[R-sig-ME] incorporating effort as an effect in binomial GLMM

[Thierry Onkelinx]

Dear Anonymous,

Here a few ideas

How did you check for zero-inflation? A lot of zero's does not imply
zero-inflation. E.g. table(rpois(1e6, lambda = 0.01)) has lots of zero's
but no zero-inflation. I'd recommend using a Poisson distribution. Then
check for zero-inflation by comparing the distribution of the number of
zero's from several datasets simulated based on the model with the observed
number of zero's.

The logit-link complicates the interpretation of the fishing effort in the
binomial model. I suggest using a Poisson model with log(length) of the
nets as a fixed effect to the model to correct from fishing effort. Then
you can get predictions in terms of number per unit length the net.

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

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Op vr 17 jan. 2020 om 22:01 schreef Ben Bolker <bbolker using gmail.com>:

>
>   [this is not my question; it's posted on behalf of someone who wants
> to remain anonymous ...]
>
> I am testing the effect of a treatment to reduce bycatch in fishing
> nets. Note the the design uses paired nets (control vs experiment)
> soaked simultaneously but of different length (limited budget did no
> allow to have an experimental net as long as control net).
>
> The dependent variable are counts (no. individuals entangled), and I
> have fishing effort and treatment (control vs experiment) as independent
> variables. Since bycatch events were rare , the dataset is zero inflated
> and positive catches are usually of 1 individual, therefore we switched
> to a binomial model to test the probability of catching an individual
> where if the catch is zero then probability =0, but if the catch is >0
> then probability is a 1.
> We used this model to predict bycatch probability in control and
> experimental nets by setting fishing effort = 1.
>
> There is an issue being raised, that Fishing effort being significantly
> higher for control than experimental nets, the binomial model can yield
> biased estimates of treatment and overestimate treatment efficiency.
>
> I thought that including Effort as a fixed effect in the model would
> mean that the model takes into account the difference in effort when
> predicting the bycatch probability. Is that true?
> However, I am not entirely sure HOW the glmer function does it and I
> would like to know your opinion about the issue being raised."
>
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