sknn(x, ...)
"sknn"(x, grouping, kn = 3, gamma=0, ...)
"sknn"(x, ...)
"sknn"(x, grouping, ..., subset, na.action = na.fail)
"sknn"(formula, data = NULL, ..., subset, na.action = na.fail)
formula
is not given).formula
is not given).groups ~ x1 + x2 + ...
.
That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.formula
are preferentially to be taken.gamma=0
ordinary knn classification is used.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.) 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)
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