput
.
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Boulesteix, A.L., Strimmer, K. (2007).
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.
Briefings in Bioinformatics 7:32-44.
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, shrinkldaCMA
, svmCMA
### load Golub AML/ALL data data(golub) ### extract class labels golubY <- golub[,1] ### extract gene expression golubX <- as.matrix(golub[,-1]) ### select learningset ratio <- 2/3 set.seed(111) learnind <- sample(length(golubY), size=floor(ratio*length(golubY))) ### run PLS, combined with Random Forest #result <- pls_rfCMA(X=golubX, y=golubY, learnind=learnind) ### show results #show(result) #ftable(result) #plot(result)