similar to: negative binomial latent class regression in package flexmix

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

Hello list, I am not sure if the terminology that I am using here is widely used, however, I provide an example in the hopes that my problem will become clear. My basic problem is that I am unsure of how to 'constrain' my model estimates to reproduce the aggregate (by factor levels) observed dependent variable for a negative.binomial model. I realize this sounds rather vague, so I provide

Dear list, Can anyone tell me how to obtain the rank of a sparse Matrix, for example from package Matrix (class dgCMatrix)? Here is an example of QR decomposition of a sparse matrix (from the sparseQR class help). library(Matrix) data(KNex) mm <- KNex$mm str(mmQR <- qr(mm)) Similarly, using the functions/classes from the relatively new MatrixModels package: library(MatrixModels)

Dear list, I'm wondering if anyone can help me calculate the deviance of either a zeroinfl or hurdle model from package pscl? Even if someone could point me to the correct formula for calculating the deviance, I could do the rest on my own. I am trying to calculate a pseudo-R-squared measure based on the R^{2}_{DEV} of [1], so I need to be able to calculate the deviance of the full and null

Dear list, I am trying to plot a radial dendrogram using the ape package, which requires my data to be of class 'phylo'. Currently I have my dendrogram stored as an object of class 'dendrogram' which was produced from an outside bit of C code, but was made into an object of class 'igraph.eigenc' and converted to a dendrogram using 'as.dendrogram()' from the igraph

Dear list, I posted essentially this same question to the r-sig-geo mailing list last week with no response :( Unfortunately I am no closer to reaching a solution, so I now post it here (with some clarifications) in the hope that someone following this list might have an answer for me: Has anyone had much experience with spatial interaction (or gravity) models, specifically in the form of

Dear list, I have been working with the MatrixModels package quite a bit this week, and it is proving to be extremely valuable for my current work (I am working with several factors with many levels, leading to a sparse model matrix). However, as my knowledge of statistical theory leaves much to be desired, there are certain aspects of model evaluation etc that I am having trouble with. Has

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

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

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:

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

I would like to extract the information criteria from an hlme object (lcmm package). Would you please advise me on how to extract just the BIC (AIC)? On another topic, when I apply hlme (lcmm) and flexmix (flexmix package) to the same data set, I get BIC values that suggest two different latent class structures. Flexmix using FLXMRlmm predicts two classes while hlme says 4. Can you suggest a

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

Hi everyone? Does anybody know how can I estimate a Latent class multinomial (or conditional) logit using R? I have tried flexmix, poLCA, and they do not seem to support this model. thanks in advance adan -- View this message in context: http://r.789695.n4.nabble.com/Latent-class-multinomial-or-conditional-logit-using-R-tp4230083p4230083.html Sent from the R help mailing list archive at

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

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

Dear R-help listers, I am a new convert to R. I am trying to use a r package to conduct latent class analysis as a triangulation check of my cluster analysis using the cluster package in R. I have about 30 cases and 6 indicators, some of which are binary indicators and others are ratio-level variables (percentages). I looked around for information in flexmix, lca, and poLCA, and couldn't find

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 [

As far as I know, there are 2 libraries for latent class regression, flexmix and mmlrc. Since I don't have experience with either one, can someone give me some advice which library is better? Thank you so much.