## Usage

rknnBeg(data, y, k = 1, r = 500, mtry = trunc(sqrt(ncol(data))), fixed.partition = FALSE, pk = 0.5, stopat = 4, cluster=NULL, seed = NULL)
rknnBel(data, y, k = 1, r = 500, mtry = trunc(sqrt(ncol(data))), fixed.partition = FALSE, d = 1, stopat = 4, cluster=NULL, seed = NULL)

## Arguments

data

An n x p numeric design matrix.

y

A vector of responses. For a numeric vector, Random Knn regression
is performed. For a factor, Random classification is performed.

k

An integer for the number of nearest neighbors.

r

An integer for the number of base KNN models.

mtry

Number of features to be drawn for each KNN.

fixed.partition

Logical. Use fixed partition of dynamic partition of the data into training and testing subsets for each KNN.

pk

A real number between 0 and to indicate the proportion of the feature set to be kept in each step.

d

A integer to indicate the number of features to be dropped in each step.

stopat

an integer for the minimum number of variables.

cluster

An object of class `c("SOCKcluster", "cluster")'