kknn (version 1.3.1)

# kknn: Weighted k-Nearest Neighbor Classifier

## Description

Performs k-nearest neighbor classification of a test set using a training set. For each row of the test set, the k nearest training set vectors (according to Minkowski distance) are found, and the classification is done via the maximum of summed kernel densities. In addition even ordinal and continuous variables can be predicted.

## Usage

kknn(formula = formula(train), train, test, na.action = na.omit(), k = 7, distance = 2, kernel = "optimal", ykernel = NULL, scale=TRUE, contrasts = c('unordered' = "contr.dummy", ordered = "contr.ordinal")) kknn.dist(learn, valid, k = 10, distance = 2)

## Arguments

formula
A formula object.
train
Matrix or data frame of training set cases.
test
Matrix or data frame of test set cases.
learn
Matrix or data frame of training set cases.
valid
Matrix or data frame of test set cases.
na.action
A function which indicates what should happen when the data contain 'NA's.
k
Number of neighbors considered.
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", "rank" 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.

## Value

kknn returns a list-object of class kknn including the components
fitted.values
Vector of predictions.
CL
Matrix of classes of the k nearest neighbors.
W
Matrix of weights of the k nearest neighbors.
D
Matrix of distances of the k nearest neighbors.
C
Matrix of indices of the k nearest neighbors.
prob
Matrix of predicted class probabilities.
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

This nearest neighbor method expands knn in several directions. First it can be used not only for classification, but also for regression and ordinal classification. Second it uses kernel functions to weight the neighbors according to their distances. In fact, not only kernel functions but every monotonic decreasing function \$f(x) for all x>0\$ will work fine.

The number of neighbours used for the "optimal" kernel should be \$ [ (2(d+4)/(d+2))^(d/(d+4)) k ]\$, where k is the number that would be used for unweighted knn classification, i.e. kernel="rectangular". This factor \$(2(d+4)/(d+2))^(d/(d+4))\$ is between 1.2 and 2 (see Samworth (2012) for more details).

## 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)

## Examples

Run this code
library(kknn)

data(iris)
m <- dim(iris)[1]
val <- sample(1:m, size = round(m/3), replace = FALSE,
prob = rep(1/m, m))
iris.learn <- iris[-val,]
iris.valid <- iris[val,]
iris.kknn <- kknn(Species~., iris.learn, iris.valid, distance = 1,
kernel = "triangular")
summary(iris.kknn)
fit <- fitted(iris.kknn)
table(iris.valid\$Species, fit)
pcol <- as.character(as.numeric(iris.valid\$Species))
pairs(iris.valid[1:4], pch = pcol, col = c("green3", "red")
[(iris.valid\$Species != fit)+1])

data(ionosphere)
ionosphere.learn <- ionosphere[1:200,]
ionosphere.valid <- ionosphere[-c(1:200),]
fit.kknn <- kknn(class ~ ., ionosphere.learn, ionosphere.valid)
table(ionosphere.valid\$class, fit.kknn\$fit)
(fit.train1 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15,
kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 1))
table(predict(fit.train1, ionosphere.valid), ionosphere.valid\$class)
(fit.train2 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15,
kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 2))
table(predict(fit.train2, ionosphere.valid), ionosphere.valid\$class)

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