R: Backward Elimination Feature Selection with Random KNN

rknnBeg {rknn} | R Documentation

## Backward Elimination Feature Selection with Random KNN

### Description

Recursive Backward Elimination Feature Selection with Random KNN

### 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")’
`seed` | An integer seed.

### Author(s)

Shengqiao Li<lishengqiao@yahoo.com>

[Package rknn version 1.2-1 Index]