I think it is more clear-cut than so, at least if the Poisson situation is
something to go by.
There, you can do either of these and get equivalent results
> fit.lung <- glm(cases ~ age + city, offset=log(pop),
+ family=poisson, data=lungcancer)
> fit.lung2 <- glm(cases/pop ~
[Please keep r-help in the cc: list]
I don't quite know how to interpret the difference between specifying
effort as an offset vs. as weights; I would have to spend more time
thinking about it/working through it than I have available at the moment.
I don't know that specifying effort a
Using an offset of log(Effort) as in your second model is the more
standard way to approach this problem; it corresponds to assuming that
catch is strictly proportional to effort. Adding log(Effort) as a
covariate (as illustrated below) tests whether a power-law model (catch
propto (Effort)^(
Colleagues,
I have a dataset that includes five variables.
- Catch: the catch number counted in some species (ind.)
- Effort: fishing effort (the number of fishing vessels)
- xx1, xx2, xx3: some environmental factors
As an overdispersion test on the “Catch” variable, I modeled with negative
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