Problems of non-convergence PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Rowan Smeets posted on Monday, July 09, 2018 - 8:31 am
I ran into problems of non-convergence in conducting a GMM with free variances. Although the best loglikelihood has been replicated, the systems says that a number of perturbed starting value run(s) did not converge. Even if a gradually increase random starts (from 20 4, to 100 20, to 200 40, to 400 80 and so on), I still get this warning that a number of perturbed starting values did not converge. Is this really something to be worried about? Can I take any other measures to overcome non-convergence?
 Bengt O. Muthen posted on Monday, July 09, 2018 - 10:52 am
GMM with free variances is a very flexible model for which there may be relatively little information in the data to estimate its parameters. This is reflected in the many non-convergencies. But if you have a substantial number of best logL convergencies you need not worry too much about the solution - although with a different sample in the future, it may may be hard to replicate your findings.
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