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 [EMAIL PROTECTED] web http://pages.unibas.ch/botschoen/leuzinger ______________________________________________ [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
