glmmTMB: Including variable in as fixed effect and in dispersion model (Ben Bolker): R-sig-mixed-models Digest, Vol 174, Issue 21

Thank you for the feedback. I have attempted NB1 without any luck (model did not converge). Adding "time" to the dispersion model did not seem to influence the resulting simulation either. I have been trying several different adjustments to the models without success. The more general issue that motivated my question is covered in depth at https://stats.stackexchange.com/questions/523171/model-misfit-with-dharma-what-needs-can-be-done

Kind Regards,
Andre

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Emne: R-sig-mixed-models Digest, Vol 174, Issue 21

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Today's Topics:

   1. glmmTMB: Including variable in as fixed effect and in
      dispersion model (Andre Syvertsen)
   2. Re:  glmmTMB: Including variable in as fixed effect and in
      dispersion model (Ben Bolker)

----------------------------------------------------------------------

Message: 1
Date: Fri, 25 Jun 2021 10:32:49 +0000
From: Andre Syvertsen <Andre.Syvertsen at uib.no>
To: "r-sig-mixed-models at r-project.org"
        <r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] glmmTMB: Including variable in as fixed effect and
        in dispersion model
Message-ID:
        <AM0PR0102MB31726D5E121BCF3BE4F6FF5A9E069 at AM0PR0102MB3172.eurprd01.prod.exchangelabs.com>

Content-Type: text/plain; charset="utf-8"

Hi guys,

Is it possible/meaningful to include a variable both as a fixed effect and in the dispersion model? For example, I have run the following model:

m4DaysPlayed <- glmmTMB(daysPlayed ~ 1 + time + ageCategory * gender + (time | id), disp = ~time, dfLong, family = truncated_nbinom2)

Rationale: I want to study the effect of time on the outcome variable, but I have also found evidence for heteorskedasticity when simulating through DHARMa. I suspect that the time variable influences this, the sample size decreases/variation increases as time goes on.

Kind regards,
Andr?

------------------------------

Message: 2
Date: Fri, 25 Jun 2021 20:35:52 -0400
From: Ben Bolker <bbolker at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME]  glmmTMB: Including variable in as fixed
        effect and in dispersion model
Message-ID: <8213affb-ee0d-7839-325e-cdd69630d454 at gmail.com>
Content-Type: text/plain; charset="utf-8"; Format="flowed"

   I don't see why not, although to some extent the negative binomial
error structure should account for the phenomenon you're seeing: since
the variance of the nbinom(2) is var = mu*(1+mu/k), the coefficient of
variation is sqrt(var/mu^2) = sqrt(1/mu + k).  I'm not sure what you
mean by "sample size decreasing", but for a large mean (mu), the CV
should be approximately constant (~ sqrt(k)), while for small mean the
CV should be increasing with decreasing mu (~ sqrt(1/mu)).

   It might be worth trying truncated_nbinom1 as well?