Interface to SVMlight
Function to call SVMlight from R for classification. Multiple group classification is done with the one-against-rest partition of data.
svmlight(x, ...) ## S3 method for class 'default': svmlight(x, grouping, temp.dir = NULL, pathsvm = NULL, del = TRUE, type = "C", class.type = "oaa", svm.options = NULL, prior = NULL, out = FALSE, ...) ## S3 method for class 'data.frame': svmlight(x, ...) ## S3 method for class 'matrix': svmlight(x, grouping, ..., subset, na.action = na.fail) ## S3 method for class 'formula': svmlight(formula, data = NULL, ..., subset, na.action = na.fail)
- matrix or data frame containing the explanatory variables
formulais not given).
- factor specifying the class for each observation
formulais not given).
- formula of the form
groups ~ x1 + x2 + .... That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.
- Data frame from which variables specified in
formulaare preferentially to be taken.
- directory for temporary files.
- Path to SVMlight binaries (required, if path is unknown by the OS).
- Logical: whether to delete temporary files
- Multiclass scheme to use. See details.
- Optional parameters to SVMlight.
For further details see:
How to useon http://svmlight.joachims.org/.
- A Priori probabilities of classes.
- Logical: whether SVMlight output ahouild be printed on console (only for Windows OS.)
- An index vector specifying the cases to be used in the training sample. (Note: If given, this argument must be named.)
- specify the action to be taken if
NAs are found. The default action is for the procedure to fail. An alternative is
na.omit, which leads to rejection of cases with missing values
- currently unused
Function to call SVMlight from R for classification (
SVMlight is an implementation of Vapnik's Support Vector Machine. It
is written in C by Thorsten Joachims. On the homepage (see below) the
source-code and several binaries for SVMlight are available. If more
then two classes are given the SVM is learned by the one-against-all
class.type="oaa"). That means that each class is trained against the other K-1
classes. The class with the highest decision function in the SVM
wins. So K SVMs have to be learned.
class.type="oao" each class is tested against every other and the final class is elected
by a majority vote.
type="R" a SVM Regression is performed.
- A list containing the function call and the result of SVMlight.
- Support vector machines
## Only works if the svmlight binaries are in the path. data(iris) x <- svmlight(Species ~ ., data = iris) ## Using RBF-Kernel with gamma=0.1: data(B3) x <- svmlight(PHASEN ~ ., data = B3, svm.options = "-t 2 -g 0.1")