Problem comparing Akaike's AIC - nlme package
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, ripley at stats.ox.ac.uk 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