svm: Classification based on support vector machines using...
In MAclinical: Class prediction based on microarray data and clinical parameters

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

This function builds a prediction rule based on the learning data (microarray predictors only) and applies it to the test data. Prediction is performed based on support vector machines. The function svm_x uses the function svm from the package e1071.

1	svm_x(Xlearn,Zlearn=NULL,Ylearn,Xtest,Ztest=NULL,ordered=NULL,nbgene=NULL,...)

`Xlearn`	A nlearn x p matrix giving the microarray predictors for the learning data set.
`Zlearn`	A nlearn x q matrix giving the clinical predictors for the learning data set. This argument is ignored.
`Ylearn`	A numeric vector of length nlearn giving the class membership of the learning observations, coded as 0,1.
`Xtest`	A ntest x p matrix giving the microarray predictors for the test data set.
`Ztest`	A ntest x q matrix giving the clinical predictors for the test data set. This argument is ignored.
`ordered`	A vector of length p giving the order of the microarray predictors in terms of relevance for prediction. For instance, if the three first elements of `ordered` are 30,2,2400, it means that the most relevant genes are the genes in the 30th, 2nd and 2400th columns of the gene expression data matrix `Xlearn`. Note: if `ordered=NULL`, the columns of `Xlearn` and `Xtest` are assumed to be already ordered.
`nbgene`	The number of genes to be selected for use in dimension reduction. Default is `nbgene=NULL`, in which case all genes are used.
`...`	Other arguments to be passed to the function `svm` from the `e1071` package.

This function is included in the package for comparison.

A list with the element

prediction

A numeric vector of length nrow(Xtest) giving the predicted class for each observation from the test data set.

Anne-Laure Boulesteix (http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/eng.html)

Boulesteix AL, Porzelius C, Daumer M, 2008. Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value. Bioinformatics 24:1698-1706.

testclass, testclass_simul, simulate, plsrf_x_pv, plsrf_xz, plsrf_xz_pv, rf_z, logistic_z.

# load MAclinical library
# library(MAclinical)

# Generating xlearn, zlearn, ylearn, xtest, ztest
xlearn<-matrix(rnorm(3000),30,100)
ylearn<-sample(0:1,30,replace=TRUE)
xtest<-matrix(rnorm(2000),20,100)

my.prediction1<-svm_x(Xlearn=xlearn,Ylearn=ylearn,Xtest=xtest)

ordered<-sample(100)
my.prediction2<-svm_x(Xlearn=xlearn,Ylearn=ylearn,Xtest=xtest,ordered=ordered,nbgene=20)