similar to: question about mclust and prior probabilities

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 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

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

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, 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

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

I'm running Mclust 3.0 in R-2.7.0 and have run into a situation where the optimal number of clusters, 12, seems to be greater than the number of colors available to Mclust. The code below, which demonstrates errors, does not if the max no of clusters is set at 10. sessionInfo, after a restart of R and loading of packages, follows the output. Is this a known problem with Mclust? Can I

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

A new version of mclust is now available as a contributed package on CRAN. The associated manual is located at http://www.stat.washington.edu/www/research/reports/2006/tr504.pdf The main feature in terms of new functionality is the option to include a Bayesian prior in the mixture model for regularization. The older version is still available mclust02 for those who need backward compatibility.

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

I am following instructions online for cluster analysis using the mclust package, and keep getting errors. http://www.statmethods.net/advstats/cluster.html These are the instructions (there is no sample dataset unfortunately): # Model Based Clustering library(mclust) fit <- Mclust(mydata) plot(fit, mydata) # plot results print(fit) # display the best model This is what I did and the error I

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

I am new to r and mclust. I have version 1.7.1 of r, precompiled for Classic Mac OS (I am running 9.2.2 on a PowerBook G4). I downloaded the precompiled version 2.0-2 of mclust for Classic MacOS. It appears (from the example I was using below) that I need a library called lapackLib. There does not appear to be a precompiled version at CRAN. There is a makefile for lapackLib, to use with MPW.

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:

Hi All, I am trying to cluster a one-dimensional data (see the file attached) using Mclust() but got an error message like: >Mclust(x) Error in rep(1, n) : Object "n" not found When I do a simulation sometimes it works sometimes doesn't. >Mclust(c(rnorm(50),rnorm(56,-0.5))) Error in rep(1, n) : Object "n" not found >Mclust(c(rnorm(56),rnorm(56,-0.5))) best

Dear all I have a problem in fitting lines() of the normal distributions identified with Mclust on a histogram or a mclust1Dplot. Here is some sample code to explain : set.seed(22) foo <- c(rnorm(400, 10, 2), rnorm(500, 17, 4)) mcl <- Mclust(foo, G=2) mcl.sd <- sqrt(mcl$parameters$variance$sigmasq) mcl.size <- c(length(mcl$classification[mcl$classification==2]),

Hi All, I am using em() function to estimate a poisson-gaussian process from a univariate one dimension time series, but not sure how to do. In the help manual, it specify that in "pro" of the argument "parameter", if the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components. But in the example, the parameter