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.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
>
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--
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
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