similar to: plot.hclust: strange behaviour with "manufactured"

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

This question concerns rpart's facility for user-defined functions that accomplish splitting. I was interested in modifying the code so that in each terminal node, a linear regression is fit to the data. It seems that from the allowable inputs in the user-defined functions, that this may not be possible, since they have the form: function(y, wt, parms) (in the case of the

Dear R Helpers, I read carefully the documentation and all postings on the hclust and cutree functions, however some aspects of the tree ordering and cluster assignment performed by these functions remain unclear to me, so I would very much appreciate your help in making sure I get them right. Here is an example, with values chosen to illustrate the problems. I have a set of five profiles

I am making heatmaps for a dataset (~ 300*600 matrix) with the following R script (I am not familiar with R and this is the first time I am using it). library("gplots") library("Cairo") mydata <- read.csv(file="data.csv", header=TRUE, sep=",") rownames(mydata)=mydata$Name mydata <- mydata[,2:297] mydatamatrix <- data.matrix(mydata) mydatascale

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] >

Hi, We are trying to interpret the clusters generated by hclust method of R "stats" package. The problem here is when i get the hc$order then there is some order, while exporting to file that order is lost. Here is the example code and their results: > hc <- hclust(dist(USArrests), "ave") > plot(hc) > hc$label [1] "Alabama" "Alaska"

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 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),

Dear R community, I am trying to understand how the ward linkage works from a quantitative point of view. To test it I have devised a simple 3-members set: G = c(0,2,10) The distances between all couples are: d(0,2) = 2 d(0,10) = 10 d(2,10) = 8 The smallest distance corresponds to merging 0 and 2. The corresponding ESS are: ESS(0,2) = 2*var(c(0,2)) = 4 ESS(0,10)

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 am new to clustering in R and I have a dataset with approximately 17,000 rows and 8 columns with each data point a numerical character with three decimal places. I would like to cluster the 8 columns so that I get a dendrogram as an output. So, I am simply creating a distance matrix of my data, using the 'hclust' function, and then plotting the results (see below, my data is

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 had a look at the online documentation, and didn't see from that what is my problem. If I should have, pardon me. Here is my session. As I understand the documentation, this should work with only an hclust object. I get a similar error when in include a FUN argument. I am using V2.4.0. > hc Call: hclust(d = dist(mtx2, method = "manh"), method =

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

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! 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

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

I use the following code to scan a (limited) parameter space of clustering strategies ... data <- read.table(... dataTranspose <- t(data) distMeth <- c("euclidean", "maximum", "manhattan", "canberra", "binary" ) clustMeth <- c("ward",

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