dear all, I'm trying to run a two part growth mixture model. I did a lcga for the binary part first.
1.) 3 or 4 class solutions fitted better than a one class or two class solution, but I get bad average post probabilities for the 3 or 4 classes. If I have more than two groups, at least on group has a averagepost-prob of 60-70 (diagonal of matrix). What is an acceptable value? I thought of 80!? Are there any chances to isolate more than two meaningful and valide groups in a lggmm of boh parts? Is it worth it to proceed?
2.) I went to joint LGMM of both parts. I used the means of the growth factors lcga in the following lgmm as starting values, but it didn't converge.
After nearly 2 hrs, I get the following messages: THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-ZERO DERIVATIVE OF THE OBSERVED-DATA LOGLIKELIHOOD.
THE MCONVERGENCE CRITERION OF THE EM ALGORITHM IS NOT FULFILLED. CHECK YOUR STARTING VALUES OR INCREASE THE NUMBER OF MITERATIONS. ESTIMATES CANNOT BE TRUSTED. THE LOGLIKELIHOOD DERIVATIVE FOR PARAMETER 20 IS -0.55772741D-01.
Any suggestions with regard to model convergence, additional starting values, may be variances? I was inspired by the recent Hser et al. article (2007). I think they used one latent class for both model parts. Is it significantly more computational demanding to use 2 latent class variables for both parts separately?
1. I would say any entropy value is acceptable in the sense that entropy does not have to do with model fit or the best choice of number of classes. But to be useful for classification, I would think you want higher numbers.
2. Just increase to say miter=1000; as suggested in the error message.
2. I will try but I'm nt very optimistic that it works.
I'm very interested in isolating a "zero-group" (structural zeroes). How do I specify such a group? I've heard of threshold = 15 for the binary part and zero mean for intercept and slope of the continuous part. Do I have to add zero means of the growth parameters for the binary part or is threshold = 15 sufficient?
o.k., I have given it up, it is far too complex and miles away from convergence. I focus on an alternative, namely to isolate groups within the entire model (both parts) on the basis of LCGA, which is often done by people who use SAS (group based approach, Nagin). I know the disadvantages (too many groups may be isolated by LCGA) and it is rough and dirty but may be sufficient for me. Entropy looks very good.
1.)Is LCGA compatible with two-part in general? I only have read an article by Bengt, using LCGA on the binary part (but not on both parts simultanously).
Two-part modeling requires that the two parts correlate in some way. Generally this is through the growth factors. If you do an LCGA, the growth factor variances are fixed at zero and there can be no growth factor covariances between the two processes. LCGA can be done with a two-part model if the latent class variable influences both parts. The following paper on two-part factor mixture may be of interest. It can be downloaded from our website.
# Kim, Y.K. & Muthén, B. (2007). Two-part factor mixture modeling: Application to an aggressive behavior measurement instrument.
o.k., your first statement was the reason for asking this question :-).
"LCGA can be done with a two-part model if the latent class variable influences both parts."
Sorry, this was a missunderstanding. Bengt used LCGA on the separated binary part to get a clue about the number of groups in general. This was followed by LGMM on both parts. Now I'm separating groups on the basis of both parts simultanously with one class variable in my "Variable" command. Your answer implies, that this seems o.k., albeit having no correlated parts within the classes.
thank you again, as always :-) So I have to use the means not adjusted for covariates for the visual plot in my paper, if this is not possible with imputation. I want to calculate confidence intervals for these means. Using "cinterval" only gives me cintervals for my model but not for the estimated means. I there any way to get them out of mplus?
I noticed that the current version 5.2 contains improvements concerning type=imputation. Has anything changed regarding means available in this kind of analysis? This would, for instance, alleviate the exploration of LGMM conditioned on imputed covariates, since sometimes the mean trajectories of the classes look different as compared to unconditional models. So far, the only chance to have a closer look on this issue is with non-imputed conditional models.
I am interested in two-part growth mixture modeling. I did find two papers on this topic("Two-Part Growth Mixture Modeling" and "Trajectories of heroin addiction: growth mixture modeling results based on a 33-year follow-up study"), but could not find any input example Can you advise where I can find the input examples?
It would be an extension of Example 6.16 in the user's guide. I don't have any other example.
Mirim Kim posted on Tuesday, March 21, 2017 - 10:20 pm
Dear Dr. Muthen,
After reading the Kim and Muthen (2009), I want to ask you about the way to specify the distribution of your study. Was it the same way in Muthen and Muthen (2002); How to use a Monte Carlo study to decide on sample size and determine power? How can I set the specific parameter? For example, in the Kim and Muthen (2009), the data generation was based on log-normal distribution (M=-2, sigma=1). It would be really appreciated if you could share your idea.
I think you would have to use mixtures with one class for zero. See for example:
Wall, M. M., Park, J. Y., & Moustaki, I. (2015). IRT modeling in the presence of zero-inflation with application to psychiatric disorder severity. Applied Psychological Measurement. DOI: 10.1177/0146621615588184 view abstract
Samuli Helle posted on Wednesday, February 05, 2020 - 1:01 pm
Thanks Bengt! After reading the article, I am unsure that mixture modeling fits to my data (I might well be wrong). So for each child we have asked some questions about his/her grandparents. But since some grandparents were dead we have missing data (non-response, not zeros) for the questions for those dead grandparents. Thus, I am wondering whether sample selection model would be more appropriate here?
No, not many just one... Any other options in Mplus that I could try? I checked your RMA book and in chapter 10 (Missing Data) there is a NMAR selection model where missing data indicator is regressed on Y (Table 10.18). I tried to run a similar model in my case but Mplus did not even produce an output to show what went wrong. The only difference was that I had a latent variable as an outcome (code clip below), variable "miss" being 1 if data on "x1-x3" was missing and else 0.