nm {klaR} | R Documentation |
Function for nearest mean classification.
nm(x, ...) ## Default S3 method: nm(x, grouping, gamma = 0, ...) ## S3 method for class 'data.frame': nm(x, ...) ## S3 method for class 'matrix': nm(x, grouping, ..., subset, na.action = na.fail) ## S3 method for class 'formula': nm(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. |
gamma |
gamma parameter for rbf weight of the distance to mean. If gamma=0 the posterior is 1 for the
nearest class (mean) and 0 else. |
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.) |
... |
nm
is calling sknn
with the class means as observations.
If gamma>0
a gaussian like density is used to weight the distance to the class means
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 and the class means (learn
)).
Karsten Luebke, luebke@statistik.tu-dortmund.de
data(B3) x <- nm(PHASEN ~ ., data = B3) x$learn x <- nm(PHASEN ~ ., data = B3, gamma = 0.1) predict(x)$post