similar to: what is used as height in hclust for ward linkage?

Hi, I want to know if there is a difference between the two hierarchical methods Agnes and hclust when there are used with the same method and the same metric on the same data! I ask this question because I executed the following program: hc <- hclust(dist(AGRIINSTTableFinaleCR), "ward") agnes<-agnes(dist(AGRIINSTTableFinaleCR),method="ward") And clusters are not the

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",

I have a training set of data for known classes with 5 observations of 12 variables for each class. I want to use this information to classify new data into classes which are known to be different to those in the training set but each new class may contain one or more observations. The distribution of within class distances is expected to be similar for all classes and this is found to be the

Sabrán disculparme; no solo no encontré cómo calcular como lo hacía R antes, si no que no encuentro una explicación de autoridad. Si encontré la mención al cambio... además del propio cambio. Pero, digo, habrá algún sitio o foro en el que un montón de matemáticos que saben una barbaridad y hablan de tal manera que yo no entiendo, diran algo así como "no, che, eso no está bien así, hay que

Dear useRs,Is there a command or package in R to do heirarchical clustering by "modified ward method"??regardseliza [[alternative HTML version deleted]]

Hello to the R people, within ward clustering the distance calculated to decide the clustering of 2 subsets (h1 and h2) is the variation of inertia : d(h1,h2)=I(h1Uh2)-I(h1)-I(h2); i've been said that a way to calculate faster this d(h1,h2) is using the huygens theorem decomposing the inertia into "the inertia to the centroid + the distance to an axe" (that's my version ...). My

Hi R people, i'm looking for the Reciprocal Nearest Neighbour algorithm applied to Ward clustering; I know that it is explained in details in the following book: Downs, G. M.; Willett, P. In Advanced Computer-Assisted Techniques in Drug Discovery; van de Waterbeemd, H., Ed.; VCH: Weinheim, 1994; Vol. 3. but cannot order copies it without knowing the pages involved; could someone

"Andrew C. Ward" <acward at uqconnect.net.au> : >With respect to 'tone' and 'friendliness', perhaps all that >is meant or needed is that people be polite and respectful. >I shake my head as often at rude answers Oh, by gosh, by golly. I don't think an occasional dose of 'real life', via a jab from the Professor, will cause any lasting harm

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

Dear R community, I would be greatful if somebody could shed light on the following. I have created a set of 6 points to check how centroid agglomeration works in cluster analysis: > Y <- data.frame(x=c(-1,1,1,-1,10,12),y=c(1,1,-1,-1,0,0)) It is quite intuitive to understand that the last clusters to be joined will be {1,2,3,4} with {5,6}. Now, the centroid for the first cluster has

Hi, I clustered a distance matrix and would like to draw it using plot.hclust or plot.dendrogram. The dendrogram is not informative because I have a few extremely small dissimilarities in the distance matrix (e.g. 0), but most of the other distances are in the range 1e10+-5000. I would like to show the tree only for the height of 1e10+-5000 but unfortunately their are no parameter like

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

Hi all, The function heatmap uses the functions dist and hclust with default parameters. How to change these parameters? For example, i want to use the ward criterion for hierarchical clustering with binary distance. Best regards, Olivier.

Gives strange results. I get 'weird' dendrograms with canberra / binary distance metric and median / centroid cluster methods. Is this just my data? Dan

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"

Hi, I'm doing hierarchical clustering, and want to export my dendrogram to a tree-viewing/editing software. I can do this by converting the data to Newick format (hc2Newick in ctc package), but I can't get branch lengths to show in the resulting phylogram. I figured it might help to convert my hclust object into a phylo object (as.phylo in ape package), but the following lines give me

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 =

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

This worked for me with your example: source("dumpdata.R") storage.mode(x.hc$merge) <- "integer" plot(x.hc) (R-1.4.1 compiled from source on WinNT4.) Andy > -----Original Message----- > From: Hugh Chipman [mailto:hachipma at icarus.math.uwaterloo.ca] > Sent: Wednesday, February 20, 2002 5:32 PM > To: andy_liaw at merck.com > Cc: r-help at stat.math.ethz.ch