R help - Nov 2012 - Weight matrix in linear regression

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

 		 	   		  
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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

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