Simple k nearest Neighbours
Function for simple knn classification.
sknn(x, ...) ## Default S3 method: sknn(x, grouping, kn = 3, gamma=0, ...) ## S3 method for class 'data.frame' sknn(x, ...) ## S3 method for class 'matrix' sknn(x, grouping, ..., subset, na.action = na.fail) ## S3 method for class 'formula' sknn(formula, data = NULL, ..., subset, na.action = na.fail)
x |
matrix or data frame containing the explanatory variables
(required, if |
grouping |
factor specifying the class for each observation
(required, if |
formula |
formula of the form |
data |
Data frame from which variables specified in |
kn |
Number of nearest neighbours to use. |
gamma |
gamma parameter for rbf in knn. If |
subset |
An index vector specifying the cases to be used in the training sample. (Note: If given, this argument must be named.) |
na.action |
specify the action to be taken if |
... |
currently unused |
If gamma>0 an gaussian like density is used to weight the classes of the kn nearest neighbors.
weight=exp(-gamma*distance). This is similar to an rbf kernel.
If the distances are large it may be useful to scale the data first.
A list containing the function call.
Karsten Luebke, karsten.luebke@fom.de
data(iris) x <- sknn(Species ~ ., data = iris) x <- sknn(Species ~ ., gamma = 4, data = iris)
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