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