svmEval {chemometrics} | R Documentation |
Support Vector Machine evaluation by CV
Description
Evaluation for Support Vector Machines (SVM) by cross-validation
Usage
svmEval(X, grp, train, kfold = 10, gamvec = seq(0, 10, by = 1), kernel = "radial",
degree = 3, plotit = TRUE, legend = TRUE, legpos = "bottomright", ...)
Arguments
X |
standardized complete X data matrix (training and test data) |
grp |
factor with groups for complete data (training and test data) |
train |
row indices of X indicating training data objects |
kfold |
number of folds for cross-validation |
gamvec |
range for gamma-values, see |
kernel |
kernel to be used for SVM, should be one of "radial", "linear",
"polynomial", "sigmoid", default to "radial", see |
degree |
degree of polynome if kernel is "polynomial", default to 3, see
|
plotit |
if TRUE a plot will be generated |
legend |
if TRUE a legend will be added to the plot |
legpos |
positioning of the legend in the plot |
... |
additional plot arguments |
Details
The data are split into a calibration and a test data set (provided by "train"). Within the calibration set "kfold"-fold CV is performed by applying the classification method to "kfold"-1 parts and evaluation for the last part. The misclassification error is then computed for the training data, for the CV test data (CV error) and for the test data.
Value
trainerr |
training error rate |
testerr |
test error rate |
cvMean |
mean of CV errors |
cvSe |
standard error of CV errors |
cverr |
all errors from CV |
gamvec |
range for gamma-values, taken from input |
Author(s)
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
References
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
See Also
Examples
data(fgl,package="MASS")
grp=fgl$type
X=scale(fgl[,1:9])
k=length(unique(grp))
dat=data.frame(grp,X)
n=nrow(X)
ntrain=round(n*2/3)
require(e1071)
set.seed(143)
train=sample(1:n,ntrain)
ressvm=svmEval(X,grp,train,gamvec=c(0,0.05,0.1,0.2,0.3,0.5,1,2,5),
legpos="topright")
title("Support vector machines")