gknn  R Documentation 
gknn
is an implementation of the knearest neighbours algorithm making use of general distance measures. A formula interface is provided.
## S3 method for class 'formula'
gknn(formula, data = NULL, ..., subset, na.action = na.pass, scale = TRUE)
## Default S3 method:
gknn(x, y, k = 1, method = NULL,
scale = TRUE, use_all = TRUE,
FUN = mean, ...)
## S3 method for class 'gknn'
predict(object, newdata,
type = c("class", "votes", "prob"),
...,
na.action = na.pass)
formula 
a symbolic description of the model to be fit. 
data 
an optional data frame containing the variables in the model. By default the variables are taken from the environment which ‘gknn’ is called from. 
x 
a data matrix. 
y 
a response vector with one label for each row/component of

k 
number of neighbours considered. 
scale 
a logical vector indicating the variables to be
scaled. If 
method 
Argument passed to 
use_all 
controls handling of ties. If true, all distances equal to the kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbours. 
FUN 
function used to aggregate the k nearest target values in case of regression. 
object 
object of class 
newdata 
matrix or data frame with new instances. 
type 
character specifying the return type in case of class
predictions: for 
... 
additional parameters passed to 
subset 
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) 
na.action 
A function to specify the action to be taken if 
For gknn()
, an object of class "gknn"
containing the data and the specified parameters. For predict.gknn()
, a vector of predictions, or a matrix with votes for all classes. In case of an overall class tie, the predicted class is chosen by random.
David Meyer (David.Meyer@Rproject.org)
dist
(in package proxy)
data(iris)
model < gknn(Species ~ ., data = iris)
predict(model, iris[c(1, 51, 101),])
test = c(45:50, 95:100, 145:150)
model < gknn(Species ~ ., data = iris[test,], k = 3, method = "Manhattan")
predict(model, iris[test,], type = "votes")
model < gknn(Species ~ ., data = iris[test], k = 3, method = "Manhattan")
predict(model, iris[test,], type = "prob")
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