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
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