Mimicking SPSS weighted least squares

-----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.

--
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