similar to: new version of mclust available

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

> > However... I could become an R convert if I'm told it's > > > > a) mind-bogglingly simple to install on Win Nt > > there are firm limits to my geek credentials > > b) can handle cluster analysis with 2000 records (mclust, preferred, > > although I'd take kmeans if nec'y). Splus 5.0 on the > > Manchester Cray has been falling

Hi, Marion, I believe the package FlexMix provides a more generalized version of finite mixture modeling than is found in mclust/mclust02. Please see: http://cran.r-project.org/doc/vignettes/flexmix/flexmix-intro.pdf Karen --- Karen M. Green, Ph.D. Karen.Green@sanofi-aventis.com Research Investigator Drug Design Group Sanofi Aventis Pharmaceuticals -----Original Message-----

I am trying to follow the mclust examples in "MCLUST: Software for Model Based Clustering, Density Estimation and Disriminant Analysis" by Chris Fraley and Adrian Raftery, but I cannot reproduce the density and uncertainty surfaces for the Lansing Woods maples. I am using R 1.8.1 with the code below. The same code works fine in S-Plus 6.2 Am I missing something or is this a bug?

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'm trying out flexmix and mclust for the first time on some univariate data which is typically best described as lognormal, but can sometimes be gamma distributed as well. I first tried using EM on mclust assuming the data was lognormally distributed and could only get it to work in "E" mode, i.e. the equal variance mode. I could never get it to work on "V" mode [

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

I'm sorry, this is probably a very simple question. I am relatively new to R and am having difficulty understanding how to use the mclust options to change the prior probabilities for a mixture model. I am dealing with a situation where I have 2 groups of data and it is known that the sample sizes are different in the population. I want to incorporate the probability associated with

Hello everyone. I have a few MCLUST questions and I was hoping someone could help me out. If you’re an MCLUST user, they will likely be pretty easy to answer. Thanks in advance for any help. Ken What are the pros/cons of starting a finite mixture model at the “m” step versus the “e” step (where “m” is the maximization step and “e” is the expectation step of the EM algorithm)? In

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

Hello, I'm trying to use Mclust to fit a Gaussian Mixture Model to a mulitdimensional data set. Because of the specific source of my data, I know that all components have the same variance and that the covariance between dimensions is zero (modelname=VII). Furthermore, I have a reliable estimate of the variance of the components. I want to to use this estimate as a prior in mclust, hoping

Dear all, I am attempting to model some one-dimensional data using Gaussian mixture model with mclust. Generally, the data that I have have 3 overlapping populations (with one of them being the majority, and the other two combining to less than 15%) and for some reason, mclust consistently ignores the smaller peaks, giving me strange values for the means (completely anti-intuitive in terms of

> No HTML this time. Sorry Dear all, I am attempting to model some one-dimensional data using Gaussian mixture model with mclust.? Generally, the data that I have have 3 overlapping populations (with one of them being the majority, and the other two combining to less than 15%) and for some reason, mclust consistently ignores the smaller peaks, giving me strange values for the means

Hello All... I gave a task to my students that involved using mclust to look for clusters in some bivariate data of isotopes vs various mining locations. They discovered something I didn?t expect; the data (called tur) is appended below. p <- qplot(x = dD, y = dCu65, data = tur, color = mine) print(p) # simple bivariate plot of the data; looks fine mod1 <- Mclust(tur[,2:3]) mod1$G mod2

Dear R users, I use mclust to fit a mixture of normal distributions to many datasets. Usually the Mclust function finds 1 or two normal distributions, rarely, 3. But I hit a strange case today. my.data <- c(57.96920, 51.79415, 51.20538, 55.53637, 51.64291, 56.61476, 51.28855, 55.56169, 51.85113, 54.03330, 51.37370, 49.48561, 52.41580, 53.51176, 60.49293, 55.77012, 51.59270, 56.29660,

Hi all! I have an ordered vector of values. The distribution of these values can be modeled by a sum of Gaussians. So I'm using the package 'mclust' to get the Gaussians's parameters for this 1D distribution. It works very well, but, for input sizes above 100.000 values it starts taking really forever. Unfortunately my dataset has around 4.6M values... My question: is it

Dear R people, I need to do some work with clustering, but know next to nothing about it at present. R has (at least) three clustering packages, cluster, mclust, cclust. I was wondering if someone can direct me to some good books where I could find documentation and background on the functions in these packages. The html help in these packages lists the following as references. Can people

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.

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

Robust Covariance Estimation Software via Nearest Neighbor Variance Estimation (NNVE) Software to carry out robust covariance estimation by Nearest Neighbor Variance Estimation (NNVE) [Wang and Raftery (2002, J. Amer. Statist. Ass.)] is now available for R and Splus. In the simulation studies published in JASA, this had mean squared error at least 100 times smaller than that of other leading