mixed mutlinomial regression for count data with, overdisperion & zero-inflation
On 18/05/2016 08:26, St?phanie P?riquet wrote:
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?
Stephanie, Make a histogram of the 1000 values of the percentages of zeros....and present the 69.3% as a big blue/red dot. If the dot for your observed data is in the tails you have a problem. I don't see the point of a test in your case. Such a simulation is close to bootstrapping...so I guess you can come up with a test somehow. If you do this type of analysis in a Bayesian framework it is often (and confusingly) called a Bayesian p-value (counting how often the simulated value is larger than your observed one). I would just go for the histogram...seems you are lucky. Alain
Best,
Stephanie
On 17 May 2016 at 20:21, Highland Statistics Ltd
<highstat at highstat.com <mailto:highstat at highstat.com>> wrote:
On 17/05/2016 18:53, St?phanie P?riquet 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 <mailto: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
<mailto:stephanie.periquet at gmail.com>>
> To: Ben Bolker <bbolker at gmail.com <mailto:bbolker at gmail.com>>
> Cc: r-sig-mixed-models at r-project.org
<mailto: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
<mailto: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
<mailto:bbolker at gmail.com>> wrote:
>
>> St?phanie P?riquet <stephanie.periquet at ...>
<mailto: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
>>
>>
>> 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
<mailto:R-sig-mixed-models at r-project.org> mailing list
>
>
>
--
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 <mailto:highstat at highstat.com>
URL: www.highstat.com <http://www.highstat.com>
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--
*St?phanie 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 <mailto:highstat at highstat.com>
URL:www.highstat.com <http://www.highstat.com>
--
*St?phanie 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 [[alternative HTML version deleted]]