Support Vector Machine evaluation by CV

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Description

Evaluation for Support Vector Machines (SVM) by cross-validation

Usage

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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 svm

kernel

kernel to be used for SVM, should be one of "radial", "linear", "polynomial", "sigmoid", default to "radial", see svm

degree

degree of polynome if kernel is "polynomial", default to 3, see svm

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

svm

Examples

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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")

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