It would appear that the SPSS procedure would then give exactly the same
point estimates of the parameters, and change the inference structure by
changing the ``denominator degrees of freedom'' from n-p to sum(w) - p.
This seems to me to make little sense ... But then, it ***is***
SPSS. :-)
cheers,
Rolf
On 11/03/2008, at 11:35 AM, Peter Dalgaard wrote:
> Rolf Turner wrote:
>> On 11/03/2008, at 4:04 AM, Ben Domingue wrote:
>>
>>
>>> Howdy,
>>> In SPSS, there are 2 ways to weight a least squares regression:
>>> 1. You can do it from the regression menu.
>>> 2. You can set a global weight switch from the data menu.
>>> These two options have no, in my experience, been equivalent.
>>> Now, when I run lm in R with the weights= switch set accordingly, I
>>> get the same set of results you would see with option #1 in SPSS.
>>> Does anybody know how to duplicate option #2 from SPSS in R?
>>>
>>
>> I think it's up to you to find out what ``option #2 from SPSS''
>> actually
>> *does*. If you know that, then you can (with a modicum of effort)
>> duplicate that option in R. The help file for lm() tells you that
>> R uses the weights by minimizing sum(w*e^2) where w = weights and
>> e = ``errors'' or residuals.
>>
>>
>>
> I believe case weighting in SPSS effectively replicates the
> relevant row (not sure if anything sensible comes out if weights
> are non-integer). So
>
> lm(...., data=mydata[rep(1:nrow(mydata),w),])
>
> or thereabouts should do it. Might not be too efficient though.
>
> --
> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*) -- University of Copenhagen Denmark Ph: (+45)
> 35327918
> ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45)
> 35327907
>
>
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