similar to: finite mixture modeling

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

As an alternative to Latent GOLD, I'm wondering if anyone knows of and R package that can manage Latent Class Analysis with mixed variable types (continuous, ordinal, and nominal/binary). [[alternative HTML version deleted]]

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

Can anyone point me to a comparision of the various packages avaliable to do Finite Mixture Models or Latent Class Models in R. As ever my googling only produce papers on specific packages or implementations. I am looking of a considered comparision that criticlaly compares them. Failing that just your own opinion or experience would be nice to hear. Best R

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

I am running a latent class regression with 3 nominal and 2 ordinal variables using depmixS4 but the available response models do not include one for ordinal response.  How do I go about this?   Penny [[alternative HTML version deleted]]

Dear R users, Is there any package for Latent Class Analysis (to be used in a clustering application) which supports mixed indicator variables (categorical and continuous)? Alternatively, is there any other clustering algorithm available that supports this type of data? Thanks in advance for your help. Regards, Lars. [[alternative HTML version deleted]]

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Error in optim(method = "BFGS", c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : initial value in 'vmmin' is not finite I am running a logit model with latent class segments. I successfully got estimates for 2 segments. However when I tried to increase the no. of segments, I got this error message at the end. I checked my code again but can't find anything wrong. Is this error

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

We are trying to build a human respiration model. Preliminary analysis of some breathing signals has shown that humans breathe through switching among a finite number of patterns. Hidden Markov seems to be the right approach. Since most of our code is written in R scripting language, finding an R package implementing an HMM that we can use for our prototype would be very helpful. I have been

I don't know of any R package that can match all the functionality of LEM eg fitting equality constraints to model parameters a la LISREL. WRT dumping tables, I would have thought that as.data.frame.table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") }

Package news (see below for general description of functionality) depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in the EM algorithm, most

Package news (see below for general description of functionality) depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in the EM algorithm, most

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

Hello all! I want to estimate parameters in a MIMIC model. I have one latent variable (ksi), four reflexive indicators (y1, y2, y3 and y4) and four formative indicators (x1, x2, x3, x4). Is there a way to do it in R? I know there is the SEM library, but it seems not to be possible to specify formative indicators, that is, observed exogenous variables which causes the latent variable. Thanks,

Do we have any R package that can do analysis on finite mixture model with logistic regression? Thanks Faith Feng Gao Dept. of Statistics Virginia Tech. Email: fgao at vt.edu

Dear All, This is a question somewhat off-topic. Say, if I have known the number of components in the mixture, all the estimated parameters, prior probabilities, and so on for a finite mixture model, how might I compute the posterior probabilities of each case for a new dataset without observed response (Y)? I want to know the parametric form of such calculation such that I can calculate it

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

Are there any S-Plus or R libraries/packages that do Finite Mixture Analysis following the algorithms similar to those implemented in Geoffrey MacLachlan's EMMIX program? Thanks. Dr. Marc R. Feldesman email: feldesmanm at pdx.edu email: feldesman at attglobal.net fax: 503-725-3905 "Don't know where I'm going. Don't like where I've been. There may be no exit. But