Thank you for your replies! Regarding your comment about having logRT in a gamma model with log link, I decided to try it after getting this warning if I only use RT as the dependent variable: (glmer(RT ~ ...) *optimizer (bobyqa) convergence code: 0 (OK)Model failed to converge with max|grad| = 0.00209134 (tol = 0.002, component 1)Model is nearly unidentifiable: very large eigenvalue - Rescale variables?* Do you have a better suggestion for dealing with this that does not require the log transformation and that may allow me to use the influence.me package? Best wishes, Catia
glmer and influence.me - complaining about nAGQ==0
2 messages · Cátia Ferreira De Oliveira, Ben Bolker
These convergence warnings are not necessarily problematic (see ?lme4::convergence, for example). In particular, the overly large max |grad| is only slightly above the threshold (and, these computations can be *less* reliable for very large data sets); the large eigenvalue is similarly just a warning, not necessarily a problem. Do model diagnostics (e.g. with DHARMa) generally look OK? You can try allFit() if you have some patience. The main thing I would do is think carefully/inspect model predictions to see whether you think RT is the more appropriate scale. On 4/26/21 12:24 PM, C?tia Ferreira De Oliveira via R-sig-mixed-models wrote:
Thank you for your replies! Regarding your comment about having logRT in a gamma model with log link, I decided to try it after getting this warning if I only use RT as the dependent variable: (glmer(RT ~ ...) *optimizer (bobyqa) convergence code: 0 (OK)Model failed to converge with max|grad| = 0.00209134 (tol = 0.002, component 1)Model is nearly unidentifiable: very large eigenvalue - Rescale variables?* Do you have a better suggestion for dealing with this that does not require the log transformation and that may allow me to use the influence.me package? Best wishes, Catia [[alternative HTML version deleted]]
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