[R] AIC and anova, lme

[Dieter Menne]

Patrick Giraudoux <patrick.giraudoux <at> univ-fcomte.fr> writes:

> 
> Dear listers,
> 
> Here we have a strange result we can hardly cope with. We want to 
> compare a null mixed model with a mixed model with one independent 
> variable.
> 
>  > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2)
>  > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, 
> na.action=na.omit, data=bdd2)
...
> The usual conclusion would be that the two models are equivalent and to 
> keep the null model for parsimony (!).
> 
> However, an anova shows that the variable 'log(1e-04 + transat)' is 
> significantly different from 0 in model 2 (lmmedt9)
> 
>  > anova(lmmedt9)
>                      numDF denDF   F-value p-value
> (Intercept)              1    20 289.43109  <.0001
> log(1e-04 + transat)     1    20  31.18446  <.0001
> 

Ask the author of pgirmess to add some checks for the model as anova and
stepAIC do:

Dieter

-----
library(MASS)
library(nlme)
fm1 <- lme(distance ~ age, data = Orthodont) 
fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)


>>In anova.lme(fm1, fm2) :
<<  Fitted objects with different fixed effects. REML comparisons are not<<
meaningful.

stepAIC(fm2)
>>Error in extractAIC.lme(fit, scale, k = k, ...) : 
>>  AIC undefined for REML fit