Cross-Validation with k-Nearest Neighbors Classifier.

Description

This is function performs a 10-fold cross validation on a given data set using k nearest neighbors (kNN) classifier. The output is a vector of predicted labels.

Usage

KNN.CV(x,cl,constrain,kn=10)

Arguments

Details

Details are described in Cover, et al. (1967).

Value

The function returns a vector of predicted labels.

Author(s)

Stefano Cacciatore and Leonardo Tenori

References

Cover TM, Hart PE.
TNearest neighbor pattern classification.
IEEE Trans Inf Theory 1967;13(1):21-7.

See Also

PLS.SVM.CV,PCA.CA.KNN.CV

Examples

data(iris)
u=iris[,-5]
class=as.factor(iris[,5])
results=KNN.CV(u,class,1:length(class))
levels(results)=levels(class)
table(results,class)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(KODAMA)
Loading required package: e1071
Loading required package: plsgenomics
Loading required package: MASS
Loading required package: boot
Loading required package: parallel
Loading required package: class

Attaching package: 'KODAMA'

The following object is masked from 'package:plsgenomics':

    transformy

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/KODAMA/KNN.CV.Rd_%03d_medium.png", width=480, height=480)
> ### Name: KNN.CV
> ### Title: Cross-Validation with k-Nearest Neighbors Classifier.
> ### Aliases: KNN.CV
> ### Keywords: croos-validation
> 
> ### ** Examples
> 
> data(iris)
> u=iris[,-5]
> class=as.factor(iris[,5])
> results=KNN.CV(u,class,1:length(class))
> levels(results)=levels(class)
> table(results,class)
            class
results      setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         47         3
  virginica       0          3        47
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>