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

[Simone Santoro]

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.
>
> 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


	[[alternative HTML version deleted]]