
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, ...)
matrix
. Rows correspond to observations, columns to variables.
data.frame
, when f
is not missing (s. below).
ExpressionSet
.
numeric
vector.
factor
.
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.
X
is a data.frame
. The
left part correspond to class labels, the right to variables.missing
;
in that case, the learning set consists of all
observations and predictions are made on the
learning set.seed
. This is
useful to guarantee reproducibility of the results,
due to the random component in the random Forest.randomForests
from the
package of the same name.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)
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