Message/Author 

xue li posted on Friday, June 16, 2006  12:32 pm



Hi, every one: There are 3000 clusters, within each cluster there are 10 subjects . Each subject has a depent variable measured 0 or 1, and each subject has no covariate. I want to use two level mixture model to analysis this data. The reason is the observed data are grouped into 2 classes. Class one has mean 3 and variance approximately 0; and the second class has mean 0 and variance approcimately 4.Approximately 37.7% subjects are in class 1. The problem is the model never converge. Could someone tells me whether there is mistakes in my program. Thanks. my email xli28@uic.edu I use the following code. Title: Mixture model for breast cancer projectrace by age specific poverty twolevel logistic regression Data: !file is C:\xli28\project1\EB\mplus for EB\EB6ageg.dat; file is C:\xli28\project1\EB\sim_age3.dat; !file is F:\xue new\sim_age3.dat; variable: names are ncluster povind int; usevariables are povind; missing are . ; classes = c (2); categorical=povind; cluster=ncluster; analysis: type =twolevel random mixture; algorithm=integration; estimator=ML; integration=GAUSSHERMITE(15); adaptive=on; model: %within% %overall% ipovind@0; [i]; i; %c#1% [i*3]; i@0; %c#2% [i*0]; i*4; %between% %overall% [povind$1@0]; output: tech1; tech8; 

xue li posted on Friday, June 16, 2006  12:37 pm



Hi, the error message is as follows. Since the data set is created by simulation. The starting value will be plausible. I can't figure out the reason. Error message: Unperturbed starting value run did not converge. THE ESTIMATED WITHIN COVARIANCE MATRIX IN CLASS 1 COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 1. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE ESTIMATED WITHIN COVARIANCE MATRIX IN CLASS 0 COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 1. CHANGE YOUR MODEL AND/OR STARTING VALUES. SERIOUS PROBLEM IN THE OPTIMIZATION WHEN COMPUTING THE POSTERIOR DISTRIBUTION. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE LOGLIKELIHOOD DECREASED IN THE LAST EM ITERATION. CHANGE YOUR MODEL, STARTING VALUES AND/OR THE NUMBER OF INTEGRATION POINTS. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. MODEL RESULTS Estimates Within Level Latent Class 1 I  POVIND 1.000 Means I 3.000 Variances I 0.000 Latent Class 2 I  POVIND 1.000 Means I 0.000 Variances I 4.000 Between Level Latent Class 1 Thresholds POVIND$1 0.000 Latent Class 2 Thresholds POVIND$1 0.000 Categorical Latent Variables Within Level Means C#1 0.000 


I'm running a mixture twolevel model in mplus. However, I would like to define the latent class variable as a between level variable. In other words, I would like to classify 'clusters' (in the mplus multilevel language). How can I do this? I tried including the statement BETWEEN = c; but it looks as if this statement is being ignored. The reason why I want to classify at the between level is that my respondents are situated at this level; the within level has 18 ratings of different experimental scenario's by each respondent. I'm not interested in segmenting at this level (because I would probably end up with 18 classes corresponding to the 18 ratings), but at the respondent level. In the previous discussion I found the following statement  has this been implemented? "In future Mplus versions, we will also have a latent class variable varying on the between level. That would be a schoollevel variable, classifying different schools." 


See Example 10.2. If this is being ignored, perhaps you are using a version of Mplus where this was not yet implemented. 


I was wrong. "between=c" is not being ignored, but I misinterpreted the output: it lists the class counts in terms of level 1 (the within level), but I now see that observations from the same cluster are indeed assigned to the same class. Thanks. 

M.Heidari posted on Thursday, July 21, 2016  9:50 pm



Hi, Is it possible to run multilevel mixture factor models in Mplus? Is it related to example 10.4 & 10.5? Thank you very much. 


Yes to both. 

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