rknn: Random KNN Classification and Regression

In rknn: Random KNN Classification and Regression

Description

Random KNN Classification and Regression

Usage

rknn(data, newdata, y, k = 1, r = 500, mtry = trunc(sqrt(ncol(data))), 
        cluster = NULL, seed = NULL)
rknn.cv(data, y, k = 1, r = 500, mtry=trunc(sqrt(ncol(data))), 
        cluster=NULL, seed = NULL)
rknnReg(data, newdata, y, k=1, r=500,  mtry=trunc(sqrt(ncol(data))), 
        cluster=NULL, seed=NULL)	

Arguments

data	A training dataset.
newdata	A testing dataset.
y	A vector of responses.
k	Number of nearest neighbors.
r	Number of KNNs.
mtry	Number of features to be drawn for each KNN.
cluster	An object of class ‘c("SOCKcluster", "cluster")’
seed	An integer seed.

Value

Return a RandomKNN object.

Author(s)

Shengqiao Li<[e-mail]>

rknn documentation built on May 2, 2019, 12:35 p.m.