[R] analysis of data with observation weights

[John Fox]

Dear Peter and Michal,

I was under the impression that the weights argument in lm specifies 
inverse-variance weights, but that the weights argument in glm specifies 
case weights. Inverse-variance weights, which produce a WLS solution, are 
inappropriate for Michal's problem. I checked and now see that the weights 
arguments for both lm and glm are inverse-variance weights, so the 
procedure that I suggested was incorrect.

Sorry,
  John

At 11:48 PM 11/14/2002 +0100, Peter Dalgaard BSA wrote:
>Michal Bojanowski <bojaniss at poczta.onet.pl> writes:
>
> > I'm gettin the same coefficients, but different standard errors. I 
> guess this is
> > what you had in mind.
> >
> > I guess I need a book on WLS... Thank you for the answer anyway.
>
>Thomas Lumley once did a brief but very good writeup on the various
>kinds of weighting. I forget whether it was for one of the open
>mailing lists or in connection with a discussion in R-core.
>
>One thing I remember from it was the need to distinguish between the
>various reasons for weighting. The one used in lm/glm is based on the
>idea that some measurements are more precise than others and therefore
>deserve more weight, so basically the weight is the inverse variance
>of an observation. However, you might want to weight observations
>differently even if their variance is the same, e.g. to obtain a
>method that is stable against differences in population structure,
>even if the model is slightly wrong. (Some rather subtle issues are
>involved here and I'm not sure I'm representing them adequately.)

-----------------------------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox
-----------------------------------------------------

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