[R-sig-ME] GLMM for underdispersed count data: Conway-Maxwell-Poisson and Ordinal

Simone Santoro santoro at ebd.csic.es
Tue Jan 31 15:25:08 CET 2017 

Thank you very much Mollie. I am going to take again the analyses in a 
couple of weeks and it will be useful for me knowing that.
Bye,

Simone

El 31/01/2017 a las 15:22, Mollie Brooks escribió:
> I wanted to follow up on this discussion and let you know that glmmTMB 
> recently changed the way that it does Conway-Maxwell-Poisson 
> regression. So if you are using glmmTMB for this, you should probably 
> install the new version from source. It now is parameterized to do 
> regression on the mean rather than the approximate mean, which should 
> improve accuracy and interpretability. It’s part of the master branch 
> now, so the standard installation from source should work as described 
> here https://github.com/glmmTMB/glmmTMB
>
> cheers,
> Mollie
>
> ———————————
> Mollie E. Brooks, Ph.D.
> Postdoctoral Researcher
> National Institute of Aquatic Resources
> Technical University of Denmark
>
>> On 15Dec 2016, at 1:52, Ben Bolker <bbolker at gmail.com 
>> <mailto:bbolker at gmail.com>> wrote:
>>
>>
>>
>>
>> On 16-12-14 11:05 AM, Simone Santoro wrote:
>>> Thank you all so much for it. Really a very useful discussion, hope it
>>> may be so to others too.
>>>
>>> El 14/12/2016 a las 16:06, Mollie Brooks escribió:
>>>> Hi Simone,
>>>>
>>>> For the glmmTMB model with the Conway-Maxwell Poisson distribution,
>>>> the left side of the equation should technically by fledges rather
>>>> than as.factor(fledges). However, it looks like glmmTMB doesn’t
>>>> evaluate the as.factor() command and fits the model with fledges as
>>>> the response anyway.
>>
>>  I'd be careful with this conclusion.  I think these are *not* the same
>> model.  For the fake data set (where all the true effects are zero) the
>> results aren't that different, but what happens when you convert an
>> integer value to a factor is that the unique values get converted to
>> codes 1, 2, ...  This would potentially be disastrous.
>>
>>  I got the clmm model to run with Rune's development version; it's a
>> little hard to see whether the results are comparable or not since it's
>> fitting a qualitatively different model ...
>>
>>
>>>>
>>>> If you end up needing zero-inflation also, it can be specified using
>>>> the ziformula command. See vignette("glmmTMB") or here
>>>> https://github.com/glmmTMB/glmmTMB/blob/master/misc/salamanders.pdf
>>>> for an example.
>>>>
>>>> cheers,
>>>> Mollie
>>>>
>>>> ———————————
>>>> Mollie E. Brooks, Ph.D.
>>>> Postdoctoral Researcher
>>>> National Institute of Aquatic Resources
>>>> Technical University of Denmark
>>>>
>>>>> On 9Dec 2016, at 19:40, Simone Santoro <santoro at ebd.csic.es
>>>>> <mailto:santoro at ebd.csic.es>> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> Thank you all very much your hints. They have been really really
>>>>> helpful for me. Below you may find a reproducible code to see how
>>>>> three approaches fit a simulated data set (clmm::ordinal,
>>>>> glmmTMB::glmmTMB, fitme:spaMM). Results seem to me qualitatively
>>>>> similar but with clmm:ordinal I cannot use the three crossed random
>>>>> effects because I get an error like this:
>>>>> Error: no. random effects (=135) >= no. observations (=100)
>>>>>
>>>>> set.seed(1234)
>>>>> library(ordinal)
>>>>> library(glmmTMB)
>>>>> library(spaMM)
>>>>> dati<- data.frame(fledges= rpois(100,10), habitatF=
>>>>> as.factor(rbinom(100,1,0.5)), areaPatchFath= rnorm(100), poligF01=
>>>>> as.factor(rbinom(100,1,0.5)),StdLayingDate= rnorm(100), ageFath1=
>>>>> rpois(100,3), ageMoth1= rpois(100,3), year=
>>>>> as.factor(rpois(100,200)), ringMoth= as.factor(rpois(100,200)),
>>>>> ringFath= as.factor(rpois(100,200)))
>>>>> str(dati)
>>>>>
>>>>> system.time(Fitclm<- clmm(as.factor(fledges) ~
>>>>> habitatF*(areaPatchFath+poligF01+StdLayingDate+ageFath1+ageMoth1)+(1|year)+(1|ringMoth)+(1|ringFath),data=dati,Hess=T))
>>>>> # this way it works...
>>>>> system.time(Fitclm1<- clmm(as.factor(fledges) ~
>>>>> habitatF*(areaPatchFath+poligF01+StdLayingDate+ageFath1+ageMoth1)+(1|year)+(1|ringFath),data=dati,Hess=T))
>>>>> summary(Fitclm1)
>>>>>
>>>>> system.time(FitglmmTMB<- glmmTMB(as.factor(fledges) ~
>>>>> habitatF*(areaPatchFath+poligF01+StdLayingDate+ageFath1+ageMoth1)+(1|year)+(1|ringMoth)+(1|ringFath),data=dati,family=
>>>>> "compois"))
>>>>> summary(FitglmmTMB)
>>>>>
>>>>> system.time(FitglmmTMB<- glmmTMB(as.factor(fledges) ~
