From: Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer_at_STAT.unimaas.nl>

Date: Thu, 24 Jun 2010 15:20:46 +0200

Date: Thu, 24 Jun 2010 15:20:46 +0200

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)Received on Thu 24 Jun 2010 - 13:24:24 GMT

> 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.

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