I've never seen such a data set. Strange. The exposure variable typically
gives us the time frame or area/group in which counts occur; ie the model with
exposure tells us, for each covariate pattern, how many counts are in a given
range of time or area/group. If the time or area/group is 0 (exposure==0),
then there can be no meaningful counts. I cannot see how observations
associated with an exposure of 0 can be kept in the model.
Try modeling a Poisson or negative binomial model with a zero exposure. I'll
bet that the algorithm will not let you proceed. I suspect that the student
does not know the meaning of exposure, and needs to recode the data so that
it reflects a meaningful relationship. Maybe its too early in the morning, and
I'm missing something. But I don't think so.
Joseph Hilbe
====================
A student comes in with a poisson model. The response variable is the
number of seeds of a certain species. There is an exposure variable
which is the total seeds of all species. The problem is that there
are six exposure values of zero. There are three other predictor
variables and 72 total observations. Is there any way of dealing with
this problem other than dropping those six values? Any suggestions?
- --
Phil Ender
Statistical Consulting Group
UCLA Academic Technology Services
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