similar to: question about similarities cluster using hierclust

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

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, Can someone point me to documentation or ideas on how to calculate the centroids of clusters identified with hclust ? I would like to be able to chose the number of clusters (in the style of cutree) and then get the centroids of these clusters. This seems like a quite obvious task to me, but I haven't been able to put my hands on a relevant command. Thank you, Moritz

Dear R-help list members i would like to use R to produce clustering or partitioning of a dataset. I am trying to use the functions: - hierclust() of the package multiv -pam(), agnes() and fanny() of the package cluster But I cannot get any result because of lack of memory. Would you know any clustering function not to greedy in memory? I have tried to expand my memory limit with memory.limit()

Hello, a. is there a possibility to obtain a distance matrix with the cosine between vectors?? hclust, hierclust, dist will not work and seem to be hard to extend. b. if there is not: Is the cosine between vectors implemented somewhere? Thanks for all hints and advice! Petra Steiner -------------- next part -------------- An HTML attachment was scrubbed... URL:

Dear Wizards - Thank you so much for your help. That was exactly what I was looking for. Now, I have been working on conditional selection of columns in a data frame. My goal is to calculate the total revenue per sales representative per status in a table. I have come to a complete stop: Let's say, we have a data.frame called df with several columns and a number of rows: df <-

Hallo Friedrich, I have read your announcement, downloaded and installed it in R-0.61.3. But now I have problems :) > hc <- hierclust(dist(t(pollen)), method=3) Error in pmatch(x, table) : argument is not of mode character > hc <- hclust(dist(t(pollen))) > members(hc) Error in .Fortran("assgn", n = as.integer(n), nplus1 = as.integer(nplus1), : C/Fortran function not in

Dear All, Is there any existing code for plotting silhouette for kmeans clustering results? Many thanks! Linda [[alternative HTML version deleted]]

Hi, Being new to R I'm asking for some advice on how to optimize the performance of the following piece of code: > alpha_c <- function(lambda=600e-9,alpha_s=1e-14,N=400,spacing=1e-7){ > > k<-2*pi/lambda > ri<-c(0,0) # particle at the origin > x<-c(-N:N) > positions <- function(N) { > reps <- 2*N+1 > matrix(c(rep(-N:N, each = reps), rep(-N:N,

Here is an example of the data.frame that I have, df<-data.frame("case"=rep(1:5,each=9),"id"=rep(1:9,times=5),"x"=round(runif(length(rep(1:5,each=9))))) "case" represents the cases, "id" the persons, and "x" is the binary state. I would like to know in how many cases any two persons a. both have "1", b. the first has

Hi, Li, Thanks for reply. It is strange that I used RBGL_1.6.0 successfully on the same linux system with R 2.3.1 before! After trying to install R 2.5.1, the RBGL_1.12.0 failed to be installed. Then, I uninstalled R. Delete the directory R/lib/. Installed R.2.3.1 again. And run getBioC1.8.5 for R 2.3.1 to download the three packages graph_1.10.6, RBGL_1.12.0 and Ruuid_1.10.0. But still

Hi, all. I'm trying to cluster 12,500 objects using hclust from package mva. The distance matrix takes up nearly 600 MB. The distance matrix also needs to be copied when being passed to the fortran routine that actually does the clustering (it's modified during the clustering), so that's 1200 MB. I'm actually on a machine with 2.5 GB of memory (and nothing else running), so I

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

Dear All , Since dissimilarity is one of quality measures in clustering , I'm trying to access to the sum of dissimilarity as a whole measure. But after running my data using CLARA I obtain : 1128 dissimilarities, summarized : Min. 1st Qu. Median Mean 3rd Qu. Max. 0.033155 0.934630 2.257000 2.941600 4.876600 8.943700 But I can not find the sum of dissimilarity.How can i

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

Dear List: I have a question regarding an MDS procedure that I am accustomed to using. I have searched around the archives a bit and the help doc and still need a little assistance. The package isoMDS is what I need to perform the non-metric scaling, but I am working with similarity matrices, not dissimilarities. The question may end up being resolved simply. Here is a bit of substantive

Hi all, I have a visualization question regarding sets. My problem is as follows: I have several sets, each having some members (or 'points'). I would like to connect the points, while keeping the sets. For example, if I have 3 sets (Set 1 contains points A, B, C): Set 1: ------ A, B, C Set 2: ----- A, D, E Set 3: ----- C, D, G, H I would like each set to be represented by a circle

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this

I've read in many places that R semantics are based on Scheme semantics. As a long-time Lisp user and implementor, I've tried to make this more precise, and this is what I've found so far. I've excluded trivial things that aren't basic semantic issues: support for arbitrary-precision integers; subscripting; general style; etc. I would appreciate corrections or additions from

Hi, Li and all lists, More, I have searched the web, and found one solution as described in: https://stat.ethz.ch/pipermail/bioc-devel/2007-June/001168.html But, My condition is some how different: 1) my g++ version is: g++ (GCC) 3.2.2 20030222 (Red Hat Linux 3.2.2-5) 2) the SHLIB file is the same, but some difference in /usr/local/lib/R/etc/Makeconf: SHELL = /bin/sh SHLIB_CFLAGS =