[R-sig-ME] mixed mutlinomial regression for count data with, overdisperion & zero-inflation
Stéphanie Périquet
stephanie.periquet at gmail.com
Wed May 18 09:26:56 CEST 2016
Yeah thanks Alain, I'm definitely planning to buy this book!
So I looked at the zeros in my data abased on you advice and I did the
following:
mod<-glmer(count~item+item:season+item:moon+item:season:moon+(1|indiv/obs)+(1|id),family=poisson,nAGQ=0,data=diet3)
z<-simulate(mod,nsim=1000)
For the original data I have 69.3% of zeros while the average over the 1000
simulations is 63.5%.Is there a way to statistically compare these 2
values? Or could you say that these 2 figures are not very different and
then zero inflation models might not be necessary?
Best,
Stephanie
On 17 May 2016 at 20:21, Highland Statistics Ltd <highstat at highstat.com>
wrote:
>
>
> On 17/05/2016 18:53, Stéphanie Périquet wrote:
>
> 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!
>
>
> Stephanie,
>
> Although it does not cover multinomial models directly, this one may be of
> use as well:
>
> Beginner's Guide to Zero-Inflated Models with R (2016). Zuur AF and Ieno EN
> http://highstat.com/BGZIM.htm
>
> Sorry for the self-references.
>
> Kind regards,
>
> Alain
>
>
> 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%2BbQ at mail.gmail.com>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>
>> bbolker at gmail.com> wrote:
>> >
>> >> St?phanie P?riquet <stephanie.periquet at ...> <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>
>> 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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>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
>
> --
> *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>
>
>
> --
> 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
>
>
--
*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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