>>>>> habitatF*(areaPatchFath+poligF01+StdLayingDate+ageFath1+ageMoth1)+(1|year)+(1|ringMoth)+(1|ringFath),data=dati,family=
>>>>> "compois"))
>>>>> summary(FitglmmTMB)
>>>>>
>>>>> # This lasts much more (3-4')
>>>>> system.time(Fitfitme<- fitme(fledges ~
>>>>> habitatF*(areaPatchFath+poligF01+StdLayingDate+ageFath1+ageMoth1)+(1|year)+(1|ringFath)+(1|ringMoth),data=dati,COMPoisson(),method
>>>>> = "ML"))
>>>>> summary(Fitfitme)
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> El 08/12/2016 a las 4:32, Ben Bolker escribió:
>>>>>>   One reference that uses ordinal regression in a similar situation
>>>>>> (litter size of Florida panthers) is
>>>>>> http://link.springer.com/article/10.1007/s00442-011-2083-0 ("Does
>>>>>> genetic introgression improve female reproductive performance? A 
>>>>>> test on
>>>>>> the endangered Florida panther")
>>>>>>
>>>>>>  Not sure about the number-of-random-effects error: a reproducible
>>>>>> example would probably be needed (smaller is better!)
>>>>>>
>>>>>>  Ben Bolker
>>>>>>
>>>>>>
>>>>>> On 16-12-06 08:41 AM, Simone Santoro wrote:
>>>>>>> Dear all,
>>>>>>>
>>>>>>> I am trying to find an appropriate GLMM (with temporal and 
>>>>>>> individual
>>>>>>> crossed random effects) to model underdispersed count data (clutch
>>>>>>> size). I have found several possible ways of doing that. A good
>>>>>>> distribution for data like this would seem to be the
>>>>>>> Conway-Maxwell-Poisson but I have not found a way of using it 
>>>>>>> within a
>>>>>>> GLMM in R (I have asked here
>>>>>>> <http://stats.stackexchange.com/questions/249738/how-to-define-the-nu-parameter-of-conway-maxwell-poisson-in-spamm-package> 
>>>>>>>
>>>>>>> and here
>>>>>>> <http://stats.stackexchange.com/questions/249798/conway-maxwell-poisson-with-crossed-random-effects-in-r>).
>>>>>>> I have seen that Ben Bolker suggested (here
>>>>>>> <https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q1/021945.html>and 
>>>>>>>
>>>>>>> here
>>>>>>> <http://stats.stackexchange.com/questions/92156/how-to-handle-underdispersion-in-glmm-binomial-outcome-variable>) 
>>>>>>>
>>>>>>> to use an ordinal model in cases like this(e.g. _ordinal:clmm_). 
>>>>>>> I have
>>>>>>> tried this solution and the results I obtain makes (biological) 
>>>>>>> sense to
>>>>>>> me. However, I wonder why but I cannot put all the three crossed 
>>>>>>> random
>>>>>>> effects I have in the clmm model (_Error: no. random effects 
>>>>>>> (=1254) >=
>>>>>>> no. observations (=854)_) whereas it is not a problem for the glmer
>>>>>>> model (the no. of levels of each single random effect does not 
>>>>>>> exceed 854)*.
>>>>>>> Beyond that, and that's what I would like to ask you, *I cannot 
>>>>>>> find a
>>>>>>> reference to justify I used the ordinal model* to deal with
>>>>>>> underdispersed count data (referee will ask it for sure).
>>>>>>> Best,
>>>>>>>
>>>>>>> Simone
>>>>>>>
>>>>>>> * FMglmer<- glmer(fledges ~ habitatF * (areaPatchFath + poligF01 +
>>>>>>> StdLayingDate + ageFath1 + ageMoth1) + (1|year) + (1|ringMoth) +
>>>>>>> (1|ringFath), data = datiDRS)
>>>>>>>    FMclmm<- glmer(as.factor(fledges)~ habitatF * (areaPatchFath +
>>>>>>> poligF01 + StdLayingDate + ageFath1 + ageMoth1) + (1|year) +
>>>>>>> (1|ringMoth) + (1|ringFath), data = datiDRS)
>>>>>>>
>>>>>>>
>>>>>>> [[alternative HTML version deleted]]
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>>
>>>>>
>>>>> -- 
>>>>> Simone Santoro
>>>>> PhD
>>>>> Department of Ethology and Biodiversity Conservation
>>>>> Doñana Biological Station
>>>>> Calle Américo Vespucio s/n
>>>>> 41092 Seville - Spain
>>>>> Phone no. +34 954 466 700 (ext. 1213)
>>>>> http://www.researchgate.net/profile/Simone_Santoro
>>>>> http://orcid.org/0000-0003-0986-3278
>>>>
>>>
>>> -- 
>>> Simone Santoro
>>> PhD
>>> Department of Ethology and Biodiversity Conservation
>>> Doñana Biological Station
>>> Calle Américo Vespucio s/n
>>> 41092 Seville - Spain
>>> Phone no. +34 954 466 700 (ext. 1213)
>>> http://www.researchgate.net/profile/Simone_Santoro
>>> http://orcid.org/0000-0003-0986-3278
>>>
>

-- 
Simone Santoro
PhD
Dept. Molecular Biology and Biochemical Engineering
University Pablo de Olavide, Sevilla, Spain
ssantoro at upo.es
&
Dept. of Ethology and Biodiversity Conservation
Doñana Biological Station
Calle Américo Vespucio s/n
41092 Seville - Spain
Phone no. +34 954 466 700 (ext. 1213)
http://www.researchgate.net/profile/Simone_Santoro
http://orcid.org/0000-0003-0986-3278


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