Le 21/03/2012 10:56, Patrick Giraudoux a écrit :
Hi,
Using lme from the package nlme 3.1-103, I meet a strange warning. I
am trying to compare to models with:
library(nlme)
lmez6=lme(lepus~vulpes,random=~1|troncon/an,data=ika_z6_test)
lmez60=lme(lepus~1,random=~1|troncon/an,data=ika_z6_test)
Both have the same number of observations and groups:
lmez6
Linear mixed-effects model fit by REML
Data: ika_z6_test
Log-restricted-likelihood: -2267.756
Fixed: lepus ~ vulpes
(Intercept) vulpes
1.35017117 0.04722338
Random effects:
Formula: ~1 | troncon
(Intercept)
StdDev: 0.8080261
Formula: ~1 | an %in% troncon
(Intercept) Residual
StdDev:1.086611 0.4440076
Number of Observations: 1350
Number of Groups:
troncon an %in% troncon
1691350
> lmez60
Linear mixed-effects model fit by REML
Data: ika_z6_test
Log-restricted-likelihood: -2266.869
Fixed: lepus ~ 1
(Intercept)
1.435569
Random effects:
Formula: ~1 | troncon
(Intercept)
StdDev: 0.8139646
Formula: ~1 | an %in% troncon
(Intercept) Residual
StdDev:1.086843 0.4445815
Number of Observations: 1350
Number of Groups:
troncon an %in% troncon
1691350
...but when I want to compare their AIC, I get:
AIC(lmez6,lmez60)
df AIC
lmez6 5 4545.511
lmez60 4 4541.737
Warning message:
In AIC.default(lmez6, lmez60) :
models are not all fitted to the same number of observations
Has anybody an explanation about this strange warning ? To what extent
this warning may limit the conclusions that could be drawn from AIC
comparison ?
Thanks in advance,
Patrick
Sorry to go on on the thread, I have created, but the trouble I meet is
above my level in stats... Actually, not using AIC but an anova
approach, I get a more informative message:
anova(lmez6, lmez60)
Model df AIC BIClogLik Test L.Ratio p-value
lmez6 1 5 4545.511 4571.543 -2267.756
lmez60 2 4 4541.737 4562.566 -2266.869 1 vs 2 1.774036 0.1829
Warning message:
In anova.lme(lmez6, lmez60) :
Fitted objects with different fixed effects. REML comparisons are not
meaningful.
And fubbling a bit more, I disclosed that this was an effect of fitting
the model using REML. If fitted using ML, things are going (apparently)
smoothly:
lmez6=lme(lepus~vulpes,random=~1|troncon/an,data=ika_z6_test,method="ML")
> lmez60=lme(lepus~1,random=~1|troncon/an,data=ika_z6_test,method="ML")
> anova(lmez6, lmez60)
Model df AIC BIClogLik Test L.Ratio p-value
lmez6 1 5 4536.406 4562.445 -2263.203
lmez60 2 4 4538.262 4559.093 -2265.131 1 vs 2 3.856102 0.0496
> AIC(lmez6,lmez60)
df AIC
lmez6 5 4536.406
lmez60 4 4538.262
Now I have the following problem. What I understood from Pinheiro and
Bates's book and some forums, is that ML estimations are biased to some
extent tending to underestimate variance parameters. So probably not to
recommend however results looks consistent here.
Thus, I am lost. The two models looks to me clearly embedded (one is
just a null model with the only intercept to estimate and the other with
intercept + one independent variable (numeric), both have the same
random effects, the same response variable and the same number of
observations). Warnings, from this point of view sounds inconsistent.
They are probably not, but beyond my understanding...
Any idea ?
Patrick
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