similar to: hclust-segmentation fault

Hi R helpers... I am having troubles because of the discrepancy between the dendrogram plotted from hclust and what is wrote in the hc2Newick file. I've got a matrix C: > hc <- hclust(dist(C)) > plot(hc) with the: > write(hc2Newick(hc),file='test.newick') both things draw completely different "trees"... I have also tried with the raw distance matrix D and

I've been trying to get plot.hclust to work with a hclust object I created and have not had much success. It seems that there is some "hidden" characteristic of a hclust object that I can't see. This is most easily seen in the following example, where plot.hclust works on one object, but when this object is "dumped" and then re-read, plot.hclust no longer works. Is

I could not figure out what was the default when I ran hclust() without specifying the method. For example: I just have a code like: hclust(dist(data)) Any input would be appreciated:) [[alternative HTML version deleted]]

Hello all, I'm now working with hclust objects and was hoping to perform some basic editing on them like: - Joining = the merging of two hclust objects (so they will share one root) - Splicing = So to cut/extract a branch out of an hclust object - that by itself will be an hclust object. I noticed I could extract one element of an hclust object by turning it into a dendrogram,

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

Having some problems with using hclust.identify on my data, I revert to trying it out on the example in the help manual. My result is still the same (as with my own data): data(iris) hci <- hclust(dist(iris[,1:4])) plot(hci) testx<-identify.hclust(hci) Error in rect.hclust(x, k = k, x = x$x, cluster = cluster[, k - 1], border = "red") : k must be between 2 and 0

Hello, I am using the hclust function to cluster some data. I have two separate files with the same data. The only difference is the order of the data in the file. For some reason, when I run the two files through the hclust function, I get two completely different results. Does anyone know why this is happening? Does the order of the data matter? Thanks, RC -- View this message in

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

Hello, how can I get the similarity value (i.e., the inner cluster similarity) that was used to cut a hierarchical tree at a specific height? I would appreciate your help! Best regards, Madeleine

Good afternoon, I ran heatmap and hclust on the same matrix x (strictly, I ran heatmap(x), and hclust(dist(t(x))), and realized that the two dendrograms were slightly different, in that the left-right arrangement of one pair of subclusters (columns) was reversed in the two functions (but all individual columns were grouped correctly). Looking through the code for heatmap as a most definite

Hello! I have the following problem. I would like to plot the hclust object "hcd" (bellow, at the end of the mail) with ticks at seq(0.05,0.25,by=0.05). I tried using the code plot(hcd) and plot(hcd,axes=FALSE) axis(2,seq(0.05,0.25,by=0.05)) In both cases, the resoult is the same, ticks at 0.05 and 0.25 are not printed. I tried changing the ylim argumet in plot, however I got a

Dear R users, I have noted a difference in the merge distances given by hclust using centroid method. For the following data: x<-c(1009.9,1012.5,1011.1,1011.8,1009.3,1010.6) and using Euclidean distance, hclust using centroid method gives the following results: > x.dist<-dist(x) > x.aah<-hclust(x.dist,method="centroid") > x.aah$merge [,1] [,2] [1,] -3 -6

dear group members, I am looking for a function that assess the stability of cluster. The result of hclust function is an hclust object which can be plot as a dendrogram. However to have confidence in the tree topology usualy bootstap is applied. I understand that I can apply bootstarp on the original data and then run hclust(dist() ) as much as I resampled but how to comapre the topologies the I

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)

Dear group, I am currently struggling with the following problem for a while: I want to create a list whose elements consists of lists themselves. More concise: The list elements are HCLUST objects. However, when I try to append the HCLUST objects to my list via: cluster_list <- append(cluster_list, HCLUSTobject) the HCLUST object is appended - but not as an object but as its components. So

I am doing a cluster analysis with hclust. I want to get hclust to output the Hotelling's T squared statistic for each cluster so I can evaluate is data points should be in a cluster or not. My research to answer this question has been unsuccessful. Does anyone know how to get hclust to output the Hotelling's T squared statistic for each cluster? Mike [[alternative HTML version

library(mva) data(USArrests) hc <- hclust(dist(USArrests), "ave") plot(hc) # OK dump(c("hc"), "tst") rm(hc) source("tst") plot(hc) # Error in plot.hclust(hc) : invalid dendrogram input The same problem occurs with dput/dget --please do not edit the information below-- Version: platform =

Dear R-users, I tried to receive the merge details of a clustering by using the summary function of hclust. For illustration I use the Longley data as done by Prof Ripley (Wed 11 Apr 2001) d <- dist(longley.y) d <- d/max(d) hc <- hclust(d, "ave") But instead of getting a matrix for $merge I get: >summary(hc) Length Class Mode merge 30 -none- numeric

Thanks to Andy and Thomas, Reading help(hclust) more carefully would have done it but sometimes you do not see the wood for the trees... So hc$merge does exactly what I want. I have never been aware of the command str to get the structure of an R-object. It seems pretty useful to me. Thanks, Arne > -----Original Message----- > From: Liaw, Andy [mailto:andy_liaw at merck.com] >

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