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 V X posted on Friday, February 26, 2010 - 4:32 pm
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.
 Bengt O. Muthen posted on Saturday, February 27, 2010 - 6:10 am
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.
 Arina Gertseva posted on Tuesday, August 31, 2010 - 10:23 am
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.
 Bengt O. Muthen posted on Tuesday, August 31, 2010 - 4:28 pm
The MLF estimator should be fine at this sample size.
 Arina Gertseva posted on Sunday, April 01, 2012 - 9:31 am
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?
 Linda K. Muthen posted on Monday, April 02, 2012 - 9:14 am
I would try more random starts for the 4-class model, for example STARTS = 400 100; I would not disregard the 5- and 6-class solutions.
 Arina Gertseva posted on Wednesday, April 04, 2012 - 9:43 am
Dear Linda, I tried more random starts for the 4-class model. I went up to 1000, and I also increased the number of miterations. All that did not help.
 Linda K. Muthen posted on Wednesday, April 04, 2012 - 1:43 pm
Try more like 2000 500 or 5000 1000. If you continue to have problems, send the output and your license number to
 Bharath Shashanka Katkam posted on Monday, February 17, 2020 - 4:49 am
Hello Mplus Team,

I have used "Multilevel Structural Equation Modeling" in my research. The "Within-level" sample size is 8 observations per cluster. The "Between-level" sample size is 50 clusters. I have used the "MLF estimator" to run the "Multilevel Structural Equation Modeling". Is my sample size sufficient?
 Bengt O. Muthen posted on Tuesday, February 18, 2020 - 5:22 pm
I would recommend MLR instead.
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