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

Tue May 17 19:53:45 CEST 2016

Dear Alain,

Thanks for your reply and advices! Will try to do that and wait for your
very timely paper to come out to be sure I did the right thing!

Best,
Stephanie

On 17 May 2016 at 12:08, Highland Statistics Ltd <highstat at highstat.com>
wrote:

>
>
>
> > ----------------------------------------------------------------------
> >
> > Message: 1
> > Date: Tue, 17 May 2016 08:28:42 +0200
> > From: St?phanie P?riquet <stephanie.periquet at gmail.com>
> > To: Ben Bolker <bbolker at gmail.com>
> > Cc: r-sig-mixed-models at r-project.org
> > Subject: Re: [R-sig-ME] Mixed mutlinomial regression for count data
> >       with overdisperion & zero-inflation
> > Message-ID:
> >       <CAMKTVFXZnvS1g-FaNVQ1FQUj5u84S-fd=
> k4u_6x5PwJUZ2R+bQ at mail.gmail.com>
> > Content-Type: text/plain; charset="UTF-8"
> >
> > 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?
>
> Incorrect.
> 1. Calculate the percentage of zeros for your observed data.
> 2. Fit a model....this can be a model without zero inflation stuff.
> 3. Simulate 1000 data sets from your model and for each simulated data
> set assess the percentage of zeros.
> 4. Compare the results in 3 with those in 1.
>
> 5. Even nicer....
> 5a. Plot a simple frequency table for the original data
> (plot(table(Response), type = "h").
> 5b. Calculate a table() for each of your simulated data.
> 5c. Calculate the average frequency table.
> 5d. Compare 5a and 5c.
>
> For a nice example and R code, see:
> A protocol for conducting and presenting results of regression-type
> analyses. Zuur & Ieno
> doi: 10.1111/2041-210X.12577
> Methods in Ecology and Evolution 2016
>
> Comes out in 2 weeks or so.
>
> Kind regards,
>
> Alain
>
>
> > 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:
> >
> >> St?phanie P?riquet <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.
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
> >
>
> --
> Dr. Alain F. Zuur
>
> First author of:
> 1. Beginner's Guide to GAMM with R (2014).
> 2. Beginner's Guide to GLM and GLMM with R (2013).
> 3. Beginner's Guide to GAM with R (2012).
> 4. Zero Inflated Models and GLMM with R (2012).
> 5. A Beginner's Guide to R (2009).
> 6. Mixed effects models and extensions in ecology with R (2009).
> 7. Analysing Ecological Data (2007).
>
> Highland Statistics Ltd.
> 9 St Clair Wynd
> UK - AB41 6DZ Newburgh
> Tel:   0044 1358 788177
> Email: highstat at highstat.com
> URL:   www.highstat.com
>
>
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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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