k-Nearest Neighbour Classification

$k$-nearest neighbour classification with an interface compatible to bagging and errorest.

Usage

ipredknn(formula, data, subset, na.action, k=5, ...)

Arguments

formula: a formula of the form lhs ~ rhs where lhs is the response variable and rhs a set of predictors.
data: optional data frame containing the variables in the model formula.
subset: optional vector specifying a subset of observations to be used.
na.action: function which indicates what should happen when the data contain NAs.
k: number of neighbours considered, defaults to 5.
...: additional parameters.

Details

This is a wrapper to knn in order to be able to use k-NN in bagging and errorest.

Value

An object of class ipredknn. See predict.ipredknn.

Examples


library("mlbench")
learn <- as.data.frame(mlbench.twonorm(300))

mypredict.knn <- function(object, newdata) 
                   predict.ipredknn(object, newdata, type="class")

errorest(classes ~., data=learn, model=ipredknn, 
         predict=mypredict.knn)
#> 
#> Call:
#> errorest.data.frame(formula = classes ~ ., data = learn, model = ipredknn, 
#>     predict = mypredict.knn)
#> 
#> 	 10-fold cross-validation estimator of misclassification error 
#> 
#> Misclassification error:  0.02 
#>