# [Rd] glm gives incorrect results for zero-weight cases (PR#780)

**Thomas Lumley
**
thomas@biostat.washington.edu

*Wed, 20 Dec 2000 08:18:31 -0800 (PST)*

On 20 Dec 2000, Peter Dalgaard BSA wrote:
>* ripley@stats.ox.ac.uk writes:
*>*
*>* > The reason is obvious: glm.fit only ever updates eta[good], and
*>* > zero-weight values are not `good'. So eta[weights == 0] is stuck at the
*>* > initial values.
*>* >
*>* > There are two possible fixes:
*>* >
*>* > 1) Update eta after the final fit, and then mu. Out of range values
*>* > could then be NA (although it looks like predict.glm does not check).
*>* >
*>* > 2) Update all eta and hence mu values during the iterations. This will
*>* > apply the constraints on eta/mu at zero-weight points too, and so might
*>* > be different.
*>* >
*>* > I am inclined to think that 2) is right, and that adding points with zero
*>* > weight to the fit is not the same as omitting them.
*>* >
*>* > Opinions?
*>*
*>* Just for clarification: This applies only to cases where the
*>* parametrization is non-canonical, e.g. additive models with Poisson
*>* response, right? And essentially the issue is that if you have a model
*>* like lambda = a + b x and you put in a zero-weight observation with x
*>* = 0, then that should effectively constrain a to be positive. That
*>* does make quite good sense, yes.
*>*
*
Not just non-canonical. There are boundary problems with gamma/reciprocal
glms. I would also go for the second solution.
-thomas
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