svmCMA {CMA} | R Documentation |
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
svmCMA(X, y, f, learnind, ...)
X |
Gene expression data. Can be one of the following:
|
y |
Class labels. Can be one of the following:
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. |
... |
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
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) ### show results show(svmresult) ftable(svmresult) plot(svmresult)