Convergence in glmmTMB but not glmer

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On 10/20/20 2:02 PM, Thierry Onkelinx wrote:

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.

   If the response is non-integer, that makes my comment about
overdispersion not necessarily relevant (check again after re-fitting).

   It's often a good idea when using an offset such as log(nights) to
*also* (alternatively) try using log(nights) as a predictor: using
log(nights) assumes that the number of counts is strictly proportional
to the number of nights measured (log(counts) ~ log(nights) + <stuff> ->
counts ~ nights*exp(stuff) , whereas using log(counts) allows for some
saturation effects (log(counts) ~ alpha*log(nights) + <stuff> -> counts
~ nights^alpha*exp(stuff))

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.

   Yes, although 'strict' standardization (scaling by predictor SD or
2*predictor SD) allows direct interpretation of the parameters as a kind
of effect size (Schielzeth 2010), whereas 'human-friendly'
standardization trades interpretability for the comparison of magnitudes
being only an approximation.

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 at inbo.be <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://www.inbo.be>

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