[R-sig-ME] Including an offset in a binomial GLM/GLMM

[Baldwin, Jim -FS]

If the probability of animal presence and the detection probability are both of interest, have you considered the unmarked package?  That package will allow (and is built for) accounting for variability in sampling effort (such as the number of trap nights).

Jim Baldwin
Pacific Southwest Research Station
USDA Forest Service

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Leila Brook
Sent: Monday, August 13, 2012 11:18 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Including an offset in a binomial GLM/GLMM

Dear all,

I apologise if this is an obvious question, but I haven't been able to find reference to it in the literature so far. I was wondering whether it is possible to include an offset variable in a binomial GLM/GLMM, as well as in poisson models?



For example, I surveyed for my study species during a set time period and collected presence-absence data. On some nights the camera traps failed, so the no. trap nights is reduced, which could then influence detection.

Hence I would like to include trap night as an offset.



However, if it is possible to include the trap nights as an offset in a binomial model with logit link function, is it alright to just include it as below:



model<- glm(pres ~ a*b + c, offset=trapnight, family=binomial, data=data)



Or would it need to be transformed as in Poisson models with a log link when included in the formula:

model<- glm(count ~ a*b + c + offset(log(trapnight), family=poisson, data=data)



In this case, would the transformation be "offset(logit(trapnight))" or "offset(invlogit(trapnight))"



Thank you for your help,

Leila

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