[R-sig-ME] Mixed mutlinomial regression for count data with overdisperion & zero-inflation

[Stéphanie Périquet]

Hi Ben,

Thank you very much for your answer!

I am aware that a lot of zero doesn't mean zero inflation, but if my
understanding is correct the only way to check for ZI would be to compare
one model take doesn't take it into account and another one that does right?

With the model example I gave (count~item+item:season+item:
moon+offset(logduration)+(1+indiv)+(1|obs)) glmmADMB doesn't run but I'm
gonna dig a bit more into this ans come back t you if I can't figure it out.

Best,
Stephanie

On 17 May 2016 at 00:41, Ben Bolker <bbolker at gmail.com> wrote:

> Stéphanie Périquet <stephanie.periquet at ...> writes:
>
> >
> > Dear list members,
> >
> > First sorry for this very long first post …
>
>   That's OK.  I'm only going to answer part of it, because it's long.
> >
> > I am looking for advises to fit a mixed multinomial regression on count
> > data that are overdispersed and zero-inflated. My question is to evaluate
> > the effect of season and moonlight on diet composition of bat-eared
> foxes.
> > My dataset is composed of 14 possible prey item, 20 individual foxes
> > observed, 4 seasons and a moon illumination index ranging from 0 to 1 by
> > 0.1 implements (considered as a continuous variable even if takes only 11
> > values). For each unique combination of individual*season*moon, I thus
> has
> > 14 lines, one for the count of each prey item.
> >
> > From what I gathered, it would be possible to use
> > a standard glmm model of
> > the following form to answer my question (ie a multinomial regression):
> >
> > glmer(count~item+item:season+item:moon+offset(logduration)+
> > (1+indiv)+(1|obs)+
> > (1|id), family=poisson)
>
>   Yes, but I don't know if this will account for the possible dependence
> *among* prey types.
>
> >
> > where count is the number of prey of a given type recorded eaten;
> >
> > item is the prey type;
> >
> > logduration is the log(total time observed for a given combination of
> > individual*season*moon);
> >
> > obs is a unique id for each combination of individual*season*moon,
> > so each
> > obs value regroups 14 lines (one for each prey item) with the same
> > individual*season*moon;
> >
> > id is a unique id for each line to account for overdispersion (as
> > quasi-poisson or negative binomial distributions are not implemented in
> > lme4, Elston et al. 2001).
>
>    Seems about right.
>    There is glmer.nb now, but you might not want it; it tends to
> be slower and more fragile, and you'd still have to deal with
> zero-inflation.
>
> > However, they are a lot of zeros in my data i.e. lot of prey items has
> > never been observed being eaten for mane combinations of
> > individual*season*moon.
>
>   That doesn't *necessarily* mean you need zero-inflation. Large
> numbers of zeros might just reflect low probabilities, not ZI per se.
>
> > Following Ben Bolker wiki (http://glmm.wikidot.com/faq) I summarize
> that I
> > should use of the following methods to answer my question
> >
> >    - ·      glmmADMB, with family=nbinom
> >    - ·      MCMCglmm, with family=zipoisson
> >    - ·      "expectation-maximization (EM) algorithm" in lme4
>
>   Note there's a marginally newer version at
> https://rawgit.com/bbolker/mixedmodels-misc/master/glmmFAQ.html
>
>   Another, newer choice is glmmTMB (available on Github) with
> family="nbinom2"
>
> > Here come the questions:
>
> > 1.  1. Is it correct to assume that I could use the same model
> > structure
> > (count~item+item:season+item:moon+offset(logduration)+(1+indiv)+(1|obs))
> > in glmmADMB or MCMCglmm to answer my question ?
>
>   glmmADMB or glmmTMB, yes: I'm not sure about MCMCglmm
>
> > 2.   I then wouldn't need the (1|id) to correct for overdispersion as
> both
> > methods would already account for it, correct?
>
>    That's right, I think.
>
> > 3.   I am totally new to MCMCglmm, so  ...
>
>   I'm going to let Jarrod Hadfield, or someone else, answer this one.
> >
> > 4.     4.  If I were to use the EM algorithm method,
> > how should the results
> > be interpreted?
>
>   The result is composed of two models -- a 'binary' (structural zero vs
> non-structural zero) and a 'conditional' (count) part.
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-- 
*Stéphanie PERIQUET (PhD) * - Bat-eared Fox Research Project
*Dept of Zoology & Entomology*
*University of the Free State, Qwaqwa Campus*
*Cell: +27 79 570 2683*
ResearchGate profile
<https://www.researchgate.net/profile/Stephanie_Periquet>


Kalahari bat-eared foxes on Twitter <https://twitter.com/kal_batearedfox>

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