unequal sampling effort - binomial generalized linear model - offset

How to take into account heterogeneity in sampling effort in a binomial glm (presence absence) ? Edit

I want to link species presence with environmental variables. But, species data come from camera trapping and camera traps didn't work the same total period (some cameras worked during 50 days, others during 200 days..).

How can I introduce this heterogenity in my glm ?
I know that an offset function is available for poisson glm (using offset=log(variable name)) but I was wondering if the formulation log(cam days) works as well for a binomial glm (because the link is logit)..

Thank you very much for your help !


Less is more. Stay pure. Stay poor.
Not sure of the best approach. Could truncated both to the same length of exposure. Perhaps impute or similate the missing info. Or do some type of weighting or bias correction.

Not familiar with offset option. What would that do?
Offset allows to take into account unequal sampling effort by giving a coefficient 1 to a variable log(effort). There is an example with a Poisson glm, but I wonder if the methodology applies to a Binomial glm just as well (presence/absence). The example and code are on page 189 of his book titled, "Introduction to WinBugs for ecologist".
A free pdf copy of his book can be found at http://bayanbox.ir/view/59588541639...n-ANOVA-mixed-models-and-related-analyses.pdf

Just take a look at the middle of page 189 and you will see the example.

Thanks for your attention !
The offset option allows to take into account different sampling effort by adding a variables log(effort) with a coefficient 1 in a Poisson model. But I don't know if that could be right to use a log(effort) in a binomial model... There is an example at the page 189 of the "Introduction to WinBugs for ecologist"..

Thank you for your help