pls_rfCMA: Partial Least Squares followed by random forests
In CMA: Synthesis of microarray-based classification

Description Usage Arguments Value Author(s) References See Also Examples

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.

1	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: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`.
`y`	Class labels. Can be one of the following: A `numeric` vector. A `factor`. A `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.
`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.
`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 `seed`. This is useful to guarantee reproducibility of the results, due to the random component in the random Forest.
`models`	a logical value indicating whether the model object shall be returned
`...`	Further arguments to be passed to `randomForests` from the package of the same name.

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)