similar to: Customizing Cluster Analysis plots created with hclust

Hi all, ok, i know i can cut a dendrogram, which i did. all i get is three objects that a dendrograms itself. for example: myd$upper, myd$lower[[1]], myd$lower[[2]] and so on. of course i can plot them seperately now. but the lower parts still have hundreds of branches. i?ll need a 30 " widescreen to watch the whole picture. what i?d like to is group the lower branches , so that i get a

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

Hi all, This is the first time I'm writing to R-devel, and this time I'm just asking for the purpose for a certain line of code in stats::as.hclust.dendrogram, which comes up as I'm trying to fix dendextend. The line in question is at line 128 of dendrogram.R in R-3.3.3, at stats::as.hclust.dendrogram: stopifnot(length(s) == 2L, all( vapply(s, is.integer, NA) )) Is there any

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

Hi Sorry if this is in the help :-S I've looked at example(dendrogram) and though it gives some indication of what I want, it doesn't do all. OK, so here is what I want to do: draw a tree, and then have an action, on user-click, to either draw a sub tree or a plot of the data. I also want users to be able to search for a particular label and have it highlighted on the tree, say in

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

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

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)

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

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

Hi, Can someone please guide me towards how to produce "heatmap" output from the output of "hclust" run prior to the actual "heatmap" call? I have some rather lengthy clustering going on and tweeking the visual output with "heatmap" recalculating the clustering every time is not feasible. Thanks, Joh

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 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, The heatmap function conveniently has a "reorder.dendrogram" function so that clusters follow a certain logic. It seems that the hclust function doesn't have such feature. I can use the "reorder" function on the dendrogram obtained from hclust, but this does not modify the hclust object itself. I understand that the answer should be within the "heatmap"

Hello, a. when I use hclust with the methods media, centroid, and mcquitty, and plot the results, the dendrograms have lines that are crossing each other. Is this ok? b. My next question refers to plot.dendrogram: How can I use parameters as "hang" or "cex" here? E.g. for st <- as.dendrogram(subtreeshc[[x]]) I would like to have something like this, where cex and hang

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 =

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

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

Hey, I am running hclust on several different distance matrices and I have a question thats more about labeling. I've been looking for a way to label the edge values on the graph with their distances between them. I've been looking through the documentation and I haven't found anything yet. Anyone know if there is a way to plot 'hclust' graphs with such edge values? Or