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 `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

    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(e0171)
    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")

[Package chemometrics version 0.6 Index]