From: Jens Hainmueller <jhainm_at_fas.harvard.edu>

Date: Sat 05 Aug 2006 - 03:45:28 EST

R-help@stat.math.ethz.ch 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 Sat Aug 05 03:48:34 2006

Date: Sat 05 Aug 2006 - 03:45:28 EST

Thanks Duncan Murdoch,

> > Why do commonly used estimator functions (such as lm(),

*> > glm(), etc.)
**> > not allow negative case weights?
*

> Residual sums of squares (or deviances) could be negative

*> with negative case weights. This doesn't seem like a good
**> thing: would you really want the fit to be far from those points?
*

Yes, this is actually what I want for this particular estimator. But I can see now why this generally doesn't seem like a a good idea.

Best,

Jens

*> -----Ursprüngliche Nachricht-----
*

> Von: Duncan Murdoch [mailto:murdoch@stats.uwo.ca]

*> Gesendet: Friday, August 04, 2006 7:36 PM
**> An: Jens Hainmueller
**> Cc: r-help@stat.math.ethz.ch
**> Betreff: Re: [R] why does lm() not allow for negative weights?
**>
**> On 8/4/2006 1:26 PM, Jens Hainmueller wrote:
**> > Dear List,
**> >
*

*>
*

*>
*

> > I suspect that there is a good reason for this.

*> > Yet, I can see reasonable cases when one wants to use
**> negative case weights.
**> >
**> > Take lm() for example:
**> >
**> > ###
**> >
**> > n <- 20
**> > Y <- rnorm(n)
**> > X <- cbind(rep(1,n),runif(n),rnorm(n)) Weights <- rnorm(n)
**> # Includes
**> > Pos and Neg Weights Weights
**> >
**> > # Now do Weighted LS and get beta coeffs:
**> > b <- solve(t(X)%*%diag(Weights)%*%X) %*% t(X) %*% diag(Weights)%*%Y
**>
**> That formula does not necessarily give least squares
**> estimates in the case where weights might be negative. For
**> example, with a single observation y, a single parameter mu,
**> design matrix X = 1, and weight -1, that formula becomes
**>
**> b <- y,
**>
**> but that is the worst possible estimator in a least squares
**> sense. The residual sum of squares can be made arbitrarily
**> large and negative by setting b to a large value.
**>
**> Duncan Murdoch
**>
**>
**> > b
**> >
**> > # This seems like a valid model, but when I try lm(Y ~
**> > X[,2:3],weights=Weights)
**> >
**> > # I get: "missing or negative weights not allowed"
**> >
**> > ###
**> >
**> > What is the rationale for not allowing negative weights? I
**> ask this,
**> > because I am currently trying to implement a (two stage) estimator
**> > into R that involves negative case weights. Weights are
**> generated in
**> > the first stage, so it would be nice if I could use canned
**> functions
**> > such as
**> > lm(,weights=Weights) in the second stage.
**> >
**> > Thank you for your help.
**> >
**> > Best,
**> > Jens
**> >
**> > ______________________________________________
**> > R-help@stat.math.ethz.ch 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.
*

>

R-help@stat.math.ethz.ch 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 Sat Aug 05 03:48:34 2006

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.1.8, at Sat 05 Aug 2006 - 06:18:11 EST.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*