similar to: Error on mclust

Dear group, I want to use the mclust package on large data, and therefore I want to use a subset in the initial clustering phase. From help(mclust): k: If `k' is specified, the hierarchical clustering phase will use a sample of size `k' of the data in the initial hierarchical clustering phase. The default is to use the entire data set. m2 is a

Hi, all; I got an unusual error while using mclust library 2.1-7 on R 2.0.1. When I tried to run Mclust(rnorm(100)), I got segmentation fault error. Does anyone know what causes this problem? I came across the same problem even when I tried to run the example shown in Mclust help using iris data. Thanks in advance, Tae-Hoon Chung -------------------------------------------------- Tae-Hoon Chung

Hi, this is my first time using the nlme package, and I ran into the following puzzling problem. I estimated a mixed effects model using lme, once using groupedData, once explicitly stating the equations. I had the following outputs. All the coefficients were similar, but they're always slightly different, making me think that it's not due to numerical error. Also, what is the

Dear All; While using EMclust() in the mclust package, I frequently encountered a program error. A message window popped up with the message " Rgui.exe has generated errors and will be closed by Windows. You will need to restart the program. An error log is be created." > version _ platform i386-pc-mingw32 arch i386 os mingw32

Dear Developers: I have a question about memory management in R 2.2.1 and am wondering if you would be kind enough to help me understand what is going on. (It has been a few years since I have done software development on Windows, so I apologize in advance if these are easy questions.) ------------- MY SYSTEM ------------- I am currently using R (version 2.2.1) on a PC running Windows 2000

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need

Hi I have two questions, the first perhaps dumber than the second. Firstly, I have a data set, and when I plot a histogram it looks like a normal distribution. So I want to overlay a bell-shaped normal distribution on top of it, to demonstrate how similar it is to the normal distribution. I have read the help on dnorm(), rnorm(), pnorm() etc but still can't figure out how to plot a normal

Dear R-List I have a question about how to interpret BIC as a goodness-of-fit statistic. I was trying to use "EMclust" and other "mclust" library and found that BIC was used as a goodness-of-fit statistic. Although I know that smaller BIC indicates a better fit, it is not clear to me how good a fit is by reading a BIC number. Is there a standard way of interpreting a BIC

My question is with respect to mCLUST and the values of BIC and log likelihood. The relevant part of my R script is: ######################### BEGIN MDS ANALYSIS ######################### #load data data <- read.table("Ecoli33_Barry.dis", header = TRUE, row.names = 1) #perform MDS Scaling mds <- metaMDS(data, k = Dimensions, trymax = 20, autotransform =TRUE, noshare = 0.1,

Dear R-users, I have a two - dimensional data set which needs to be clustered into groups: I'm searching for groups of points which show a positive correlation (in a twodimensional plot of the data set), but I do not have any knowledge about how many groups there might be. Do you know of a clustering algorithm in R (or in general) which can use a-priori information about the cluster's

Dear Knowledgeable R Community Members, Please excuse my ignorance -- I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance regarding parameters in 2 functions in the "mclust" package. -------------------- PROBLEM DESCRIPTION -------------------- I have a finite mixture modeling problem -- for example, a 2-component gaussian

Hello. I know my dissimilarity matrix but not my original data. Is there any way i could use the clustering function Mclust or EMclust with this dissimilarity matrix? or at least some equivalent of these functions? As this is model based clustering i dont know if it is actually possible to do it without the original data thanks in advance for your help [[alternate HTML version deleted]]

I've been trying to install some R packages such as mclust and flexmix on linux but have had the following error messages. > I've been trying to install mclust on my notebook which has linpus linux lite os and I have installed R as well as some packages all right. However, when I tried to install mclust, it gave me the following messages. Any suggestions? Tim ++++++++++++++++

Hi, All; I tried to use library mclust in 64-bit compiled R 2.0.1 but failed. Installation went smoothly without any warning or error. However, when I tried to use them with the following simple code, it crashed. Library(mclust) Dat <- c(rnorm(20, mean=0, sd=0.2), rnorm(30, mean=1, sd=0.2)) Ind <- Mclust(dat, 1, 5)$classification cbind(Dat, Ind) The error message was:

Dear All, I have spatial data (presence/absence for 4000 squares) on 250 bird species and would like to use a model-based clustering technique to test for species associations. Is there any way of passing a distance/correlation matrix to mclust as with hclust, rather than the actual data? Or alternatively, is there a way of getting mclust to handle binary data? I'd appreciate any

While looking at someone's question on this list led me to the mclust package, and from there to its license. Excerpts: Except for strict academic use, use of MCLUST (by itself or through other packages) requires payment of an annual license fee and completion of a license agreement found at the following URL: http://depts.washington.edu/ventures/UW_Technology/Express_Licenses/mclust.php 1.

I'm trying to test a method of identifying individuals (birds) based on measured data (their calls). I have test data from known individual birds, and I am using the Mclust package to see if the program can correctly identify which calls come from different birds. So far, mclust has correctly ID'd the number of birds in the test data set (i.e., the correct # of clusters). However I

Hi, I'm trying to use the library mclust for gaussian mixture on a numeric vector. The function Mclust(data,G=3) is working fine but the fitting is not optimal and is using modelNames="E". When I'm trying Mclust(data,G=3,modelName="V") I have the following message: Error in if (Sumry$G > 1) ans[c(orderedNames, "z")] else ans[orderedNames] : argument is

Hi all, once more about the old subj :-) My data has too much various distribution families and for every particular experiment I need just to decide whether the data is "quite homogeneous" or it has two or more clusters. I've revisited the following libraries: amap, clust, cclust, mclust, multiv, normix, survey. And I didn't find any ready-to-use general