[R-sig-ME] Question about proportion data in binomial glmm

[Ben Bolker]

    The only further issue here is that the number of observations for 
the model will still be computed as including these null values. This 
should only matter if you're doing something like computing 
finite-size-corrected AICs (and to paraphrase _Numerical Recipes_, if 
this level of difference matters to you then you're on shaky ground 
anyway ...)

   The source code for the dbinom implementation in TMB:

https://kaskr.github.io/adcomp/distributions__R_8hpp_source.html

   illustrates that values with N=0, k = 0 will have no effect on the 
log-likelihood (while TMB mirrors R's behaviour most of the time, it's 
not 100% safe to assume that edge cases will work exactly the same in R 
and TMB)

On 2023-03-24 6:36 a.m., Mollie Brooks wrote:
> They have zero contribution to the log-likelihood, so they shouldn’t affect the model.
>
>> dbinom(0, 0, 0.1, log=TRUE)
> [1] 0
>
> I can’t say if they would affect any model evaluation functionality, but they shouldn't.
>
> Best,
> Mollie
>
>> On 24 Mar 2023, at 09.12, Thierry Onkelinx via R-sig-mixed-models <r-sig-mixed-models using r-project.org> wrote:
>>
>> Dear Robert,
>>
>> IMHO you should remove the cbind(0, 0) before fitting the model. There is
>> no reason to keep them in the dataset.
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
>> FOREST
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>> thierry.onkelinx using inbo.be
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>>
>> Op vr 24 mrt 2023 om 02:39 schreef rtfiner <rtfiner using gmail.com>:
>>
>>> I have a question about how glmmtmb handles proportion data for the
>>> purposes of a binomial glmm.
>>>
>>> I combined my success and failure count data into a matrix using cbind(),
>>> and used that as my response in my binomial glmm using glmmtmb.
>>>
>>> However, despite there being a few instances of zero counts in both columns
>>> and therefore an undefined proportion, the model doesn't seem to drop these
>>> rows from my data set.
>>>
>>> I don't get any errors or warnings when running the model, but I worry my
>>> results might be biased because of this.
>>>
>>> My question is: Is glmmtmb doing something like adding a tiny amount to
>>> each value of my response in order to avoid dealing with undefined
>>> proportion data?
>>>
>>> Thank you for your help,
>>>
>>> Robert
>>>
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>>>
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