similar to: A question:How to plot hierarchical clustering with different colors?

Dear Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

Hey Everyone! I wanted to carry out Hierarchical clustering using distance matrices i have calculated ( instead of euclidean distance etc.) I understand as.dist is the function for this, but the distances in the dendrogram i got by using the following script(1) were not the distances defined in my distance matrices. script: var<-read.table("the distance matrix i calculated",

hi, I want to do hierarchical clustering with Jaccord index. I tried to do with vegan package for finding index and hierarchical clustering with hclust function. While doing clustering it is showing an error message as "invalid distance method". I would be grateful if anyone tells how to rectify the error. Thanks in advance,   kind regards, Ms.Karunambigai M PhD Scholar Dept. of

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Hello List, I am trying to implement a hierarchical cluster using the hclust method agglomerative single linkage method with a small wrinkle. I would like to cluster a set of numbers on a number line only if they are within a distance of 500. I would then like to print out the members of this list. So far I can put a vector: > x<-c(2,10,200,300,600,700) into a distance matrix: >

Hi, i have the following problem: I am using k-means algorithm for clustering. But instead of using randomized centers, I would like to use centers created by hierarchical clustering. So I want to apply "hclust" on my data set (in this case the iris data), getting a solution by "cutree", calculating the means/centers of the resulting clusters and use these centers as starting

Hi, I''m using heatmap.2 to cluster my data, using the centroid method for clustering and the maximum method for calculating the distance matrix: library("gplots") library("RColorBrewer") test <- matrix(c(0.96, 0.07, 0.97, 0.98, 0.50, 0.28, 0.29, 0.77, 0.08, 0.96, 0.51, 0.51, 0.14, 0.19, 0.41, 0.51), ncol=4, byrow=TRUE)

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

Hi All, i've problem to understand how to work with R to generate a hierarchical clustering my data are in a csv and looks like : idcode,count,temp,sal,depth_m,subs 16001,136,4.308,32.828,63.46,47 16001,109,4.31,32.829,63.09,49 16001,107,4.302,32.822,62.54,47 16001,87,4.318,32.834,62.54,48 16002,82,4.312,32.832,63.28,49 16002,77,4.325,32.828,65.65,46 16002,77,4.302,32.821,62.36,47

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

Hi R users! I have a bit of a problem with using an hierarchical clustering algorithm: a<-c(1:15) b<-rep(seq(1:3), 5) c<-rnorm(15, 0,1) d<-c(sample(1:100, 15, replace=T)) e<-c(sample(1:100, 15, replace=T)) f<-c(sample(1:100, 15, replace=T)) data<-data.frame(a,b,c,d,e,f) q<-data.frame(data$d, data$e, data$f) q<-scale(q) What i want to do is to use an

Dear R users, Is there a way to apply a “stopping rule” in hierarchical Clustering? I have a data and I want to find the optimal number of clusters while doing hierarchical clustering. I would be deeply obliged if someone, whoever applied stopping rule, could send me the scripts. thanks in advance Regards Eliza botto [[alternative HTML version deleted]]

I have a large excel file with data in it. I converted it to a 'csv' format. I imported this dataset to R using the follownig command mldata <- read.csv("c:\\temp\\mldata.csv", header=T) all the column names and the rows seems to be correct. Now that I have this object, I need to perfrom hclust. I used the following hc <- hclust(dist(mldata), method="single")

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

I think the "cmeans" method in library(e1071) works better for large data sets as "fanny". (note,not for this data: fanny has also problems with standardized large data sets - here produce fanny the same memberships for all observations; cmeans works "correctly") Matthias > -----Urspr??ngliche Nachricht----- > Von: Bhaskar S. Manda [mailto:bhaskar at

Hi Sir I am new user of 'cluster' and 'amap' packages for hierarchical clustering. I want to find the optimum number of clusters described by Mojena (1977) 'Hierarchical grouping methods and stopping rules: An evaluation' The Computer Journal, 20(4). I did not find the such function for optimal choice of clusters. Some suggestions or hints are required. Thank you --

Hi, I'm trying to use R to cluster words with related meanings. Does anyone know of a non-hierarchical clustering method in R that produces non-exclusive clusters? With non-exclusive, I mean that words should be allowed to be part of multiple clusters. So my data matrix would look something like: T1 T2 T3 CLOWN_N 0 1 0 BANK_N 3 0 2 RIVER_N 0 0 2 FLOW_V 0 0 3 MONEY_N 2 0 0 PAY_V 2 0 0 The

Dear everybody! In the function hclust, at each stage distances between clusters are recomputed by the Lance-Williams dissimilarity update formula according to the particular clustering method being used. Using "centroid" method, Lance-Williams recurrence formula works properly only for euclidean distance. How is it possible to use properly centroid method with manhattan distance ?

Dear R-helpers, Is there somebody who knows if R has already a build in function for Hierarchical Clustering which uses Mutual Information as proximity measure? Many thanks and best regards, J. --------------------------------- [[alternative HTML version deleted]]

Hello, I am new to the R package. After I use R to perform the hierarchical clustering, I am only interested in retrieving the leaf nodes that share the last common ancestors. As illustrated below, I'd like to retrieve (B, C) as a cluster and then (D, E) as another cluster. Any chance to do this in R? Thanks! BTW, I just subscribed to this list (not sure if the subscription is