similar to: Computing very large distance matrix

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

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hello all! I am performingsome clustering analysis on microarray data using agnes{cluster} and I have created my own dissimilarity matrix according to a distance measure different from "euclidean" or "manhattan" etc. My question is, if I choose for example method="complete", how are the distances between the elements calculated? Are they taken form the dissimilarity

Dear Aaron, I am really a tool user and not a tool maker (actually an ecologist doing some biostatistics)... so, I take the liberty of sending a copy of this e-mail to the r-help list where capable computer persons and true statisticians may provide more relevant information and also to Paulo Ribeiro and Peter Diggle, the authors of geoR.. I really feel that your huge matrix cannot be

I try to perform a clustering using an existing dissimilarity matrix that I calculated using distance (analogue) I tried two different things. One of them worked and one not and I don`t understand why. Here the code: not working example library(cluster) library(analogue) iris2<-as.data.frame(iris) str(iris2) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7

Hello Sir, I am a third-year student at the Department of mathematics at IIT Kharagpur. I have good experience in Information Retrieval and Machine Learning. I have read many chapters of the book Introduction to Information Retrieval. Recently I am doing a project on tagging a question on a Q&A Forum using ranking the tags and probabilistic inference. I also have software development

Hi all, I am running a series of experiments where after manipulating my data I run several clustering algorithms (agnes, diana and a clustering method of my own) on the data. I wanted to determine which clustering method did the best job, so therefore I had defined my own quality measure using two criteria: compactness of the data within the clusters themselves and the amount of seperation

Hi, I am very new to R and I have written the following block of code to generate a gamma distribution for variable x (which is an array) and a function "y" whose array values depend on the individual array values of "x". The code is as follows: n=1000 x=rgamma(n,1.5,2) y=vector("numeric",n) for (i in 1:n){ y[i]=(2937/50000*exp(-1/1000*x[i])/x[i]) } now I want to

Dear R users I wondered if any of you ever tried to calculate distance matrix with very large data set, and if anyone out there can confirm this error message I got actually mean that my data is too large for this task. negative length vectors are not allowed My data size and code used dim(mydata_nor)[1] 365000 144> d <- dist(mydata_nor, method = "euclidean") Here my

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, I would like to know if someone knows an extended implementation of k-means in R to find appropriate number of clusters for a given k-dimensional data. Also, I am working on clustering for forecasting, if someone is interested or has knowledge on implementational details please mail me, I would appreciate it. Regards Skanda Kallur "Cogito, ergo sum" (I think, therefore I

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

Hi, I would like to calculate the RELATIVE euclidean distance. Is there a function in R which does it ? (I calculated the abundance of 94 chemical compounds in secretion of several individuals, and I would like to have the chemical distance between 2 individuals as expressed by the relative euclidean distance. Some compounds are in very low abundance whereas others are in high abundance,

Hi, I should preface this problem with a statement that although I am sure this is a really easy function to write, I have tried and failed to get my head around writing functions in R. I can use R where functions exist to do what I want done, but have found myself completely incapable of writing them myself. The problem is that I have a table with several rows of species and several columns of

Hello, I'm playing around with cluster analysis, and am looking for methods to select the number of clusters. I am aware of methods based on a 'pseudo F' or a 'pseudo T^2'. Are there packages in R that will generate these statistics, and/or other statistics to aid in cluster number selection? Thanks, John. --

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

I have a program which needs to compute squared Euclidean distance between two vectors million of times, which the Rprof shows is the bottleneck. I wondered if there is any faster way than my own simple function distance2 = function(x1, x2) { temp = x1-x2 sum(temp*temp) } I have searched the R-help archives and can not find anything except when the arguments are matrices. Thanks for any

Hello all I wonder what can i use to cluster vectors which composed of several factors. lets say around 30 different factors compose a vector, and if the factor is present then it encoded as 1, if not presented then it will be encoded as 0. I was thinking of using hierarchical clustering, as i know the distance between two vector were calculated through euclidean distance function, but i dont

I am studying on statistical shape analysis, I wonder is there any way or package available that I can perform Euclidean Distance Matrix Analysis (EDMA I or EDMA II) in R... thanks Gokhan -- View this message in context: http://r.789695.n4.nabble.com/Euclidean-Distance-Matrix-Analysis-EDMA-in-R-tp2266797p2266797.html Sent from the R help mailing list archive at Nabble.com.

Hi 1. I have two raster files *.asc (identical size) 2. The data in each contain presence or absence data in each cell represented by a 1 or 0 respectively 3. I would like to take the location of each 1 (presence cell) in raster file 1 and measure the euclidean distance to the nearest 1 (presence cell) in raster file 2. Obviously in some cases there will be overlap so the distance will be zero.