klaR (version 0.6-8)

sknn: Simple k nearest Neighbours

Description

Function for simple knn classification.

Usage

sknn(x, ...)

## S3 method for class 'default':
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)

Arguments

x

matrix or data frame containing the explanatory variables (required, if formula is not given).

grouping

factor specifying the class for each observation (required, if formula is not given).

formula

formula of the form groups ~ x1 + x2 + .... That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.

data

Data frame from which variables specified in formula are preferentially to be taken.

kn

Number of nearest neighbours to use.

gamma

gamma parameter for rbf in knn. If gamma=0 ordinary knn classification is used.

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 NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values

...

currently unused

Value

• A list containing the function call.

concept

• k nearest neighbour classification
• KNN

Details

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.

See Also

predict.sknn, knn

Examples

data(iris)
x <- sknn(Species ~ ., data = iris)
x <- sknn(Species ~ ., gamma = 4, data = iris)