similar to: model based clustering with flexmix

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,

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

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 [

Hello list, Has anyone had any luck creating an M-step driver for negative binomial regression for use with package flexmix? I've had a look here: http://cran.r-project.org/web/packages/flexmix/vignettes/flexmix-intro.pdf as well as poking around in the flexmix source, but I haven't had much luck getting anything to work. I can't figure out how to a) come up with an initial estimate

Hi, I am using the package "flexmix" and would like get some assistance. I am trying to run two equations jointly Y1=X1B+E1 Y2=X2G+E2 So that I have X and Y in a matrix format and would like to run the latent class model using flexmix. Though, my problem here is that Flexmix automatically generates intercepts. I have intercept for both of the equations that my X matrix looks like

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)

I am trying to estimate a latent class multinomial logit regression with flexmix. I am not sure if I should do it as follows: m4<-flexmix(cbind(y,1-y)~x1+x2|id,model=FLXMRglm(family="binomial"),data=NPreg,k=2) , where id links each row with the corresponding respondent. Each respondent has 4 alternatives to chose from. y takes the value 1 only for the alternative chosen; x1 and

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 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

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

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

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 list: I would like to recreate how the artificial data set BregFix was generated in package flexmix (thanks Bettina and Friedrich). The data set is thoroughly described in Grun and Leisch's Computational Statistics & Data Analysis 51(11) :5247-5252 but references to the appropriate seed number(s) are missing (providing these details was certainly beyond the scope of the authors'

Hi, I was going through the examples in smartpred. It seems there's an unstated dependency on the fitted() function in package flexmix. n = 20 set.seed(86) x = sort(runif(n)) y = sort(runif(n)) library(splines) fit = lm(y ~ ns(x, df=5)) plot(x, y) lines(x, fitted(fit)) # won't work w/o prior loading of the flexmix package. newx = seq(0, 1, len=n) points(newx, predict(fit,

Hi, I am trying to understand how to use the flexmix package, I have read the Leisch paper but am very unclear what is needed for the M-step driver. I am just fitting a simple linear regression model. The documentation is far from clear what the FLXmclust function does, but, it in principle could do all I need, however, I do not get sentible results as if I try the following the result is poor:

Hi all, Just a small question: After fitting a multivariate mixture using flexmix, I wish to use refit to get the parameters of covariates and their standard errors. However using refit I only can see the components for the first dependent variable. What should I do if I want to see the others? Thanks a lot, Eric -- View this message in context:

Hi, I am currently using package ?mice? to do multiple imputation for tourist data here. Then the complete datasets (e.g. m>= 100) will be analyzed with a finite mixture model using package ?flexmix?. I have found that it seems no feasible way to combine the multiple results into a final one smoothly using just a few commands. By the way I have seen that package like ?semTools? has such

Hello, I'm trying to cluster gene expression data as in http://bioinformatics.oxfordjournals.org/content/28/2/222.full It looks like they are using some combination of FlexMix and mgcv but I haven't been able to figure out the exact steps necessary to perform the clustering. Has anyone else used FlexMix to do clustering on this type of data before? Any pointers would be much

Dear R-helpers & Friedrich Leisch, I'm trying to fit a mixture of regression models on attached data set by doing the following: agl <- read.table("agl.txt") mod1 <- flexmix(resp~gng+csc|Subject,data=agl, model=FLXglm(family="binomial"),k=2) The result is a (varying) number of the following warnings: Warning messages: 1: non-integer #successes in a