[R-sig-ME] Use of offset variable when analysing rates in lme4

Caroli caroli at sun.ac.za
Thu Apr 10 08:58:35 CEST 2014

> Dear Caroli and Thierry,
> I don't have any objections to what Thierry said regarding the offset in a 
Poisson model, but I am wondering
> if a discrete binomial model might be more appropriate than a Poisson. You 
said you counted the number of
> visits per inflorescence. Does that mean you recorded whether or not there 
was a pollinator present in a
> flower before moving on to the next inflorescence, and that there could be 
at most one visitor per flower?
> In other words if you counted say 200 inflorescences, you could record 
between 0-200 visits? If so, is that
> not a case of a binomial distribution? 
> Stroup (2014) doi:10.2134/agronj2013.0342 , which was recently discussed 
on the list, has worked
> examples of a discrete binomial model if you are interested.
> I'd welcome any thoughts on this since I'm about to do a similar analysis. 
Often in these types of data, the
> number of visits can be quite low (e.g. 0-20) even when you observe 
several hundred inflorescences. Does
> anyone have input on choosing between Poisson vs discrete binomial in 
similar cases?
> Regards,
> Jens Astrom 

Dear Jens,

Thanks for your response!

I see what you mean with using a binomial model which would be more 
appropriate in the case you described. That would almost be the same as 
asking what proportion of inflorescences were visited, right? 
However, in my case I observed say 200 inflorescences and observed 
pollinators visiting the patch over a 5 min period, so it can be regarded as 
a continuous count variable (each inflorescence can be visited more than 
once). I.e., I did not "follow" individual inflorescences.
Although, as you note, number of visits were quite low in my case and in 
only a few cases were visitation rates (visits/inflorescence) larger than 
one (and these were for treatments in my experiment with only four 

Best wishes

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