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

[Prof Brian Ripley]

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.

Constraints can be added by the user, of course, but in the standard cases
(canonical links) they never bite.  Poisson with linear link is one case
where they might.  This checking is something that R has but S and GLIM
(AFAIR) do not.

-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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