I have two examples that I run hclust on: a = c(0,1,1.5,1.5) b = c(1,0,1.5,1.5) c = c(1.5,1.5,0,0.5) d = c(1.5,1.5,0.5,0) ll = as.matrix(rbind(a,b,c,d)) test = as.dist(ll) long = hclust(test)
a = c(0,0.3,1,1) b = c(0.3,0,1,1) c = c(1,1,0,0.5) d = c(1,1,0.5,0) ll = as.matrix(rbind(a,b,c,d)) test = as.dist(ll) short = hclust(test) The main difference between them is whether a and b gets clustered higher up or lower down than the b,c cluster. I am working on partitioning this kind of data into three clusters. I know I can do that with cutree. The result I get from that is the following: > cutree(short, k=3) a b c d 1 1 2 3 > cutree(long, k=3) a b c d 1 2 3 3 > And I can also access the height matrix for both: > short$height [1] 0.3 0.5 1.0 > long$height [1] 0.5 1.0 1.5 > So I know at what heights they get merged. What I seem to be unable to get at is which one of the clusters as shown by cutree correspond to what split. When I examine short in a plot I can easily see that the highest split (i.e corresponding to the last height, 1, in the height matrix) is between the cutree clusters 1 and 2,3. In the long example this split is between 1,2 and 3. I would however like to not examine all of the data I have by hand:) Could any of you point me to what I need to do to get at this data? I have tried to examine the merge data in both cases, but I am coming up short. Thanks! Karin -- Karin Lagesen, PhD student [EMAIL PROTECTED] http://folk.uio.no/karinlag ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
