similar to: latent class analysis with mixed variable types

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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]]

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]]

I'm importing a lot of text tables of data (from Latent Gold) that includes hashes in some of the column names ("Cluster#1", "Cluster#2", etc.). Is there an easy way to strip the offending hashes out before pushing the text into a table or data frame? I thought I'd use gsub, e.g., but can't figure out how to read in a text file without reading it into a table or

Hello there, I would like to test the agreement amongst 6 raters for nominal data on a scale from 1-4, and conduct a latent class analysis in R. How should the data be formatted and what code should I use? Thank you very much Lisa Wang Princess Margaret Hospital Biostatistics tel:416 946 4501

Hi, I perform latent class analysis on a matrix of dichotomous variables to create an indicator of class/category membership for each observation. I would like to know whether there is a function that selects the best fit in terms of number of classes/categories. Currently, I am doing this with the lca() function of the e1071 package. This function requires me to specify the number of classes

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 R users, I would like to perform Latent Class Logit Models for the analysis of choice experiments in environmental valuation. This kind of analysis is usually performed with NLogit Software (http://www.limdep.com). I attach the results I usually obtain using NLogit and NLogit model specifications. For Random parameter models and Logit Models I usually perform my analysis with the package

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

I´ve several packages for latent class analysis, but I was wondering if there is a package for continuos variables, which allows latent prfile analysis. Thanks for your help in advance, J Toledo _________________________________________________________________ cial-network-basics.aspx?ocid=PID23461::T:WLMTAGL:ON:WL:en-xm:SI_SB_1:092 [[alternative HTML version deleted]]

Hi all, I have a question, is there any R package dealing with latent transition analysis with both categorical and continuous indicators? So far what I found from GOOGLE are only packages dealing with latent class analysis. So what about the longitudinal situation? Any way we could look at the transition from one class to another across time points? Thank you very much. ya [[alternative

What's the best approach to running latent class analysis with R? I've downloaded both randomLCA and poLCA packages, but I am interesting in running a standard LCA with individual records (not frequency table) as input data. Wen Gu John Jay College of Criminal Justice445 West 59 StreetNew York, NY 10029 wgu@gc.cuny.edu _________________________________________________________________

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

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]," ") }

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

Does anyone know how to program mover stayer models in R? They are a type of Markov chain model where there is a group of people who stays in the same place, a group who always moves, and a group who does both. Thanks. Leanne Sanders sanders.486@osu.edu [[alternative HTML version deleted]]

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 folks, I'm just learning how to use maps. As an initial foray, I'm mapping the states that have "duty to retreat" (blue) and "stand your ground" (red) self-defense standards. Here is my extremely naive script: dtr <- c('alabama', 'arizona', 'conneticut', 'delaware', 'dist of columbia' , 'hawaii',

Hi Can anyone please suggest me a package where I can estimate a non-linear regression model? One of the independent variables is latent or unobserved. I have an indicator variable for this unobserved variable; however the relationship is known to be non-linear also. In terms of equations my problem is y=f(latent, fixed) q=g(latent) where q is the indicator variable For me both f and g are

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,