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Mplus Discussion > Growth Modeling of Longitudinal Data >
 Jungeun Lee posted on Friday, December 14, 2007 - 4:38 pm

I am working on a growth mixture model (outcome=continuous, estimator=MLR, type=Mixture Missing). When I increased the number of class=4, I encountered the following error. In my current mplus input for this model, STARTS =500 20. I am puzzled about what I can do about this...

 Bengt O. Muthen posted on Friday, December 14, 2007 - 5:51 pm
You can increase the number of random starts further until the best LL is replicated. If you have problems replicating it for many random starts, this might indicate that you are trying to extract too many classes - the data don't show signs of that many classes.
 linda beck posted on Monday, August 18, 2008 - 3:07 am
I have a very complex 3 class model and used starts = 1500 50.

my loglikelihoods were:


Since I get no "loglikelihood warning message", can I trust this solution with only imprecisely replicated values? I'm aware of the possibility of extracing too many classes with this 3 class solution (as posted above). I only want to use the BIC and tech11 here, in order to have arguments for my preferred 2 class solution, where I had no problems to replicate loglikelihood values exactly.

thanks, linda
 Bengt O. Muthen posted on Monday, August 18, 2008 - 6:37 am
The way to check if the first LL is close enough to the second is to check if it gives approximately the same solution in terms of parameter estimates. This in turn can be determined by using the seed for the second LL as "OPTSEED" in a new run where you inspect the parameter estimates and compare them to those of the first LL run.
 linda beck posted on Tuesday, August 19, 2008 - 2:10 am
thank's for that advice. The second LL isolates different kinds of classes, which was often the case when I tried to compute 3 class models with that data.
Despite that instability also speaks for 2 classes, what can one do to force LL replication? I have heard of increasing both values in the starts option, increasing stiterations, increasing number of integration points... Did I miss an option?
 Linda K. Muthen posted on Tuesday, August 19, 2008 - 7:28 am
You can increase starts to as many as 5000 100 and decrease MCONVERGENCE. If this does not help, you may need to use a simpler model.
 linda beck posted on Tuesday, August 19, 2008 - 9:44 am
Thanks, I've done exactly what you recommended before you posted it, funny... It is still running. One additional question: How can one deal with nonconverging perturbed starting values? There was a message that some did not converge under the list of LL-values (I guess this is for the final stage optimizations) and above the list of LL-values.
 linda beck posted on Tuesday, August 19, 2008 - 9:47 am
add: in another post Bengt recommended to switch to stscale=1. Is that an option?
 Linda K. Muthen posted on Tuesday, August 19, 2008 - 11:05 am
If you increase the starts to 5000 it will take more time than 100. If you have further questions about output, send your files and license number to support@statmodel.com.

You could try STSCALE=1; You can also send your files and license number to support@statmodel.com.
 Carey posted on Sunday, February 08, 2015 - 10:21 am
I am slightly confused with how many random starts to use in my LPA. Should the random starts change the solution? If it does, what does it mean?
 Linda K. Muthen posted on Sunday, February 08, 2015 - 11:28 am
You should use enough random starts so that you replicate the best loglikelihood. If you do not, you have reached a local solution. Use, for example, STARTS = 200 50 or more where the second number is 1/4 of the first number.
 J.D. Haltigan posted on Friday, September 07, 2018 - 4:51 am
Is it more generally the case that the more complex the model, the larger number of random start sets one is likely to need to replicate the best LL?
 Bengt O. Muthen posted on Friday, September 07, 2018 - 2:24 pm
Yes because the likelihood is not as smooth and pointed.
 Margarita  posted on Monday, October 08, 2018 - 8:54 am
Hi Dr. Muthen,

I wonder if there is a specific reason why you suggest (in the output) running the same model twice with twice the random starts? I understand that this is a good advice to avoid hitting local maxima. However, is this always a requirement? For instance, would 2 runs of the same model (1000 250/2000 500) offer more information than running it only once but with as many starts as the 2nd run e.g. 2000 500?

Thank you,
 Bengt O. Muthen posted on Monday, October 08, 2018 - 10:39 am
Q1: No.

Q2: No.

This output suggestion is simply meant to avoid using a solution that is based on premature stoppage by PSR. If you have a very long run and have seen PSR close to 1 for many iterations, you don't need to do this follow-up check.
 Margarita  posted on Monday, October 08, 2018 - 2:04 pm
that's great thank you!

Apologies, just realised that this thread is under growth modelling and MCMC and not general random starts. I assume the same reasoning applies to ML-EM with integration, but one would have to see close to zero abs & rel change?
 Bengt O. Muthen posted on Monday, October 08, 2018 - 2:55 pm
To some extent it is a similar story but with ML it is possible to have the more exact convergence criterion of close to zero first-order derivatives.
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