Hello,
is there a way to compute multilevel (3-stage) regression using weights
matrix (on different levels, just like in HLM) in R ? The nlme package
doesn't seem to get the job done. Do you have any suggestions ? I _really_
don't want to use HLM to do this.
Best,
Mikołaj Hnatiuk

2012/11/29 Greg Snow <538280@gmail.com>

> The gls function in the nlme package is one approach.
>
> If you know the covariance matrix exactly (it is just numerical with
> nothing that needs to be estimated) then you can also take the Cholesky
> decomposition of the inverse of the covariance matrix (or other square root
> method) and multiply the x matrix and y vector by this root, then do
> ordinary least squares.
>
> Another possibility is generalized estimating equations (gee) which I think
> are implemented in a few different packages.
>
>
> On Wed, Nov 28, 2012 at 4:03 PM, Emese Vágó <vagoemese@hotmail.com> wrote:
>
> >
> >
> >
> > Hi all,
> >
> >
> >
> > I would
> > like to do a weighted linear regression, when the error of the dependent
> > variable
> > is correlated. So I have a weighting (covariance) matrix instead of a
> > vector. As
> > I understood the „weights” argument in the lm function should be a vector
> > and
> > not a matrix. Can anyone suggest me a function (package) which would do
> the
> > job?
> >
> >
> > Thanks a
> > lot!
> >
> >
> >
> > Emese
> >
> >
> >         [[alternative HTML version deleted]]
> >
> >
> > ______________________________________________
> > 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.
> >
> >
>
>
> --
> Gregory (Greg) L. Snow Ph.D.
> 538280@gmail.com
>
>         [[alternative HTML version deleted]]
>
>
> ______________________________________________
> 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.
>
>

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