kNN evaluation by CV
Evaluation for k-Nearest-Neighbors (kNN) classification by cross-validation
knnEval(X, grp, train, kfold = 10, knnvec = seq(2, 20, by = 2), plotit = TRUE,
legend = TRUE, legpos = "bottomright", ...)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 |
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.
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 |
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.