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

[Thierry Onkelinx]

Daniel sent me the data in private.

A couple of remarks on the dataset.
- the response is non-integer. You'll need to convert it to integer (total
number) and use an appropriate offset term (log(nights)).
- make sure the factor covariate is a factor and not an integer.

Please see if that solves the problem. What happens if you use a nbinom
distribution as Ben suggested?

Personally, I don't like to "standardise" covariates. It makes them much
harder to interpret. I prefer to center to a more meaningful value than the
mean. And rescale it by changing the unit. E.g. Age ranges from 1 to 15
with mean 6.76. I'd use something like AgeC = (Age - 5) / 10. This gives a
similar range as the standardisation of Age. But one unit of AgeC
represents 10 year. And the intercept refers to Age = 5. Making the
parameters estimates easier to interpret IMHO.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op di 20 okt. 2020 om 19:40 schreef Ben Bolker <bbolker using gmail.com>:

>    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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> >
>
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