[R-sig-ME] Convergence in glmmTMB but not glmer

Ben Bolker bbo|ker @end|ng |rom gm@||@com
Tue Oct 20 19:32:50 CEST 2020


   As Thierry says, the data would allow us to give a more detailed 
answer.  However:

   * the overall goodness-of-fit is very similar (differences of ~0.001 
or less on the deviance scale)

   * the random-effects std deve estimate is similar (2% difference)
   * the parameter estimates are quite similar
   * the standard errors of the coefficients look reasonable for glmmTMB 
and bogus for lme4 (in any case, if there's a disagreement I would be 
more suspicious of the platform that gave convergence warnings)

   There's also strong evidence of dispersion (deviance/resid df > 6); 
you should definitely do something to account for that (check for 
nonlinearity in residuals, switch to negative binomial, add an 
observation-level random effect ...)

    You might try the usual set of remedies for convergence problems 
(see ?troubleshooting, ?convergence in lme4), e.g. ?allFit.  Or try 
re-running the lme4 model with starting values set to the glmmTMB 
estimates.

   Overall, though, I would trust the glmmTMB results.

On 10/20/20 12:56 PM, Daniel Wright wrote:
> Hello,
> 
> I'm having convergence issues when using glmer in lme4, but not glmmTMB.
> I'm running a series of generalized linear mixed effect models with poisson
> distribution for ecological count data. I've included a random effect of
> site (n = 26) in each model. All non-factor covariates are standardized.
> 
> The coefficient estimates of models run in glmer and glmmTMB are very
> similar, but models run in glmer are having convergence issues. Any advice
> would be appreciated, as I'm not sure if I can rely on my results from
> glmmTMB.
> 
> Attached are example of outputs from glmmTMB vs glmer:
> 
> 
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