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Dear R-users, I have one question about using SVD to get an inverse matrix of covariance matrix Sometimes I met many singular values d are close to 0: look this example $d [1] 4.178853e+00 2.722005e+00 2.139863e+00 1.867628e+00 1.588967e+00 [6] 1.401554e+00 1.256964e+00 1.185750e+00 1.060692e+00 9.932592e-01 [11] 9.412768e-01 8.530497e-01 8.211395e-01 8.077817e-01 7.706618e-01 [16]

Hi,I want to generate a dataset, which have more than one clusters (say 2) and the elements in each cluster have high correlation (say 0.85) andelements among different clusters have low (say 0.1) or zero correlation.The correlation structure of final dataset should have a block-diagonal structure, that likes 0.85 0.85 .. 0.85 0 0 0 0 ...0 0 0...0 0 0

Dear All, I am using Silhouette to estimate the number of clusters in a microarray dataset. Initially, I used the iris data to test my piece of code as follows: library(cluster) data(iris) mydata<-iris[,1:4] maxk<-15 # at most 15 clusters myindex<-rep(0,maxk) # hold the si values for each k clusters mdist<-1-cor(t(mydata)) #dissimlarity