kknn (version 1.3.0)

train.kknn: Training kknn

Description

Training of kknn method via leave-one-out (train.kknn) or k-fold (cv.kknn) crossvalidation.

Usage

train.kknn(formula, data, kmax = 11, distance = 2, kernel = "optimal",
	ykernel = NULL, scale = TRUE, contrasts = c('unordered' = "contr.dummy",
	ordered = "contr.ordinal"), ...)
cv.kknn(formula, data, kcv = 10, ...)

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.

scale

logical, scale variable to have equal sd.

contrasts

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

...

Further arguments passed to or from other methods.

kcv

Number of partitions for k-fold cross validation.

Value

• train.kknn returns a list-object of class train.kknn including the components.
• MISCLASS
  Matrix of misclassification errors.
• MEAN.ABS
  Matrix of mean absolute errors.
• MEAN.SQU
  Matrix of mean squared errors.
• fitted.values
  List of predictions for all combinations of kernel and k.
• best.parameters
  List containing the best parameter value for kernel and k.
• response
  Type of response variable, one of continuous, nominal or ordinal.
• distance
  Parameter of Minkowski distance.
• call
  The matched call.
• terms
  The 'terms' object used.

Details

train.kknn performs leave-one-out crossvalidation and is computatioanlly very efficient. cv.kknn performs k-fold crossvalidation and is generally slower and does not yet contain the test of different models yet.

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, 40, 2733-2763. (avaialble from http://www.statslab.cam.ac.uk/~rjs57/Research.html)

See Also

kknn and simulation

Examples

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)