similar to: bifurcation countings in a cluster dendrogram

Dear all, I am using the hclust method in R. I am building nice dendrograms with it. Actually for my further analysis I want to get the number of bifurcations (bif) for each object (labels) in the dendrogram. That has to be calculated by the colums merge, height and order. http://www.nabble.com/file/p16142989/Sc2Mown_Pred.txt Sc2Mown_Pred.txt Sc2Mpred<-

Hello, It seems that the plot function for dendrograms does not draw labels when they are too long. > hc <- hclust(dist(USArrests), "ave") > dend1 <- as.dendrogram(hc) > dend2 <- cut(dend1, h=70) > dd <- dend2$lower[[1]] > plot(dd) # first label is drawn > attr(dd[[1]], "label") <- "aaaaaaaaaaaaaaaaaa" > plot(dd) # first label is

Hello to all, I still have this doubt. I'd like to colour the different labels of my dendrogram each one with a different colour. How can I do? I guess I could do using *edgetext* and then *t.col* or* lab.col* but I don't know how to add edgetext to my dendrogram. Can you help me please? Example: require(graphics); require(utils) hc <- hclust(dist(USArrests), "ave") (dend1

I'd like to be able to cut dendrograms at a height I specify and then plot the resulting subtrees. I wanted to use the dendrogram object for this purpose because there doesn't seem to be a canned way to cut a hclust object and get a list of hclust objects, but there is a function (cut) that does that for dendrograms. The problem I'm having is that when I plot a dendrogram, I

I am using hclust and plot to produce dendrograms. Using my input data I am able to complete an analysis and obtain a vertical plot. I want to be able to plot the dendrogram horizontally.I am using version 2.6 of R and have updated my packages recently. Using the sample script for dendrograms I can produce a horizontal plot using the instruction horiz = TRUE in plot(). When I use the same

Hello all - This code will run, but it bogs down my computer when I run it for finer and finer time increments and more generations. I was wondering if there is a better way to write my loops so that this wouldn't happen. Thanks! -Tyler ################# # Bifurcation diagram # Using Braaksma system of equations # We have however used a Fourier analysis # to get a forcing function

Hello to all, I'd like to colour the different labels of my dendrogram. How can I do? I guess I could to using *edgetext* and then* t.col* or *lab.col* but I don't know how to add edgetext to my dendrogram. Can you help me please? Example: require(graphics); require(utils) hc <- hclust(dist(USArrests), "ave") plot(dend1) labels (USArrests) [[1]] # to know how many cities

Note that cophenetic.default (which works on the output of hclust(dist(X))) uses the row names of X as labels. as.dendrogram.hclust does not retain those row names so cophenetic.dendrogram cannot use them (so it orders them based on the topology of the dendrogram). Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Apr 21, 2016 at 7:59 AM, William Dunlap <wdunlap at tibco.com> wrote:

Dear r-list, I am trying to visually seperate the two main clusters of a dendrogram. The idea is to use: 'edgePar=list(lty=3)' for 'dend1[[1]]' and 'edgePar=list(lty=1)' for 'dend1[[2]]' I have not found a way to solve this. Any suggestions? Patrick hc <- hclust(dist(USArrests), "ave") (dend1 <- as.dendrogram(hc)) par(mfrow=c(2,2)) plot(dend1)

Hello, I have been using the "cophenetic" function for objects of class "dendrogram" and I have realised that it gives different results when it is used with objects of class "hclust". For instance, running the first example in the help file of the "cophenetic" function, d1 <- dist(USArrests) hc <- hclust(d1, "ave") d2 <-

Dear All, How can the height of a leaf be extracted from a dendrogram? Sure, I can print it, but I am not able to, say, store it in an object. I think I understand that the height is a property of the split, not the leaf itself, but the printing functions display a "height" or "h" (which changes with "hang") and that is what I want. Obviously, the info is there

Hi! In the example: hc <- hclust(dist(USArrests), "ave") dend1 <- as.dendrogram(hc) dend2 <- cut(dend1, h=70) Do the branches "Branch 1", "Branch 2", "Branch 2"...in dend2$upper str(dend2$upper) --[dendrogram w/ 2 branches and 4 members at h = 152] |--[dendrogram w/ 2 branches and 2 members at h = 77.6] | |--leaf "Branch 1" (h=

Dear R users, I m quite a novice in using R for factor analysis and I would need some help to choose the right function. I have a contingency table and I would like to perform a Correspondence analysis on this table, followed by a hirarchical clustering of my variables projected in on the first principal components. Here are my question : - what is the more appropriate function to do so ...

I think the results differ only in the order of the labels. The following function puts the labels in a standard order and then the results are the same: canonicalize.dist <- function (distObject) { o <- order(labels(distObject)) as.matrix(distObject)[o, o, drop = FALSE] } identical(canonicalize.dist(d2), canonicalize.dist(d3)) [1] TRUE Bill Dunlap TIBCO Software

Hi! I just discovered that cutree() and cut.dendrogram() do not assign the same cluster numberings when called on the same tree. More specifically, cutree() assigns cluster numbers by order of appearance in the data, while cut.dendrogram() sorts clusters by height (see example below). I guess this is for historical reasons? I'm hit by this difference when I want to get a vector of cluster

Dear all, I have been trying the following without avail and would be very grateful for any help. From a dendrogram (recursive list of lists with some structure), I would like to obtain some information of the component lists and of the enclosing list at the same time. In dendrogram-speech I basically would like the label of the leaf and the height of the enclosing branch. A dendrogram example

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

Hi, I have a gene expression experiment with 20 samples and 25000 genes each. I'd like to perform clustering on these. It turned out to become much faster when I transform the underlying matrix with t(matrix). Unfortunately then I'm not anymore able to use cutree to access individual clusters. In general I do something like this: hc <- hclust(dist(USArrests), "ave")

It is a trivial problem, but in the book I couln`t figure out how to put different colours at different regression lines plot(bif,abund,type="n", xlab= "number_bifurcations", ylab="abundances") sbif<-split(bif,stage) sabund<-split(abund,stage) points(sabund[[2]],sbif[[2]],pch=16, col="red") for(i in 1:2) abline(lm(sabund[[i]]~sbif[[i]])) Thanks in

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 =