similar to: packages for extracting subtrees

Hi, all. Last week I posted some code for extracting subtrees of trees in hclust format. Petra Steiner quickly found an example for which the code breaks, and sent it to me. The problem seems to be that I had not considered the possibility of trees with unlabeled nodes. In the new version of f.make.subtree (below), I steal some code from plot.hclust to assign labels if there are none. That

I'd like to be able to cut dendrograms at a height I specify and then plot the resulting subtrees. I wanted to use the dendrogram object for this purpose because there doesn't seem to be a canned way to cut a hclust object and get a list of hclust objects, but there is a function (cut) that does that for dendrograms. The problem I'm having is that when I plot a dendrogram, I

> I have performed clustering analysis with hclust (Ward's method) on a > database of 800 samples. As you may imagine the full dendrogram is not > really readable. I have obtained groups with cutree. I would like to plot > sub-sections of my big dendrogram to show group 1, group 2 and so on. I don't think R has anything like subtree in Splus, unfortunately. I think what has

Hi, I have a gene expression experiment with 20 samples and 25000 genes each. I'd like to perform clustering on these. It turned out to become much faster when I transform the underlying matrix with t(matrix). Unfortunately then I'm not anymore able to use cutree to access individual clusters. In general I do something like this: hc <- hclust(dist(USArrests), "ave")

Dear R community, I would like to plot a partial hclust output, so I?am looking for a subtree function that would return an tree structure I can plot. I ran the test code of subtree following the instruction on?http://finzi.psych.upenn.edu/library/extracat/html/subtree.html However, an error message popped up: ====> library(extracat) Attaching package: ?extracat? The following object is masked

Dear R Helpers, I read carefully the documentation and all postings on the hclust and cutree functions, however some aspects of the tree ordering and cluster assignment performed by these functions remain unclear to me, so I would very much appreciate your help in making sure I get them right. Here is an example, with values chosen to illustrate the problems. I have a set of five profiles

I am attempting to replicate what Cluster 3.0 and Treeview (both by Mike Eisen) to cluster both microarray genes and arrays does using R with hclust. I basically utilized the plot.mat function in sma library with some layout() and hclust(). 1. Can I know if some has already written such a function or available in some package. 2. If not, would appreciate if someone could take the time to test

Hi! I just discovered that cutree() and cut.dendrogram() do not assign the same cluster numberings when called on the same tree. More specifically, cutree() assigns cluster numbers by order of appearance in the data, while cut.dendrogram() sorts clusters by height (see example below). I guess this is for historical reasons? I'm hit by this difference when I want to get a vector of cluster

Hello, I'm having trouble figuring out how to see resulting groups (clusters) from my hclust() output. I have a very large matrix of 4371 plots and 29 species, so simply looking at the graph is impossible. There must be a way to 'print' the results to a table that shows which plots were in what group, correct? I've attached the matrix I'm working with (the whole thing

Hi: I have calculated the Silhouette score and Dunn score after hierarchical clustering for 3 clusters: #Distance measure d <- dist(USArrests, method = "euclidean") #Hierarchical clustering hc <- hclust(dist(USArrests), "ave") #calculating silhouette value for 3 clusters sil<- silhouette(cutree(hc, k=3), d) #calculating Dunn index for 3 clusters clus <- cutree(hc,

Hi! In the example: hc <- hclust(dist(USArrests), "ave") dend1 <- as.dendrogram(hc) dend2 <- cut(dend1, h=70) Do the branches "Branch 1", "Branch 2", "Branch 2"...in dend2$upper str(dend2$upper) --[dendrogram w/ 2 branches and 4 members at h = 152] |--[dendrogram w/ 2 branches and 2 members at h = 77.6] | |--leaf "Branch 1" (h=

Hi, I have the distance matrix computed and I feed it to hclust function. The plot function produces a dense dendrogram as well. But, the cutree function applied does not produce the desired list. Here is the code x=data.frame(similarity_matrix) colnames(x) = c(source_tags_vec) rownames(x) = c(source_tags_vec) clust_tree=hclust(as.dist(x),method="complete") plot(clust_tree)

>>>>> Ross Ihaka writes: > I have got a little side-tracked (from graphics) and am putting > together a little multivariate analysis library. This is just > intended to be a "core" library rather than anything exhaustive. > Mainly it is a matter of putting togther code which already exists at > StatLib. Here is my present list (only some of which is

Hi, everyone. As mentioned in my message of earlier today, I'm trying to do hierarchical clustering on a data set that's probably too big. I may have to take a subset eventually, but I'm spending a little time trying to try to reduce memory use in the hope that I'll manage to do the full set. One thing that occurred to me was to modify the underlying fortran code (hclust.f, from

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, all. I'm trying to cluster 12,500 objects using hclust from package mva. The distance matrix takes up nearly 600 MB. The distance matrix also needs to be copied when being passed to the fortran routine that actually does the clustering (it's modified during the clustering), so that's 1200 MB. I'm actually on a machine with 2.5 GB of memory (and nothing else running), so I

Core developers, I couldn't find any simple way to send a vector of colors to apply to each terminal in plot.dendrogram() or plot.hclust()---I asked R-help about it a few weeks ago and didn't get any response---so I hacked that functionality into the plot.dendrgram code (see below for hacked function plus examples).... Is there any chance this functionality could be added to the

Hello list (happy new yeaR), Here's a copy of a message i just send to Christian Hennig (who wrote the fpc package). That may interrest some of you, and maybe someone could have a better solution than mine. Romain. ------------------------------------------------------------------------------------------ Mister Hennig, [[[ I'm writing in english because i don't know german

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)