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 email@example.com
I use the following code. Title: Mixture model for breast cancer project-race by age specific poverty two-level 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;
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.
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.
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 school-level variable, classifying different schools."
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.