[R] Question on WLS (gls vs lm)

[Stats Wolf]

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_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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