similar to: function as.dist failed on large matrix on 64bit machine

Hi, I have a question about using lm on matrix, have to admit it is very trivial but I just couldn't find the answer after searched the mailing list and other online tutorial. It would be great if you could help. I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, and trainy is 492 by 1. Also, I have the newdata testx which is 240 (rows) by 220 (columns). Here is

Hi Everyone, I'm trying to use cutree to get the clusters after hclust. What I used is: mycluster<-cutree(cnclust,h=0.5) Now, my problem is, how can I get the actual clusters? Thanks! Best, Baoqiang Cao

Dear All, I was trying to format a numeric vector (100*1) by using outd <- format(x=m, sci=F, digits=2) > outd[1:10] [1] " 0.01787758" "-0.14760306" "-0.45806041" "-0.67858525" "-0.64591748" [6] "-0.05918100" "-0.25632276" "-0.15980138" "-0.08359873" "-0.37866688" >m[1:10] [1]

Use something like hclust(as.dist(mydist), ...) ought to work. Andy -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of ucecgxu at ucl.ac.uk Sent: Wednesday, January 18, 2006 4:47 PM To: r-help at stat.math.ethz.ch Subject: [R] r-help, how can i use my own distance matrix without using dist() Dear R-helpers, i am a

Hello, I'm having trouble figuring out how to see resulting groups (clusters) from my hclust() output. I have a very large matrix of 4371 plots and 29 species, so simply looking at the graph is impossible. There must be a way to 'print' the results to a table that shows which plots were in what group, correct? I've attached the matrix I'm working with (the whole thing

Dear All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

Dear R-helpers, i am a beginner of R and i am using cluster package to do hierarchical clustering i am wondering if i can use my own distance matrix to do the hierarchical clustering without using dist() function. if i have my own distance matrix, how can i ask hclust() function to recongnize it( as the output of dist() function). thank you very much and i looking forward to hearing from you.

Dear All, Just start to use the long expected R, my focus will be doing clustering on microarray data, just wonder, anyone can show me any references to conquer the steep learning curve? Thanks! Best regards, Baoqiang Cao

Hi, I'm trying to plot something in the following way and would like if you could help: I'd like in a same plot window, two plots are shown, the left one is a bird-view plot of the whole data, the right half keep changing, i.e., different plots will be shown up on request, so that when I select/click on some where in the left plot, the right plot will be the corresponding plot. What I

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)

Hi, I have the following two measurements stored in mat: > print(mat) [,1] [,2] [1,] -14.80976 -265.786 [2,] -14.92417 -54.724 [3,] -13.92087 -58.912 [4,] -9.11503 -115.580 [5,] -17.05970 -278.749 [6,] -25.23313 -219.513 [7,] -19.62465 -497.873 [8,] -13.92087 -659.486 [9,] -14.24629 -131.680 [10,] -20.81758 -604.961 [11,] -15.32194 -18.735 To calculate the ranking

Hi, I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error: > M <- list() > M$y = Dmat[,1] > M$X = Cmat > M$Ain = as.matrix(Amat) > M$bin = rep(0, dim(Amat)[1]) > M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) > M$w = rep(1, length(M$y)) > M$C = matrix(0,0,0)

Hi, I'd like to download some data files from a remote server, the problem here is that some of the files actually don't exist, which I don't know before try. Just wondering if a function in R could tell me if a file exists on a remote server? I searched this mailing list and after read severals mails, still clueless. Any help will be highly appreciated. B.C.

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

kmeans and clara work great. Thank you for the tip. I have another question: Is it possible to weight the observations in a cluster analysis ? I haven't found any mention of this in the kmeans of clara help texts. Moritz Lennert Charg? de recherche IGEAT - ULB t?l: 32-2-650.65.16 fax: 32-2-650.50.92 email: mlennert at ulb.ac.be > On Wed, 25 Apr 2001, Moritz Lennert wrote: >

heatmap.2 defaults to dist for calculating the distance matrix and hclust for clustering. Does anyone now how I can set dist to use the euclidean method and hclust to use the centroid method? I provided a compilable sample code bellow. I tried: distfun = dist(method = "euclidean"), but that doesn't work. Any ideas? library("gplots") library("RColorBrewer") test

Dear All, I'm learning to train a neural network with my training data by using nnet package, then evaluate it with a evaluation set. My problem here is that, I need the trained network to be used in future, so, what should I store? and How? Any other options other than nnet package? Any example will be highly appreciated! Best, Baoqiang Cao

Hello everybody. I am trying to cluster circular data (data points which are angles), thus i can not use the "dist" function in "mclust" to generate my distance matrix, I am using the function " Dij = 0.5*( 1 - cos(theta_i - theta_j)). The thing is "hclust" will not accept this distance matrix, i tried to put it in a data frame, but again i get an error message

Hello, Im working on a 50933 point count bird abundance dataset. I've succeeded in calculating a distance matrix for this entire set, but I don't have sufficient memory to convert this to a matrix, as below... abun.dist <- dist(abun.mat[1:50993,1:235) test <- rowMeans(as.matrix(abun.dist)) Error in matrix(0, size, size) : too many elements specified ive been able to run a hclust()

>>>>> "Simon" == Simon Urbanek <simon.urbanek@math.uni-augsburg.de> >>>>> on Sun, 5 Dec 2004 19:39:07 -0500 writes: Simon> On Dec 4, 2004, at 9:50 PM, ap_llywelyn@mac.com Simon> wrote: >> Source code leading to crash: >> >> library(cluster) >> data(xclara) >>