treeEval {chemometrics} | R Documentation

## Classification tree evaluation by CV

### Description

Evaluation for classification trees by cross-validation

### Usage

    treeEval(X, grp, train, kfold = 10, cp = seq(0.01, 0.1, by = 0.01), 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
`cp` | range for tree complexity parameter, see `rpart`
`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
`cp` | range for tree complexity parameter, 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

`rpart`

### 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(rpart)
    set.seed(123)
    train=sample(1:n,ntrain)
    par(mar=c(4,4,3,1))
    restree=treeEval(X,grp,train,cp=c(0.01,0.02:0.05,0.1,0.15,0.2:0.5,1))
    title("Classification trees")

[Package chemometrics version 0.6 Index]