svmCMA: Support Vector Machine
In CMA: Synthesis of microarray-based classification

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

Calls the function svm from the package e1071 that provides an interface to the award-winning LIBSVM routines. For S4 method information, see svmCMA-methods

1	svmCMA(X, y, f, learnind, probability, models=FALSE,seed=341,...)

`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.
`probability`	logical indicating whether the model should allow for probability predictions.
`seed`	Fix random number generator for reproducibility.
`models`	a logical value indicating whether the model object shall be returned
`...`	Further arguments to be passed to `svm` from the package `e1071`

An object of class cloutput.

Contrary to the default settings in e1071:::svm, the used kernel is a linear kernel which has turned to be out a better default setting in the small sample, large number of predictors - situation, because additional nonlinearity is mostly not necessary there. It additionally avoids the tuning of a further kernel parameter gamma, s. help of the package e1071 for details.
Nevertheless, hyperparameter tuning concerning the parameter cost must usually be performed to obtain reasonale results, s. tune.

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Boser, B., Guyon, I., Vapnik, V. (1992)
A training algorithm for optimal margin classifiers.
Proceedings of the fifth annual workshop on Computational learning theory, pages 144-152, ACM Press.

Chang, Chih-Chung and Lin, Chih-Jen : LIBSVM: a library for Support Vector Machines http://www.csie.ntu.edu.tw/~cjlin/libsvm

Schoelkopf, B., Smola, A.J. (2002)
Learning with kernels. MIT Press, Cambridge, MA.

compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA

### 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 _untuned_linear SVM
svmresult <- svmCMA(X=golubX, y=golubY, learnind=learnind,probability=TRUE)
### show results
show(svmresult)
ftable(svmresult)
plot(svmresult)