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

[rtfiner]

Thank you all for your input. I tried fitting the model with NaNs removed,
and output and evaluation were very similar, so perhaps I am okay?

-Robert

On Mon, Mar 27, 2023 at 9:18 AM Ben Bolker <bbolker using gmail.com> wrote:

>     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
> >> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> >> thierry.onkelinx using inbo.be
> >> Havenlaan 88 bus 73, 1000 Brussel
> >> www.inbo.be
> >>
> >>
> ///////////////////////////////////////////////////////////////////////////////////////////
> >> To call in the statistician after the experiment is done may be no more
> >> than asking him to perform a post-mortem examination: he may be able to
> say
> >> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> >> The plural of anecdote is not data. ~ Roger Brinner
> >> The combination of some data and an aching desire for an answer does not
> >> ensure that a reasonable answer can be extracted from a given body of
> data.
> >> ~ John Tukey
> >>
> ///////////////////////////////////////////////////////////////////////////////////////////
> >>
> >> <https://www.inbo.be>
> >>
> >>
> >> 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
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
> >>> R-sig-mixed-models using r-project.org mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>
> >>      [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> R-sig-mixed-models using r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>


-- 
*-Robert Finer*

	[[alternative HTML version deleted]]