I'm trying to estimate a 3-class GMM. I have set quad var to zero but want to allow i & s var to differ across classes. I specify random starts with starting values. C#1 needs the s var to be fixed @0, and s var are est separately for c#2 & c#3. When I order the class starting values to put the largest class last and re-estimate, the means "move", but the fixed s var "stays" with the c# it was fixed for. Now, the new model gives me the same "not pos definite" warning for the class that used to be c#1, and the new c#1 has the s var fixed at zero. How can I fix the var for a class allowing other class var to differ, and still get a stable solution? Here are example starting values:
Can't you do a run with the s variance free and request SVALUES in the Output command to save the estimates. Then you do a new run with Starts=0 and - if you like - switch the classes around to get the largest class last (the class logits won't be right, but you can refigure those if need be), and fix the s variance in the class that you want it fixed.
Thanks Bengt - Again, forgetting the basics! I've been using the final estimates to fix the starting values for the next run, but the result from your suggestion was much more informative.
I got starting values, but the result from the 3-class solution produced a third class that was only 3% of the sample, and (1) the best three LL nonreplicated with 800 random starts, (2) 29 parameters fixed to avoid a singular information matrix, and (3) non-pos def cov matrices for two classes involving i in C#1, and q in C#3.
From this, I'm guessing that I'm trying to squeeze water from a stone, and the program is telling me in a more fundamental way (than the BIC, VLMR, and BLRT), that 3 classes won't fit the data better than 2 classes without using very sample-specific fixed parameters and starting values.
Letting growth factor variances vary across classes can be dicey. A good way to approach it is to first go by the default which holds the variances class invariant, then look at the plots for estimated class-specific mean growth curves versus observed curves for people most likely to be in each class. That can show if one class seems to have less/more observed variability than the other classes. Then go and free the variance(s), such as for the intercept growth factor, for that class.