Hello,

I have read the long thread about the convergence warnings in lmer v. 1.1.6. However, I am still unclear as to how to work with them.
If I understand correctly, I should probably ignore the max | grad | warnings, at least if they are close to the tol value.
However, what about the negative Hessian eigenvalues? Can I ignore those as well? Is the number of negative Hessian eigenvalues relevant at all?
In older versions, models also did not always converge. Does any combination of the new warnings map to the cases in which earlier versions would have told me that they did not achieve convergence?

Thanks
Tobias


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Dr. Tobias Heed
Biological Psychology and Neuropsychology
Faculty of Psychology and Human Movement Science | University of Hamburg
Von-Melle-Park 11, Room 206 | D-20146 Hamburg, Germany
Phone: (49) 40 - 42838 5831 | Fax: (49) 40 - 42838 6591
tobias.heed@uni-hamburg.de
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