I have data on the proportion of clutches experiencing different fates
(e.g., 4 different sources of mortality) for 5 months . I need to test 1)
if the overall proportion of these different fates is different over the
entire study and 2) to see if there are monthly differences within (and
among)
I am attempting to run a glm with a binomial model to analyze proportion
data.
I have been following Crawley's book closely and am wondering if there is
an accepted standard for how much is too much overdispersion? (e.g. change
in AIC has an accepted standard of 2).
In the example, he fits sever
THANKS so very much for your help (previous and future!). I have a two
follow-up questions.
1) You say that dispersion = 1 by definition dispersion changes from 1
to 13.5 when I go from binomial to quasibinomialdoes this suggest that
I should use the binomial? i.e., is the dispersion fact
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