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

[Thomas Lumley]

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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