Patrick, The likelihoods of two models fitted using REML cannot be compared unless the fixed effects are the same in the two models. On Tue, 2008-02-26 at 14:38 +0100, Patrick Giraudoux wrote:> 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) > > Using the Akaike Criterion and selMod of the package pgirmess gives the > following output: > > > selMod(list(lmmedt1,lmmedt9)) > model LL K N2K AIC deltAIC w_i AICc > deltAICc w_ic > 2 log(1e-04 + transat) 44.63758 4 7.5 -81.27516 0.000000 0.65 -79.67516 > 0.000000 0.57 > 1 1 43.02205 3 10.0 -80.04410 1.231069 0.35 -79.12102 > 0.554146 0.43 > > 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 > > Has anyone an opinion about what looks like a paradox here ? > > Patrick > > > > ______________________________________________ > R-help at 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.
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)
Using the Akaike Criterion and selMod of the package pgirmess gives the
following output:
> selMod(list(lmmedt1,lmmedt9))
model LL K N2K AIC deltAIC w_i AICc
deltAICc w_ic
2 log(1e-04 + transat) 44.63758 4 7.5 -81.27516 0.000000 0.65 -79.67516
0.000000 0.57
1 1 43.02205 3 10.0 -80.04410 1.231069 0.35 -79.12102
0.554146 0.43
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
Has anyone an opinion about what looks like a paradox here ?
Patrick
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