The problem is that AIC is only defined for ML fitting, and gls defaults to REML.
I have always maintained that it is a bug that nlme's logLik function returns a log restricted likelihood for the default fits, and that this is converted to a mis-named AIC. If you use m1 <- gls(height ~ age, data = Loblolly, method="ML") you wil get agreement. On Tue, 11 Mar 2008, sbegueria wrote: > > Hello, > > I am comparing models made with nlme functions and non-nlme functions, based > on Akaike's AIC. The AIC values I get for exactly the same model formulation > --for example a linear model with no random effects fit with gls and lm, > respectively-- do not fit, although the values of the four model parameters > are exactly the same. For example: > > m1 <- gls(height ~ age, data = Loblolly) > m2 <- lm(height ~ age, data = Loblolly) > > m1$coefficients > (Intercept) age > -1.312396 2.590523 > m2$coefficients > (Intercept) age > -1.312396 2.590523 > > But then: > > AIC(m1) > [1] 428.9243 > AIC(m2) > [1] 423.9153 > > I am trying to compare between more complex models, i.e. different ways of > incorporating spatial self-correlation, and this issue with the AIC is > really making me silly! > > Thanks, > > S. Begueria -- 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
