similar to: high memory allocation

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

I have "small" problem ... with the cluster library each time I try to use the "agnes","pam","fanny" functions with more than 20000 elements I get the following error: >Error in vector("double", length) : negative length vectors are not allowed >In addition: Warning message: >NAs introduced by coercion But with the clara

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Dear R users, I'm having trouble using aggregate() and would greatly appreciate your advice. I am using R 1.5.1 on Windows 2000. I want to call my function in the following way extract.data(x=dat[, "Age", "Year"]) where extract.data() uses aggregate() to count the number of cases for each combination of "Age" and "Year". I've defined

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()

Hi, I have the graph below: | 422 | 1 321 | 1 | 1 |32 1 |23 |_______________________ The numbers are points in coordinates (simplified graph). I need make a cluster analysis of this for identify groups and make a graph with this groups under circle. In R exist several packages for cluster analysis, but, which the package for this analysis and

I compiled R 2.1.0 under Windows XP SP2 as a preliminary to rebuilding a custom package for use with R 2.1.0. The compile completed successfully, and I was able to run demo(graphics) successfully. But make check and make check-recommended fail. > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 1.0 year 2005

Dear users, is anybody of you aware of a R command to perform laplace transform or even its inversion? Thank you very much. Luca

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

My computer is a pentium 4 running at 2.4 GHz. My R is 1.7.1 I have written a program in R that calls C++. The program spends most of the time in C++ ( > 90% ). R basically deals with output and input. How slower can this be compared with the program I would get from rewriting everything in C++? Thank you.

Hi, I am running a loop to plot multiple plots. In s-plus, it shows multiple pages in the graphic window to allow checking on each plot. but in R, the next plot always overwrite the previous one, so i can only have the last plot produced, is there a way to have multiple pages in the graphic window just like S-plus does? Thanks

Hello, In the documentation for agnes in the package 'cluster', it says that NAs are allowed, and sure enough it works for a small example like : > m <- matrix(c( 1, 1, 1, 2, 1, NA, 1, 1, 1, 2, 2, 2), nrow = 3, byrow = TRUE) > agnes(m) Call: agnes(x = m) Agglomerative coefficient: 0.1614168 Order of objects: [1] 1 2 3 Height (summary): Min. 1st Qu. Median Mean 3rd

Hello! I created a distances matrix for 13 objects using daisy (see the attached file). I am trying to clusteranalyse it using agnes but it's not working. What might be the problem: mydistances<-read.csv("Results of daisy.csv") mycluster<-agnes(mydistances, method="ward") I am getting: Error in agnes(mydistances, method = "ward") : NA/NaN/Inf in foreign

Dear R users, I discovered something strange using the function agnes() of the cluster package on R 3.0.1 and on R 2.14.1. Indeed, the clusterings obtained are different whereas I ran exactly the same code. I quickly looked at the source code of the function and I discovered that there was an important change: agnes() in R 2.14.1 used a FORTRAN code whereas agnes() in R 3.0.1 uses a C code.

I have a directed graph which is represented as a matrix on the form 0 4 0 1 6 0 0 0 0 1 0 5 0 0 4 0 Each row correspond to an author (A, B, C, D) and the values says how many times this author have cited the other authors. Hence the first row says that author A have cited author B four times and author D one time. Thus the matrix represents two groups of authors: (A,B) and (C,D) who cites

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,

I have Windows XP Professional Version 2002 and the R-Version 2.1.1. I did cluster analysis with the cluster package and the agnes (method = ?ward?). The results are satisfactory. But the dendrogram of agnes is confused to work with the results. Is there a tool, I can get a clear arrangement of the results for the cluster analysis. For example a matrix with different numbers for each group.

I have written the following function to calculate the weighted mean difference for univariate data (see http://www.xycoon.com/gini_mean_difference.htm for a related formula). Unsurprisingly, the function is slow (compared to sd or mad) for long vectors. I wonder if there's a way to make the function faster, short of creating an external C function. Thanks very much for your advice. gmd

Hi, Suppose I have a multidimensional array: tmp <- array(1:8, c(2,2,2)) is there a function out there that, given a one-dimensional array index, will return the separate indices for each array dimension? for instance, tmp[8] is equivalent to tmp[2,2,2]. I'd like to derive the vector (2,2,2) from the index 8. thanks, Brad Buchsbaum