similar to: Cluster analysis: dissimilar results between R and SPSS

Hi all! I'm new to R and don't know many about it. Because it is free, I managed to learn it a little bit. Here is my problem: I did a cluster analysis on 30 observations and 16 variables (monde, figaro, liberation, etc.). Here is the .txt data file:

Hello, I have trouble with a data set that comprises 2136 lines of 20 columns. I would like to do a hierarchical clustering and I tried the following: ages.hclust <- hclust(dist(ages, method="euclidean"), "ward") but I get the following error message: Error: cannot allocate vector of size 17797 Kb When I try to do the dist() alone first without the hclust(), I get the

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

Dear R-Help-Team, I would like to cluster my data using the ward-method. In several papers I read (e.g. Bahrenberg) that it is neccesary to use the "squared euclidean distance" with the ward-method. Unfortunatelly I cannot find this term in r as a method for measuring the distance. Does anybody have an idea? Thanks in advance, Carolin [[alternative HTML version deleted]]

Hi all I am trying to perform the follwing: fit<-pvclust(wq, method.hclust="ward", method.dist="euclidean") but get a strange error message that I just cant figure out. Has anyone come across this? Any help would be most appricieated Error in hclust(distance, method = method.hclust) : NA/NaN/Inf in foreign function call (arg 11) In addition: Warning message: NAs

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

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",

Hello, I analyse some free-sorting data so I use hierarchical clustering. I want to compare my proximity matrix with the tree representation to evalute the fitting. (stress, cophenetic correlation (pearson's correlation)...) "The cophenetic similarity of two objects a and b is defined as the similarity level at wich objects a and b become members of the same cluster during the course of

I have used heatmap to visualize my microarray data. I have a matrix of M-values. I do the following. #The distance between the columns. sampdist <- dist(t(matrix[,]), method="euclidean") sclus <- hclust(sampdist, method="average") #The distance between the rows. genedist <- dist(matrix[,], method="euclidean") gclus <- hclust(genedist,

Hi guys, I've been using the kmeans and hclust functions for some time now and was wondering if I could specify a custom metric when passing my data frame into hclust as a distance matrix. Actually, kmeans doesn't even take a distance matrix; it takes the data frame directly. I was wondering if there's a way or if there's a package that lets you create distance matrices from

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

Dear R users, I have noted a difference in the merge distances given by hclust using centroid method. For the following data: x<-c(1009.9,1012.5,1011.1,1011.8,1009.3,1010.6) and using Euclidean distance, hclust using centroid method gives the following results: > x.dist<-dist(x) > x.aah<-hclust(x.dist,method="centroid") > x.aah$merge [,1] [,2] [1,] -3 -6

Hello everyone, Does anyone know how to implement the argument "unit" in R's plclust function ? I used to use Splus where this argument exists but it has not been implemented in R's plclust. The reason why I switched from Splus to R is that Ward's method is not implemented for S's hclust whereas it is implemented for R's hclust. What I would need is S's plclust

Thanks to Andy and Thomas, Reading help(hclust) more carefully would have done it but sometimes you do not see the wood for the trees... So hc$merge does exactly what I want. I have never been aware of the command str to get the structure of an R-object. It seems pretty useful to me. Thanks, Arne > -----Original Message----- > From: Liaw, Andy [mailto:andy_liaw at merck.com] >

Hi all, I've stumbled upon some memory limitations for the analysis that I want to run. I've a matrix of distances between 38000 objects. These distances were calculated outside of R. I want to cluster these objects. For smaller sets (egn=100) this is how I proceed: A<-matrix(scan(file, n=100*100),100,100, byrow=TRUE) ad<-as.dist(A)

I am trying to make dendogram based on gene expression matrix , but getting some error: I countMatrix = read.table("count.row.txt",header=T,sep='\t',check.names=F) colnames(countMatrix) count_matrix <- countMatrix[,-1] # remove first column (gene names) rownames(count_matrix) <- countMatrix[,1] #added first column gene names as rownames) >

Hi, I have created a heatmap using heatmap.2 having 7 clusters. I would like to extract the list of genes that are in these 7 clusters. Is there any function that can be used to extract genes for each cluster? Cheers, Sudhir -- __________________________________________________________ SAVE PAPER - Please do not print this e-mail unless absolutely necessary Being happy doesn't mean

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 Sir How to perform cluster analysis using Ward's method and K- means clustering? Regards -- AMINA SHAHZADI Department of Statistics GC University Lahore, Pakistan. [[alternative HTML version deleted]]

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =