train.kknn: Training kknn

In kknn: Weighted k-Nearest Neighbors

Description

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

Usage

train.kknn(formula, data, kmax = 11, ks = NULL, 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, if ks is not specified.
ks	A vector specifying values of k. If not null, this takes precedence over kmax.
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.

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.

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.

Author(s)

Klaus P. Schliep [e-mail]

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)
## Not run: 
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)

## End(Not run)
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)

kknn documentation built on May 2, 2019, 3:26 a.m.