similar to: Question about plotting a hclust tree

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

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)

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)

I've found that while cutree() and rect.hclust() make the same classes for a given height in the dendrogram, the actual labeling of the classes is different. For example, both produce the same 4 classes but class 1 according to cutree() is class 4 according to rect.hclust(). Would it be possible that future versions provide the same labeling? rect.hclust() is useful to display the classes

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

I am making heatmaps for a dataset (~ 300*600 matrix) with the following R script (I am not familiar with R and this is the first time I am using it). library("gplots") library("Cairo") mydata <- read.csv(file="data.csv", header=TRUE, sep=",") rownames(mydata)=mydata$Name mydata <- mydata[,2:297] mydatamatrix <- data.matrix(mydata) mydatascale

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

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

howdy R friends, I've searched CRAN but to no avail... I'm trying to use mva's hclust and print out for say 10 clusters in batch. How do I do this? It's unclear if I can use cutree. thanks, John Strumila -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Hi! I am newbee to R and I am facing the problem in plotting the dedrogram with lot of objects. The lines and labels are overlapped very badly, and writing the graphic to postscript and zooming there is not helping either. I tried cut.dendrogram method, but getting the error that it doesn't exist even though I get the man pages for it. I would not find any solution in web as well, and I

I have the following code that perform hiearchical clustering and plot them in heatmap. __ library(gplots) set.seed(538) # generate data y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep=""))) # the actual data is much larger that the above # perform hiearchical clustering and plot heatmap test <- heatmap.2(y)

Hello, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

I'm using the hclust method from the package mva (pre-compiled-version on Win NT) and would like to cut the tree in pieces. Have someone implemented the function cutree (as in S-PLUS) in R? Many thanks Markus Huerzeler -- Dr. Markus Huerzeler Phone: +41 / 61 / 686 98 83 AICOS Technologies AG Fax: +41 / 61 / 686 98 88 Efringerstrasse 32 mailto:mhuerzeler at

I had a look at the online documentation, and didn't see from that what is my problem. If I should have, pardon me. Here is my session. As I understand the documentation, this should work with only an hclust object. I get a similar error when in include a FUN argument. I am using V2.4.0. > hc Call: hclust(d = dist(mtx2, method = "manh"), method =

Hi All Not sure if this a bioconductor question or general R mailing list so apologies if this has gone to the wrong one................. When plotting dendrograms created by hclust you can "identify" clusters by clicking on the graphics and returning a list of what is contained in each cluster. However I'd like to be able to "zoom in" on specific clusters and plot

Hallo everybody, I have a question concerning plotting of hclust dendrograms. I created several of those dendrograms and like to plot them in different plots. I'd like to have all plots with the same scaling of the y axis so that they become comparable by eye. That means they should all start at y=0 and end up with a certain given value, e.g. y=8. I tried the following: plot(

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")

Hello all, A question/suggestion: I was wondering if there is a chance of changing stats::cutree to be S3 and use cutree.hclust? For example: cutree <- function(tree, k = NULL, h = NULL,...) { UseMethod("cutree") } cutree.hclust <- stats::cutree # This will obviously need the actual content of stats::cutree This would be nicer for people like me to add new methods to

Hi. I did write those functions, and sent them (I thought) to one of the R maintainers to see whether they would be appropriate for inclusion (because I'd seen some requests on the mailing lists). However, I'm happy to post them -- I should have thought of it before. WARNING: I've tested these functions on some data arising in my work and also on the USArrests data that comes with