similar to: Help understanding cutree used for Dunn Index

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 have calculated the Silhouette score and Dunn score after hierarchical clustering for 3 clusters: #Distance measure d <- dist(USArrests, method = "euclidean") #Hierarchical clustering hc <- hclust(dist(USArrests), "ave") #calculating silhouette value for 3 clusters sil<- silhouette(cutree(hc, k=3), d) #calculating Dunn index for 3 clusters clus <- cutree(hc,

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")

Hello, I want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is said that you have to use euclidean

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 =

Dear all, I am encountering a cyclic dependency error when running R CMD check on an R package I wrote (R version 2.6.1, Mac OS X 10.4), see the error message below. Creating a new generic function for "print" in "clValid" Creating a new generic function for "summary" in "clValid" Creating a new generic function for "plot" in

Hi As a beginer with R I have been trying to plot dendrograms for individual groups after using cutree. The example in the help files appears to work fine for Euclidean distances using the "average" clustering method. However, when I use the "Ward" method the the reprocessed subgroup does not appear to have the same structure as it did when the whole dataset was processed. Is

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

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'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, 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)

Hi Everyone, I'm trying to use cutree to get the clusters after hclust. What I used is: mycluster<-cutree(cnclust,h=0.5) Now, my problem is, how can I get the actual clusters? Thanks! Best, Baoqiang Cao

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

Full_Name: Anja von Heydebreck Version: 1.3.0 OS: Alpha Unix Submission from: (NULL) (141.14.19.61) Hi, I repeatedly obtained meaningless results from the function 'cutree' in the 'mva' package, when the argument 'h' was greater or equal to the maximum height occuring: > library('mva') > y [,1] [,2] [,3] [,4] [1,] 0 1 -1 1 [2,] 0 -1

I try to use the routine cutree to cut a tree (created by hclust) into several groups by specifiing the hight of the cut. a<-1:10 cutree(tree, h=a*100) The Matrix with group meberships returned is ok for most of the hights, but in some cases (as for example h=800 and h=900) the results don't make sense (group membership=0 or 58965231, it looks like the range of data allowed by the data

> 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 list, the following approach did not work: clustersA <- pam(distances, nkA, diss=TRUE); gc(); filenameclu = paste("filenameclu", ".txt"); write.table(clustersA , file=filenameclu,sep=","); although it worked with clustersA <- hclust(distances, method="ward"); and a consecutive kclassA <- cutree(clustersA, k=nkA); filename =

Hi everybody, I have a problem with a cluster analysis. I am trying to use hclust, method=ward. The Ward method works with SQUARED Euclidean distances. Hclust demands "a dissimilarity structure as produced by dist". Yet, dist does not seem to produce a table of squared euclidean distances, starting from cosines. In fact, computing manually the squared euclidean distances from cosines

I have the following code that perform hiearchical clustering and plot them in heatmap. __ library(gplots) set.seed(538) # generate data y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep=""))) # the actual data is much larger that the above # perform hiearchical clustering and plot heatmap test <- heatmap.2(y)