Re: [R] weights vs. offset (negative binomial regression)

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 ~

Re: [R] weights vs. offset (negative binomial regression)

[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

Re: [R] weights vs. offset (negative binomial regression)

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

[R] weights vs. offset (negative binomial regression)

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