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
inflorescences).
Best wishes
Caroli