From: Peter Dalgaard <P.Dalgaard_at_biostat.ku.dk>

Date: Tue, 11 Mar 2008 16:26:32 +0100

Date: Tue, 11 Mar 2008 16:26:32 +0100

John Fox wrote:

> Dear JRG, Rolf, Ben, and Peter,

*>
**> "Frequency" weights, possibly even non-integer weights, are useful for
**> surveys where observations are sampled with unequal probabilities of
**> selection. The approach in SPSS gives correct point estimates in this
**> situation but incorrect standard errors. The survey package, for example,
**> provides a better solution.
**>
**> Regards,
**> John
**>
*

Actually, I count this as a 3rd variant of weighting. I believe that
SPSS 's standard errors are actually OK for the case where one data line
actually represents a number of identical replicates. To my mind, there
are three (main) kinds of weighting:

(1) Variance weighting (weights proportional to inverse variances) (2) Case weights (weights identical to number of replicates) (3) Inverse probability weights (weights inversely proportional tosampling freq.)

All three give the same point estimates, beta=inv(X'WX)X'WY but the SEs and DF are different (W is the diagonal matrix of weights). I think the formulas are as follows (please correct if I goofed):

in (1) you get sigma^2=Y'(W-WX' inv(X'WX)X'W)Y/(n-rank(X)) ,

VCOV= sigma^2 inv(X'WX), in (3) it is sigma^2=Y'(I-WX inv(X'WX)X') (I- X inv(X'WX)X'W)Y/(n-rank(X)), VCOV=sigma^2 inv(X'WX) X'WWX inv(X'WX)

in both these cases, the DF are n-rank(X) (glossing over complications that arise when the weights become zero) and the VCOV are stable to proportional scaling of W.

in (2) you get sigma^2=Y'(W-WX' inv(X'WX)X'W)Y/(tr(W)-rank(X)),

VCOV= sigma^2 inv(X'WX),

This is deceptively similar to (1), but notice the denominator of
sigma^2. In this case, multiplying the weights by, say, 2 will roughly
halve the VCOV, which is fair enough since it means that you have twice
as much data.

> --------------------------------

*> John Fox, Professor
**> Department of Sociology
**> McMaster University
**> Hamilton, Ontario, Canada L8S 4M4
**> 905-525-9140x23604
**> http://socserv.mcmaster.ca/jfox
**>
**>
**>
**>> -----Original Message-----
**>> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-
**>> project.org] On Behalf Of JRG
**>> Sent: March-10-08 10:27 PM
**>> To: Rolf Turner; r-help_at_r-project.org; Ben Domingue
**>> Cc: r-help_at_r-project.org
**>> Subject: Re: [R] 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 Øster 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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**>
**>
*

-- 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 ~~~~~~~~~~ - (p.dalgaard_at_biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Tue 11 Mar 2008 - 15:31:12 GMT

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