similar to: AW: Question

Dear All, I am attempting to use the FANNY fuzzy clustering function in R (Kaufman & Rousseeuw, 1990), found in the "cluster" package. I have run into a variety of difficulties; the two most crucial difficulties are enumerated below. 1. Where is the 'm' parameter in FANNY? In _Finding Groups in Data: An Introduction to Cluster Analysis_ (1990) by Kaufman & Rousseeuw,

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

Dear R people, I need to do some work with clustering, but know next to nothing about it at present. R has (at least) three clustering packages, cluster, mclust, cclust. I was wondering if someone can direct me to some good books where I could find documentation and background on the functions in these packages. The html help in these packages lists the following as references. Can people

Hi, I'm trying to use a similarity matrix (triangular) as input for pam() or fanny() clustering algorithms. The problem is that this algorithms can only accept a dissimilarity matrix, normally generated by daisy(). However, daisy only accept 'data matrix or dataframe. Dissimilarities will be computed between the rows of x'. Is there any way to say to that your data are already a

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not accepted by "pam": A call

I've been asked (privately) >>>>> "CarlosJ" == jaramilloc <jaramilloc at si.edu> >>>>> on Wed, 14 Sep 2005 09:40:22 -0400 writes: .......... CarlosJ> In Kaufman & Rousseeuw 2000 book on Cluster Analysis, it says that CarlosJ> Daisy can compute Pearson correlation between variables and then CarlosJ> transform

Hi, I want to know which distance is using in the function kmeans and if we can change this distance. Indeed, in the function pam, we can put a distance matrix in parameter (by the line "pam<-pam(dist(matrixdata),k=7)" ) but we can't do it in the function kmeans, we have to put the matrix of data directly ... Thanks in advance, Nicolas BOUGET

Hi everybody, I want to use the cluster package (Cluster Analysis Extended Rousseeuw et al.). I downloaded it from the CRAN and installed it on my linux system (fedora core 4). All seemed to be allright. But when trying to launch examples, I obtained the following message : > library(cluster) > data(votes.repub) > agn1 <- agnes(votes.repub, metric = "manhattan",

I haven't read the book, but could anyone explain more about this parameter? help(agnes) says that ac measures the amount of clustering structure found. From the definition given in help(agnes.object), however, it seems that as long as the dissimilarity of the merger in the final step of the algorithm is large enough, the ac value will be close to 1. So what does ac really mean? Thank

Dear all, I am no stat wiz and I am just trying to use the AGNES algorithm at my very modest level of statistical of understanding. I have difficulties understanding the ouput from AGNES. My question is: how to interpret the output, especially how do you I know which cluster solution is the best? In SPSS, an Agglomeration Schedule table is produced and I used to look at the biggest jump between

Hello, As suggested in "De'ath, 2002. Multivariate regression trees: A new technique for modelling species-environment relationships. Ecology, 83 (4):1105-1117" (for those interested), I am trying to compare the performance of a multivariate regression tree to a cluster analysis. A simple partitioning with k clusters (as done by `pam`) seemed straightforward and appropriate

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")

I use hierclust (hierarchical clustering) in multiv package. In the documentation it is said that plclust (plotting a dendrogram) is available in S-plus. Can I find it anywhere (I have searched through http://lib.stat.cmu.edu/S and found only quotations of plclust in multiv) or is it only part of the S package (which I don't have)? Thanks --------------- Charles RAUX, Laboratoire

Hello, I had a question regarding clustering using the agnes() function from the 'cluster' package. I was wondering if anyone knew how I can identify cluster points after running the agnes function. For example, I created a dataset with points randomly scattered around (0,0), (0,1) and (1,0). After clustering, the dendrogram shows all the clustered points and I get the ordering and

Sorry for the long message. I'm doing my best to try to explain myself. I have fitted a spline to my data, I have fitted a spline, filled in the missing data by replicating the spline coefficients associated to the last node. I obtained a number of dendograms by different combination of distance and link-method by calling DIST and AGNES. The agglomerative coefficient is very high (~ 0.99) for

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

Thanks a lot, Michael! I cc to R-help, where this question really belongs {as the 'Subject' suggests itself...} -- please drop 'bioconductor' from CC'ing further replies. >>>>> "michael" == michael watson (IAH-C) <michael.watson at bbsrc.ac.uk> >>>>> on Thu, 17 Jun 2004 09:16:59 +0100 writes: michael> OK, admittedly it

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

Full_Name: Mark MacLennan Version: 1.7.1 OS: Solaris 8 Submission from: (NULL) (216.17.17.197) Bug PR#2867 appears to still be occurring ... I am running Solaris 8 using gcc 3.3 and while running the tests for R 1.7.1 I get the following error message regarding Lapack routine dqeqp3 I don't know how serious an issue this is! Thanks for any help! Mark ----- running code in