I am currently trying to get a "lme" analyses running to correct for
the
non-independence of residuals (using e.g. corAR1, corARMA) for a
larger data
set (>10000 obs) for an independent (lgeodisE) and dependent
variable
(gendis). Previous attempts using SAS failed. In addition we were
told by
SAS that our data set was too large to be handled by this procedure
anyway
(!!).
SAS script
proc mixed data=raw method=reml maxiter=1000;
model gendis=lgeodisE / solution;
repeated /subject=intercept type=arma(1,1);
So I turned to R. Being a complete R newbie I didn't arrive
computing
exactly the same model in R on a reduced data set so far.
R command line (using "dummy" as a dummy group variable)
model.ARMA<-lme(gendis~lgeodisE,correlation=corARMA(p=1,q=1),random=~1|dummy).
Furthermore, memory allocation problems occurred again on my 1GB RAM
desktop
during some trials with larger data sets.
Can anybody help?
Cheers,
Peter
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