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.w...@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 <jorism...@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_squares_generalized.htm >> >> Cheers >> Joris >> >> On Thu, Jun 24, 2010 at 9:16 AM, Stats Wolf <stats.w...@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@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.m...@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.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php ______________________________________________ R-help@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.