similar to: tool for cluster analysis

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 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 am using R for hierarchical clustering of a number of documents. I have a distance matrix on which I have applied hclust method. When I plot the result of hclust method, I can see the dendogram plotted. What I need now is the dendogram stored as a tree in a data structure. My goal is to automatically label all internal nodes. For that, I need to know, which leaf nodes make a first level

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

Well, you don't give much of an example.... I'm replying CC to the R mailing list. Please ask questions there, rather than adressing individuals for basic help. Here is one; does it answer your question ? data(agriculture) ag.ag <- agnes(agriculture) class(ag.ag) pltree(ag.ag) # the dendrogram, if you want to see it ## cut the dendrogram -> get cluster assignments: (ck3 <-

Dear R-help listers, Is anyone aware of a function that outputs dendrogram ultrametrics? Cheers, Lisa. PS please reply to me personally as well as to the list because the website wasn't letting me subscribe for some reason. thanks... Lisa Holman Research Officer, Vegetation Dynamics Policy & Science Division NSW Department of Environment & Conservation PO Box 1967, Hurstville 2220.

I'm clustering rather large data sets and would like to cut the dendrograms to get a better view of specific components. I calculate the dissimilarity matrix using daisy() because I have a mixture of variable types: factors, ordered factors and numerical variables. If I want one dendrogram, I use agnes() for the agglomerative nesting and pltree() to draw the dendrogram. That way, I get the

Hi all, I have a bit of a newbie question here that I hope y'all can help with. I've run a cluster analysis using hclust on about 500 objects (using R1.3 under Win 2000). The problem is that the tips of the dendrogram are so close together on the plot that the labels overlap and are unreadable. I've used "cex" to reduce the label sizes but this isn't sufficient with so

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

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

The R function hclust is used to do cluster analysis, but based on R help I see no way to print the actual fusion distances (that is, the vertical distances for each connected branch pairs seen in the cluster dendrogram). Any ideas? I'd like to use them test for significant differences from the mean fusion distance (i.e. The Best Cut Test). To perform a cluster analysis I'm using: x

Hi, I am clustering objects using the agnes() function and the UPGMA clustering method (function = "average"). Everything works well, but apparently something is wrong with the dendrogram. For example: x<-c(102,102.1,112.5,113,100.3,108.2,101.1,104,105.5,106.3) y<-c(110,111,110.2,112.1,119.5,122.1,102,112,112.5,115) xy<-cbind(x,y) library(cluster) UPGMA.orig<-agnes(x)

Is the swiss data set in R the same as S dataset swiss.x . I was trying out some clustering by doing the following that I got from Venables and Ripley's book. h<-hclus(dist(swiss.x), method= "connected") plclust(h) cutree(h,3) plclust(clorder(h,cutree(h,3))) I tried swiss instead of swiss.x, it doesnot seem happy. Thanks ../Murli

Hi all, Can someone tell me how to justify (right or left) the labels on the branches of a dendrogram tree? I have produced a dendrogram via agnes and plotted it with pltree. The dendrogram terminal branch labels seem to be centre-justified by default and I was hoping to change this to left justification. Thanks, Duncan ***************************************** Dr. Duncan Mackay School of

Hi list, I am doing cluster analysis using the cluster package. I created a dendrogram using the function plot(agnes(myData)). When I try to change the sise of labels, it does not work. I tried cex = 1.5, etc. Nothing worked. Can anyone give me a hint on how to change the sise of the labels, as well as the sise of axes labels. Thanks Mahdi -- ----------------------------------- Mahdi Osman

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 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

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

Hello, I have used the functions agnes and cutree to cluster my data (4977 objects x 22 variables) into 8 clusters. I would like to refine the solution using a k-means or similar algorithm, setting the initial cluster centres as the group means from agnes. However my data matrix has NA's in it and the function kmeans does not appear to accept this? > dim(centres) [1] 8 22 > dim(data)