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

[Highland Statistics Ltd]

> ----------------------------------------------------------------------
>
> 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).

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