dear R users

i try to fit a gls model to a rather large dataset with an AR(1) error
structure:

attach(sf.a1filt)
m1.a.gls <- gls(fluxt~co2+light+vpd+wind,
 correlation = corAR1(0.8))
summary(m1.a.gls)
detach(sf.a1filt)

there are approx. 5000 observations, and the computation seems to take several
hours, i actually killed the process because i became too impatient. is there
any way to be more efficient with R? (because really the model will be more
complex, i.e. more predictors and higher autoregressive order).

os: linux suse, R version: latest, machine: ibm thinkpad 42T, 1GHz RAM


------------------------------------------------
Sebastian Leuzinger
Institute of Botany, University of Basel
Schönbeinstr. 6 CH-4056 Basel
ph   0041 (0) 61 2673511
fax  0041 (0) 61 2673504
email Sebastian.Leuzinger@(protected)
web  http://pages.unibas.ch/botschoen/leuzinger

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