Mimicking SPSS weighted least squares
On 11 Mar 2008 at 14:09, Rolf Turner wrote:
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.
Well, if that IS what SPSS does, then it sounds like what Stata calls frequency weights, the general idea being that each "observation" in fact represents some non-negative number (w) of actual observations that have identical values. Not much more than a glorified version of a frequency distribution table. I don't see anything fundamentally wrong with frequency weights, given an appropriate situation. ---JRG John R. Gleason
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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