Thanks for the suggestion!

It seems to me predict() is the opposite of what I'm looking for.  
With an extended linear model

Y = X*b + e, e ~ N(0, R s^2), R s^2 is the  covariance matrix of the  
residual term e

both X and the structure of R remain the same across all the loops in  
my case, and Y is different and so are the estimates of b and R s^2  
from one iteration to another.

Gang


On Jul 16, 2008, at 1:29 PM, Steven McKinney wrote:

> Hi Gang,
>
> Have you tried the predict() function?
> You should be able to hand off new data
> to your saved fitted model object.
> See
> ?predict.gls
> for an example.
>
> HTH
>
> Steve McKinney
>
> -----Original Message-----
> From: r-sig-mixed-models-bounces@r-project.org on behalf of Gang Chen
> Sent: Wed 7/16/2008 8:21 AM
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Generalized least squares program
>
> I'm using function gls in nlme to run some extended linear model with
> generalized least squares many many times in a loop with the same
> design matrix X and same residual covariance structure but different
> values for the response variable. A lot of time is wasted in
> reformulating the same model structures and overhead matrix
> calculations. Such a waste makes the runtime range from one day to
> one month. Are there some slots available in gls that would allow me
> to avoid the unnecessary waste? If not, is there a counterpart of gls
> for extended linear model with generalized least squares available in
> lme4 that would give such slots?
>
> Thanks,
> Gang
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>


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