[Rd] glm gives incorrect results for zero-weight cases (PR#780)
Wed, 20 Dec 2000 08:18:31 -0800 (PST)
On 20 Dec 2000, Peter Dalgaard BSA wrote:
> email@example.com 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.
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