Dear Dr Muthen, I am wondering what is MLF ? Mplus User's Guide only provided very limited information. Would you provide more reference with regard to this estimation method? How does it differ from MLR ? When would you consider to use MLF rather than MLR? Thank you.
MLF is a common approach in statistics to computing SEs and is defined in equation (168) of the Mplus Technical Appendices ("through Version 2") on the web site. It uses the sums of products of first-order derivatives. It is a simpler estimator of SEs than ML and MLR because they also use approximations of second-order derivatives. In large samples the 3 methods are equivalent. When samples are not large it depends on the situation (distributional violations, non-independence) which is best. MLR is often preferrable given its robustness. In some cases, MLR SEs cannot be computed in which case Mplus switches to MLF. I am not aware of studies providing a comprehensive comparison of the 3 methods for the large sets of models that Mplus offers.
Dear Dr. Muthen, I am running the mixture model for murder rates at the county level. My sample size is quite large (N=2700). I use the ML estimator, but only 1-class model has executed normally with ML estimator. Starting with 2-class model, I am getting a warning about a non-positive Fisher information matrix, and the results are presented for the MLF estimator. In your earlier post you said that in large samples, Ml, MLR and MLF are equivalent. Do you think my sample size is large enough for accepting the results based on MLF, or I need to try to modify my model to get a solution based on ML or MLR estimators? Thank you. Arina.
Dear Dr. Muthen, I was running a series of mixture models with varying number of classes for the rates of female aggregated assault at the county level simultaneously. The 1-, 2-, 3- and 5- and 6-classs models terminated normally, but the 4-class model did not. The warning said the model could not converge. Should I disregard the 5- and 6-class solutions before I modify a 4-class model?