| CER {RSKC} | R Documentation |
Classification Error Rate (CER)
Description
Compute the classification error rate of two partitions.
Usage
CER(ind, true.ind,nob=length(ind))
Arguments
ind |
Vector, containing the cluster labels of each case of a partition 1. |
true.ind |
Vector, containing the cluster labels of each case of a partition 2. |
nob |
The number of cases (the length of the vector ind and true ind) |
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)