Classification Error Rate (CER)

Description

Compute the classification error rate of two partitions.

Usage

CER(ind, true.ind,nob=length(ind))

Arguments

Value

Return a CER value. CER = 0 means perfect agreement between two partitions and CER = 1 means complete disagreement of two partitions. Note: 0 <= CER <= 1

Note

This function uses comb, which generates all combinations of the elements in the vector ind. For this reason, the function CER is not suitable for vector in a large dimension.

Author(s)

Yumi Kondo <y.kondo@stat.ubc.ca>

References

H. Chipman and R. Tibshirani. Hybrid hierarchical clustering with applications to microarray data. Biostatistics, 7(2):286-301, 2005.

Examples

vec1<-c(1,1,1,2,3,3,3,2,2)
vec2<-c(3,3,3,1,1,2,2,1,1)
CER(vec1,vec2)

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.

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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(RSKC)
Loading required package: flexclust
Loading required package: grid
Loading required package: lattice
Loading required package: modeltools
Loading required package: stats4
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RSKC/CER.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CER
> ### Title: Classification Error Rate (CER)
> ### Aliases: CER
> 
> ### ** Examples
> 
> vec1<-c(1,1,1,2,3,3,3,2,2)
> vec2<-c(3,3,3,1,1,2,2,1,1)
> CER(vec1,vec2)
[1] 0.1388889
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>