similar to: identify.hclust() not working for me

No, you do not necessarily need the XY coordinates. You can also use polygon (field, in your case) adjacency information. See Rogerson, Peter A. 2001. _Statistical methods for geography_. Thousand Oaks: Sage. You might also look at GeoDA, a free and soon-to-be open source spatial analysis package. For your application, it might be easier to use than R. Go to https://www.geoda.uiuc.edu/.

All, I have done a cluster analysis analysis of some spatial locations, based on variables other than lat-long, and want to show the results on a map to see if my clusters have some geographic meaning. For kmeans (etc.) I would just use different symbols (etc.) to distinguish the clusters, but I am doing hierarchical CA. I am looking for ideas or (better) code. One

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),

Hi all, Quick question: What function can I use to draw a line in the margin of a plot? segments() and lines() both stop at the margin. In case the answer depends on exactly what I'm trying to do, see below. I'm using R v. 2.8.1 on Windows XP. Cheers, Alan I'm trying to make a horizontal barplot with a column of numbers on the right side. I'd like to put a line between the

Dear R-help! I ask you to help me with my problems with using R. First, I ask you to forgive my bad English! I try to use R in my study. Subject of my work is comparative study of flora of lakes in different regions of Russia. I have done floristical descriptions of 152 lakes (I think it's enough) and have tabulated it. As data I have a table, such has 152 rows (lakes) 290 variables (species

[cc:ing to the maintainer of digest] FYI, package 'digest' (v0.2.1 2005/11/04 04:45:53) generates the same output regardless of input with R v2.4.0 devel (2006-07-25 r38698). Starting a vanilla R session you get: > library(digest) > digest(1) [1] "3416a75f4cea9109507cacd8e2f2aefc" > digest(2) [1] "3416a75f4cea9109507cacd8e2f2aefc" > digest(rnorm(10)) [1]

I've gotten smb-wall to work with Samba v3. If there's a better way, please let me know. If not, what's the chances this could be included in a future release? #!/usr/bin/perl # #@(#) smb-wall.pl Description: #@(#) A perl script which allows you to announce whatever you choose to #@(#) every PC client currently connected to a Samba Server... #@(#) ...using "smbclient -M"

hi Howard, I'll check into the discrepancy but having the FLAC blocksize match the CD sample frame size is probably not desirable. the optimal blocksize for FLAC is relative to the stationarity of the signal and the 588 sample frame is not related to that, it's an artifact of optimal storage on CD. Josh --- Howard Farrar <howard.farrar at meridian.co.uk> wrote: > Hi > >

I have been attempting to do some work using hclust, and have run into a (possibly subtle) problem. The background is that I constructed a dissimilarity matrix ``d1'' (it involved something called the ``Jaccard similarity coefficient''; I won't go into the details unless requested). I then did d2 <- as.dist(d1) try <- hclust(d2,method=ward)

I have been working on a CTDB cluster on and off for a while now. I had it working great for a while. THen I decide dthat I wanted to change the configuration of my replicated volumes. I changed my DRBD configuration to match my desired configuration. Now I can get the CTDB to work quite right. I am able to join the cluster to the domain without issues. I can also list my ad users and groups using

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

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

Dear R-helpers, While plotting a hclust tree, is it possible to mark group information on it? Suppose I have a hclust tree "ClusTree", and I use cutree to cut the tree into different groups as following a=cutree(ClusTree,h=10) How do I mark the grouping info stored in a while I plot "ClusTree"? Best Regards....

Good afternoon, After cuting a hierarchical tree using cutree(), how to check correspondances between classes and branches? This is what we do: srndpchc <- hclust(dist(srndpc$x[1:1000,1:3]),method="ward") #creation of hierarchical tree plclust(srndpchc,hmin=20000) #visualisation srndpchc20000 = cutree(srndpchc,h=20000) #returns 4 classes table(srndpchc20000 ) srndclass20000 =

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 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

Preparing a paper for a medical journal. Using the prop.test() function in R (v2.4.0) to compare two groups' response to data like the following. A sample of 100 individuals from Population I, 18 with positive readings from a certain test, vs. A sample of 148 individuals from Population II, 61 with positive readings. Results look like this: R version 2.4.0 Patched (2006-11-25

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 There, I'm working on some cluster analyses on a large data-set using hclust with Wards method and Manhattan (city block) distance measures. I've created dendrograms to illustrate the clustering criteria, but would like to create a plot to examine for the classic elbow criterion to use in determining the best number of clusters. Ideally I'd like to plot percent variance explained

I like to monitor simulation by reporting some current values to the console, every 25th iteration say. I think it might be nice to have that appear in a separate window. Anyone know how? regards, David Farrar New River Analytic [[alternative HTML version deleted]]