treeEval: Classification tree evaluation by CV

In chemometrics: Multivariate Statistical Analysis in Chemometrics

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 <[e-mail]>

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")

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.