I am trying to analyse a data with gls/lm using the following set of models
prcn.0.lm <- lm( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1()) prcn.0.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1m.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1(),method="ML") I get the following AICs for these models: > AIC(prcn.1m.gls) [1] -78.3 > AIC(prcn.1.gls) [1] -46.3 > AIC(prcn.0.gls) [1] -24.7 > AIC(prcn.0.lm) [1] -59.8 It is the difference between the last two, which puzzles me. They are the same models. So I can't compare the AICs of prcn.0.lm and prcn.1.gls directly. When using anova() for the comparison, I get a sensible result: > anova(prcn.1.gls,prcn.0.lm) Model df AIC BIC logLik Test L.Ratio p-value prcn.1.gls 1 6 -46.3 -28.62 29.1 prcn.0.lm 2 5 -24.7 -9.97 17.3 1 vs 2 23.6 <.0001 Multiple arguments in AIC() give: > AIC(prcn.1.gls,prcn.0.lm) df AIC prcn.1.gls 6 -46.3 prcn.0.lm 5 -59.8 How can I be sure to make it right? ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help