I find arima() fits such models very much faster. On Mon, 25 Jul 2005, Sebastian Leuzinger wrote:
> 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). -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
