From: Joris Meys <jorismeys_at_gmail.com>

Date: Thu, 24 Jun 2010 15:43:21 +0200

Date: Thu, 24 Jun 2010 15:43:21 +0200

Thanks! I was getting confused as wel, Wolf really had a point.

I have to admit that this is all a bit counterintuitive. I would expect those weights to be the inverse of the variances, as GLS uses the inverse of the variance-covariance matrix. I finally found in the help files ?nlme::varWeights the needed information, after you pointed out the problem and after strolling through quite a part of the help files...

Does this mean that the GLS algorithm uses 1/sd as weights (can't imagine that...), or is this one of the things in R that just are like that?

Cheers

Joris

On Thu, Jun 24, 2010 at 3:20 PM, Viechtbauer Wolfgang (STAT)
<Wolfgang.Viechtbauer_at_stat.unimaas.nl> wrote:

> The weights in 'aa' are the inverse standard deviations. But you want to use the inverse variances as the weights:

*>
**> aa <- (attributes(summary(f1)$modelStruct$varStruct)$weights)^2
**>
**> And then the results are essentially identical.
**>
**> Best,
**>
**> --
**> Wolfgang Viechtbauer http://www.wvbauer.com/
**> Department of Methodology and Statistics Tel: +31 (0)43 388-2277
**> School for Public Health and Primary Care Office Location:
**> Maastricht University, P.O. Box 616 Room B2.01 (second floor)
**> 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck)
**>
**>
**> ----Original Message----
**> From: r-help-bounces_at_r-project.org
**> [mailto:r-help-bounces_at_r-project.org] On Behalf Of Stats Wolf Sent:
**> Thursday, June 24, 2010 15:00 To: Joris Meys
**> Cc: r-help_at_r-project.org
**> Subject: Re: [R] Question on WLS (gls vs lm)
**>
**>> Indeed, they should give the same results, and hence I was worried to
**>> see that the results were not that same. Suffice it to look at
**>> standard errors and p-values: they do differ, and the differences are
**>> not really that small.
**>>
**>>
**>> Thanks,
**>> Stats Wolf
**>>
**>>
**>>
**>> On Thu, Jun 24, 2010 at 2:39 PM, Joris Meys <jorismeys_at_gmail.com>
**>> wrote:
**>>> Indeed, WLS is a special case of GLS, where the error covariance
**>>> matrix is a diagonal matrix. OLS is a special case of GLS, where the
**>>> error is considered homoscedastic and all weights are equal to 1. And
**>>> I now realized that the varIdent() indeed makes a diagonal covariance
**>>> matrix, so the results should be the same in fact. Sorry for missing
**>>> that one.
**>>>
**>>> A closer inspection shows that the results don't differ too much. The
**>>> fitting method differs between both functions; lm.wfit uses the QR
**>>> decomposition, whereas gls() uses restricted maximum likelihood. In
**>>> Asymptopia, they should give the same result.
**>>>
**>>> Cheers
**>>> Joris
**>>>
**>>>
**>>> On Thu, Jun 24, 2010 at 12:54 PM, Stats Wolf <stats.wolf_at_gmail.com>
**>>> wrote:
**>>>> Thanks for reply.
**>>>>
**>>>> Yes, they do differ, but does not gls() with the weights argument
**>>>> (correlation being unchanged) make the special version of GLS, as
**>>>> this sentence from the page you provided says: "The method leading
**>>>> to this result is called Generalized Least Squares estimation
**>>>> (GLS), of which WLS is just a special case"?
**>>>>
**>>>> Best,
**>>>> Stats Wolf
**>>>>
**>>>>
**>>>>
**>>>> On Thu, Jun 24, 2010 at 12:49 PM, Joris Meys <jorismeys_at_gmail.com>
**>>>> wrote:
**>>>>> Isn't that exactly what you would expect when using a _generalized_
**>>>>> least squares compared to a normal least squares? GLS is not the
**>>>>> same as WLS.
**>>>>>
**>>>>> http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_least_square
**>>>>> s_generalized.htm
**>>>>>
**>>>>> Cheers
**>>>>> Joris
**>>>>>
**>>>>> On Thu, Jun 24, 2010 at 9:16 AM, Stats Wolf <stats.wolf_at_gmail.com>
**>>>>> wrote:
**>>>>>> Hi all,
**>>>>>>
**>>>>>> I understand that gls() uses generalized least squares, but I
**>>>>>> thought that maybe optimum weights from gls might be used as
**>>>>>> weights in lm (as shown below), but apparently this is not the
**>>>>>> case. See:
**>>>>>>
**>>>>>> library(nlme)
**>>>>>> f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris,
**>>>>>> weights = varIdent(form = ~ 1 | Species)) aa <-
**>>>>>> attributes(summary(f1)$modelStruct$varStruct)$weights
**>>>>>> f2 <- lm(Petal.Width ~ Species / Petal.Length, data = iris,
**>>>>>> weights = aa)
**>>>>>>
**>>>>>> summary(f1)$tTable; summary(f2)
**>>>>>>
**>>>>>> So, the two models with the very same weights do differ (in terms
**>>>>>> of standard errors). Could you please explain why? Are these
**>>>>>> different types of weights?
**>>>>>>
**>>>>>> Many thanks in advance,
**>>>>>> Stats Wolf
**>>>>>>
**>>>>>> ______________________________________________
**>>>>>> 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.
**>>>>>>
**>>>>>
**>>>>>
**>>>>>
**>>>>> --
**>>>>> Joris Meys
**>>>>> Statistical consultant
**>>>>>
**>>>>> Ghent University
**>>>>> Faculty of Bioscience Engineering
**>>>>> Department of Applied mathematics, biometrics and process control
**>>>>>
**>>>>> tel : +32 9 264 59 87
**>>>>> Joris.Meys_at_Ugent.be
**>>>>> -------------------------------
**>>>>> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
**>>>>>
**>>>>
**>>>
**>>>
**>>>
**>>> --
**>>> Joris Meys
**>>> Statistical consultant
**>>>
**>>> Ghent University
**>>> Faculty of Bioscience Engineering
**>>> Department of Applied mathematics, biometrics and process control
**>>>
**>>> tel : +32 9 264 59 87
**>>> Joris.Meys_at_Ugent.be
**>>> -------------------------------
**>>> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
**>>>
**>>
**>> ______________________________________________
**>> 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.
**>
*

-- Joris Meys Statistical consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control tel : +32 9 264 59 87 Joris.Meys_at_Ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php ______________________________________________ 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 Thu 24 Jun 2010 - 13:46:04 GMT

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

Archive generated by hypermail 2.2.0, at Thu 24 Jun 2010 - 14:00:35 GMT.

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