similar to: Which package to use when fit Mixture regression model with ......................

I've been trying with no success to model mixtures of Gamma distributions using the package flexmix (see examples below). Can anyone help me get it to model better? Thanks very much. -Ben ## ## Please help me get flexmix to correctly model mixtures of ## Gamma distributions. See examples below. ## library('flexmix') ## ## Plot a histogram of dat and the Gamma mixture model given

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

Dear R-users, I would like to use the package flexmix to fit latent classes to a regression model. My data are repeated measurements of bernouilli variables so I can use the binomial family link to the glm function. The design is not balanced, meaning that for some individuals in my data set I have 10 measurements or more, for others I only have 5 or even less. My question is the following. Can

Hello all, I would like to fit a mixture model whose components are normal distributions confined in a closed interval. Since there are already several packages for EM, I would like to extend one of these instead of writing a new script from scratch. What would be the best way to customize such an existing package for doing that? Which one is the best in terms of extensibility? (e.g mixtools,

gamlss.mx can fit these I believe (although no experience with these myself) flexmix may be (relatively easily) adaptable to accomplish this as well hth, Ingmar Ingmar Visser Universitair Hoofddocent ontwikkelingspsychologie | Directeur College Psychologie Afdeling Psychologie | Faculteit Maatschappij- en Gedragswetenschappen | Universiteit van Amsterdam Bezoek | Nieuwe Achtergracht 129B | Kamer

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Dear Listers, My post might be somewhat OT. Currently, I am trying to use flexmix to build a finite mixture model. For instance, I am getting the prior probability and coefficients for each latent class from training data. Is there a way to get the posterior probablity and prediction of a new dataset? What I am thinking is to apply the prior prob and coefficient from training set to testing data

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 [

Dear R Users, R Core Team, I currently wonder how to predict the probability of an event with new data resulting from a finite mixture. I read the documentation of the flexmix package and the examples of applications provided on CRAN but I could not find how to predict (except "manually" but I am looking for a simpler solution) the final probability of the mixture (for each individual)

Dear useRs, FlexMix version 1.0-0 has been released on CRAN. FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based

Dear useRs, FlexMix version 1.0-0 has been released on CRAN. FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based

I would like to fit a gaussian mixture model to a vector with about 50,000 points. I have tried using Mclust to do so, but 50,000 points requires more memory than I have (and I am running with 4Gb). Any other suggestions for how to do so? Oh, I don't know the number of components, but the number will likely be less than 5 or 6. Thanks, Sean

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-----

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 Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

Dear all, Does anyone know if the mixture analysis (i.e : sexual dimorphism, age group,…) can be done with R software ? Thanks for your help Dan --------------------------------- [[alternative HTML version deleted]]

Hello all, I am trying to fit a truncated mixture model and I wrote a driver for flexmix following the example in the vignette, but it doesn't work for me: it assigns all data points to one component only, e.g.: > > source('bugged.R') > > Call: > flexmix(formula = x ~ 1, k = 2, model = truncatedmodel(lower = -4, > upper = 4)) > > prior size

Dear R users: I am wondering how to ask for *fixed* number of distributions under parameterized Gaussian mixture model. I know that em() and some related functions can predict the parameterized Gaussian mixture model. However, there seems no parameter to decide number of distributions to be mixed (if we known the value in advance). That is, assume I know the (mixed)data is from 3 different

Problem on flexmix when trying to apply signature developed in one model to a new sample. Dear R Users, R Core Team, I have a problem when trying to know the classification of the tested cases using two variables with the function of flexmix: After importing the database and creating a matrix: BM<-cbind(Data$var1,Data$var2) I see that the best model has 2 groups and use: ex2

Dear All; I tried to use fitdistr() in the MASS library to fit a mixture distribution of the 3-parameter Weibull, but the optimization failed. Looking at the source code, it seems to indicate the error occurs at if (res$convergence > 0) stop("optimization failed"). The procedures I tested are as following: >w3den <- function(x, a,b,c)