Mimicking SPSS weighted least squares
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 ?ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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