[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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