similar to: hclust too slow?

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

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] >

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 =

Buenas tardes a todos, Estoy interesado en cambiar los límites del eje y en un dendograma construído utilizando la función hclust(). A continuación un ejemplo: hc <- hclust(dist(USArrests), "ave") plot(hc) Hasta aquí todo bien. Si quisiera cambiar los límites del eje "y" de c(0, 200)? Al usar plot(hc, ylim = c(0, 200)) no observo efecto alguno. Qué puedo hacer?

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

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

Dear R-Help, I have a clustering problem with hclust that I hope someone can help me with. Consider the classic hclust example: hc <- hclust(dist(USArrests), "ave") plot(hc) I would like to cut the tree up in such a way so as to avoid small clusters, so that we get a minimum number of items in each cluster, and therefore avoid singletons. e.g. in this example, you can see

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

I am doing cluster analysis [hclust(Dist, method="average")] on data that potentially contains redundant objects. As expected, the inclusion of redundant objects affects the clustering result, i.e., the data a1, = a2, = a3, b, c, d, e1, = e2 is likely to cluster differently from the same data without the redundancy, i.e., a1, b, c, d, e1. This is apparent when the outcome is visualized

Dear All According to the identify.hclust documentation the function "cuts the tree at the vertical position of the pointer and highlights the cluster containing the horizontal position of the pointer". When I carry out this, the tree isn't cut where I click - in fact, there seems to be a limit below which I cannot go. Consider the following code: mat <- matrix(rnorm(5000),

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

I have a data set with 6 variables and 251 cases. The people who supplied me with this data set believe that it falls naturally into three groups, and have given me a rule for determining group number from these 6 variables. If I do scaled.stuff <- scale(stuff, TRUE, c(...the design ranges...)) stuff.dist <- dist(scaled.stuff) stuff.hc <- hclust(stuff.dist)

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

Full_Name: Ivan Egorov Version: 1.8.1 OS: MS Windows 2000, SP4 Submission from: (NULL) (194.186.91.129) V<-t(matrix(scan('C:/V3.dat'),3)) d<-dist(V) hc<-hclust(d) rect.hclust(hc,5) Error message is displayed: Read 24 items Error in rect(m[which[n]] + 0.66, par("usr")[3], m[which[n] + 1] + 0.33, : plot.new has not been called yet Here's my data file

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

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)

Hello, I'm trying to do a cluster analysis on a large data set. I tried it out with a smaller one first, but I got this error: > hc<-hclust(dist(x),"ave") Error: cannot allocate vector of size 4129151 Kb The data sample used (i.e. "x") is a numerical data set of size 32513 by 31 Does anyone know how I can do this analysis? Is R capable of this

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

Hi When I use plot(hclust(dist..)...)...) etc to create a dendrogram of a hierarchial cluster analysis, I end up with a vertical tree. What do I need to do to get a horizontal tree? Also, my users are used to seeing trees who's leaves all "end" at the same place (eg. Like in minitab). Is this possible in R? Thanks Mick Michael Watson Head of Informatics Institute for Animal

Dear all, The default plotting method for hclust trees looks just fine for few objects like in the example dataset. But when it comes to many features (eg some 1000 and more - I'm trying to visualize clustered microarray data) it renders a tree, that one cannot inspect, because of overlapping text and lines. My question is, is there a way or a plotting parameter for plotting a tree which is