sknn {klaR} | R Documentation |
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 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 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.) |
... |
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, luebke@statistik.tu-dortmund.de
data(iris) x <- sknn(Species ~ ., data = iris) x <- sknn(Species ~ ., gamma = 4, data = iris)