I have been attempting to do some work using hclust, and have run into a (possibly subtle) problem. The background is that I constructed a dissimilarity matrix ``d1'' (it involved something called the ``Jaccard similarity coefficient''; I won't go into the details unless requested). I then did d2 <- as.dist(d1) try <- hclust(d2,method=ward) plot(try,labels=FALSE) After looking at the plot, I tried mmm <- cutree(try,h=7) and got the error message Error in cutree(try, h = 7) : the 'height' component of 'tree' is not sorted (increasingly); consider applying as.hclust() first I was much puzzled by this initially, since try is already an ``hclust'' object (I checked class(try)) but after a substantial amount of hair-tearing I discovered that the entries of the height component of try are constant over long stretches. E.g. the first 54 entries are 0 (to the 7 printed decimal places). This doesn't *seem* to be cause for alarm --- the help says explicitly that height is a *non-decreasing* sequence (but not necessarily a strictly increasing one). I checked with(try,all.equal(height,sort(height)) and got [1] TRUE but order(try$height) is NOT equal to 1:745 (note that 746 is the number of subjects in the data set). I have done an RSiteSearch() on "cutree" and turned up nothing that seemed relevant. Finally, I found that if I do try$height <- round(try$height,6) then mmm <- cutree(try,h=7) ``works'' (without error). Are there traps for young players in employing such a strategy? What should I really worry about? If anyone wants to try it for themselves with the real distance matrix, I can bundle it up and email it to them privately. Thanks for any insights. cheers, Rolf Turner ###################################################################### Attention:\ This e-mail message is privileged and confid...{{dropped:9}}