train.kknn: Training kknn

Description

Training of kknn method via leave-one-out crossvalidation.

Usage

train.kknn(formula, data, kmax = 11, distance = 2, kernel = "triangular",
	ykernel = NULL, contrasts = c('unordered' = "contr.dummy",
	ordered = "contr.ordinal"), ...)

Arguments

formula

A formula object.

data

Matrix or data frame.

kmax

Maximum number of k.

distance

Parameter of Minkowski distance.

kernel

Kernel to use. Possible choices are "rectangular" (which is standard unweighted knn), "triangular", "epanechnikov" (or beta(2,2)), "biweight" (or beta(3,3)), "triweight" (or beta(4,4)), "cos", "inv", "gaussian" and "optimal".

ykernel

Window width of an y-kernel, especially for prediction of ordinal classes.

contrasts

A vector containing the 'unordered' and 'ordered' contrasts to use.

...

Further arguments passed to or from other methods.

Value

train.kknn returns a list-object of class train.kknn including the components
MISCLASSMatrix of misclassification errors.
MEAN.ABSMatrix of mean absolute errors.
MEAN.SQUMatrix of mean squared errors.
fitted.valuesList of predictions for all combinations of kernel and k.
best.parametersList containing the best parameter value for kernel and k.
responseType of response variable, one of continuous, nominal or ordinal.
distanceParameter of Minkowski distance.
callThe matched call.
termsThe 'terms' object used.

References

Hechenbichler K. and Schliep K.P. (2004) Weighted k-Nearest-Neighbor Techniques and Ordinal Classification, Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich (http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper399.ps) Hechenbichler K. (2005) Ensemble-Techniken und ordinale Klassifikation, PhD-thesis Samworth, R.J. (2012) Optimal weighted nearest neighbour classifiers. Annals of Statistics, to appear. (avaialble from http://www.statslab.cam.ac.uk/~rjs57/Research.html)

Examples

Run this code

library(kknn)
data(miete)
(train.con <- train.kknn(nmqm ~ wfl + bjkat + zh, data = miete, 
	kmax = 25, kernel = c("rectangular", "triangular", "epanechnikov",
	"gaussian", "rank", "optimal")))
plot(train.con)
(train.ord <- train.kknn(wflkat ~ nm + bjkat + zh, miete, kmax = 25,
 	kernel = c("rectangular", "triangular", "epanechnikov", "gaussian", 
 	"rank", "optimal")))
plot(train.ord)
(train.nom <- train.kknn(zh ~ wfl + bjkat + nmqm, miete, kmax = 25, 
	kernel = c("rectangular", "triangular", "epanechnikov", "gaussian", 
	"rank", "optimal")))
plot(train.nom)
data(glass)
glass <- glass[,-1]
(fit.glass1 <- train.kknn(Type ~ ., glass, kmax = 15, kernel = 
	c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 1))
(fit.glass2 <- train.kknn(Type ~ ., glass, kmax = 15, kernel = 
	c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 2))
plot(fit.glass1)
plot(fit.glass2)