On Mon, 10 Dec 2012, Jeremy Goss wrote:
> Dear all,
> I am modeling the incidence of recreational anglers along a stretch of
> coastline, and with a vary large proportion of zeros (>80%) have chosen
to
> use a zero inflated negative binomial (ZINB) distribution. I am using the
> same variables for both parts of the model, can anyone help me with R code
> to compute overall marginal effects of each variable?
>
> My model is specified as follows:
>
> ZINB <- zeroinfl(Tot.Anglers ~ Location + Season + Daytype + Holiday.not
+
> CPUE + ShoreType + Access + Source.pop + WindSpeed + offset(beat_length),
> dist="negbin", data=anglers)
We haven't implemented any marginal effects for hurdle/zeroinfl because I
rarely find these useful in practice. Also, you probably would need
several marginal effects for the same variable because you might want to
describe the effect on the zero-inflation, on the count component, and on
the mixture of both.
But with the building blocks provided by hurdle/zeroinfl you can compute
many of the quantities that are potentially of interest "by hand". For
hurdle models, there is some discussion of this in the following posting:
https://stat.ethz.ch/pipermail/r-help/2012-January/300949.html
Best,
Z
>
> Many thanks,
> Jeremy
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>