Function for simple knn classification.
sknn(x, ...)# S3 method for default
sknn(x, grouping, kn = 3, gamma=0, ...)
# S3 method for data.frame
sknn(x, ...)
# S3 method for matrix
sknn(x, grouping, ..., subset, na.action = na.fail)
# S3 method for formula
sknn(formula, data = NULL, ..., subset, na.action = na.fail)
A list containing the function call.
matrix or data frame containing the explanatory variables
(required, if formula
is not given).
factor specifying the class for each observation
(required, if formula
is not given).
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 frame from which variables specified in formula
are preferentially to be taken.
Number of nearest neighbours to use.
gamma parameter for rbf in knn. If gamma=0
ordinary knn classification is used.
An index vector specifying the cases to be used in the training sample. (Note: If given, this argument must be named.)
specify the action to be taken if NA
s 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 on any required variable. (Note: If given, this
argument must be named.)
currently unused
Karsten Luebke, karsten.luebke@fom.de
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.
predict.sknn
, knn
data(iris)
x <- sknn(Species ~ ., data = iris)
x <- sknn(Species ~ ., gamma = 4, data = iris)
Run the code above in your browser using DataLab