CMA (version 1.30.0)

svmCMA: Support Vector Machine

Description

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

Usage

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

Arguments

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

Value

cloutput.

References

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.

See Also

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

Examples

### 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)