knnEval {chemometrics} | R Documentation

## kNN evaluation by CV

### Description

Evaluation for k-Nearest-Neighbors (kNN) classification by cross-validation

### Usage

    knnEval(X, grp, train, kfold = 10, knnvec = seq(2, 20, by = 2), 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
`knnvec` | range for k for the evaluation of kNN
`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
`knnvec` | range for k for the evaluation of kNN, 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

`knn`

### 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(class)
    set.seed(123)
    train=sample(1:n,ntrain)
    resknn=knnEval(X,grp,train,knnvec=seq(1,30,by=1),legpos="bottomright")
    title("kNN classification")

[Package chemometrics version 0.6 Index]