I don't believe the issue is different. In some respects, lme and lmer essentially accomplish the same thing for mixed linear models. lmer differs in many ways than lme in how estimates are computed and in the type of models it can handle. It is probably best to read the new vignettes in the lme4 package to see inside lmer a bit more. 

However, if you estimate the same model with lme and lmer to the same data you'll get the same results. But, lme gives DF and p-values but lmer does not.


-----Original Message-----
From: Manuel Morales [mailto:Manuel.A.Morales@williams.edu]
Sent: Mon 6/25/2007 3:32 PM
To: Doran, Harold
Cc: Jean-Baptiste Ferdy; r-help@stat.math.ethz.ch
Subject: Re: [R] degrees of freedom in lme
 
On Mon, 2007-06-25 at 13:15 -0400, Doran, Harold wrote:
> This is such a common question that it has a an "FAQ-like" response from Doug Bates. Google "lmer p-values and all that" to find the response. 

Isn't this a different question, though, since Jean-Baptiste is using
nlme. 

Details on the calculation of DF in nlme can be found in chapter 4 of
the book by Pinheiro and Bates "Mixed Effects Models in S and S-PLUS.
Using the formula provided, I get denDF of 10 for level 1 and 32 for
level 2. I'm not sure why lme is using the denDF estimated at level 2 in
this example ...

> > -----Original Message-----
> > From: r-help-bounces@stat.math.ethz.ch 
> > [mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of 
> > Jean-Baptiste Ferdy
> > Sent: Monday, June 25, 2007 12:26 PM
> > To: r-help@stat.math.ethz.ch
> > Subject: [R] degrees of freedom in lme
> > 
> > Dear all,
> > 
> > I am starting to use the lme package (and plan to teach a 
> > course based on it next semester...). To understand what lme 
> > is doing precisely, I used balanced datasets described in 
> > Pinheiro and Bates and tried to compare the lme outputs to 
> > that of aov. Here is what I obtained:
> > 
> > > data(Machines)
> > > summary(aov(score~Machine+Error(Worker/Machine),data=Machines))
> > Error: Worker
> >           Df  Sum Sq Mean Sq F value Pr(>F) Residuals  5 
> > 1241.89  248.38
> > 
> > Error: Worker:Machine
> >           Df  Sum Sq Mean Sq F value    Pr(>F)
> > Machine    2 1755.26  877.63  20.576 0.0002855 ***
> > Residuals 10  426.53   42.65
> > ---
> > Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> > 
> > Error: Within
> >           Df Sum Sq Mean Sq F value Pr(>F)
> > Residuals 36 33.287   0.925
> > > 
> > anova(lme(fixed=score~Machine,random=~1|Worker/Machine,data=Machines))
> >             numDF denDF  F-value p-value
> > (Intercept)     1    36 773.5709  <.0001
> > Machine         2    10  20.5762   3e-04
> >   
> > No problem here: the results are essentially the same, which 
> > is expected. Now I turn to an ANCOVA with a random grouping factor.
> > 
> > > data(Orthodont)
> > > OrthoFem <- Orthodont[Orthodont$Sex=="Female",];
> > > summary(aov(distance~age+Error(Subject/age),data=OrthoFem))
> > Error: Subject
> >           Df  Sum Sq Mean Sq F value Pr(>F) Residuals 10 
> > 177.227  17.723
> > 
> > Error: Subject:age
> >           Df Sum Sq Mean Sq F value    Pr(>F)
> > age        1 50.592  50.592  52.452 2.783e-05 ***
> > Residuals 10  9.645   0.965
> > ---
> > Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> > 
> > Error: Within
> >           Df Sum Sq Mean Sq F value Pr(>F) Residuals 22 9.8250  0.4466
> > > anova(lme(fixed=distance~age,random=~1+age|Subject,data=OrthoFem))
> >             numDF denDF   F-value p-value
> > (Intercept)     1    32 1269.7764  <.0001
> > age             1    32   52.4517  <.0001
> > 
> > This time the F values are (almost) identical, the numerator 
> > degrees of freedom are the same, but the denominator degrees 
> > of freedom are very different (10 for aov vs. 32 for lme). I 
> > understand that there is an issue with the estimation of that 
> > number, but I would naively expect the number given by lme to 
> > be close to that provided by aov is the case of a balanced 
> > dataset. That's obviously not true in the case of an 
> > ANCOVA... But why?? And how should I interpret the F-test 
> > given by anova.lme?
> > 
> > Thanks in advance for your help !
> > --
> > Jean-Baptiste Ferdy
> > Institut des Sciences de l'Évolution de Montpellier CNRS UMR 
> > 5554 Université Montpellier 2
> > 34 095 Montpellier cedex 05
> > tel. +33 (0)4 67 14 42 27
> > fax  +33 (0)4 67 14 36 22
> > 
> > ______________________________________________
> > R-help@stat.math.ethz.ch 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.
> >
> 
> ______________________________________________
> R-help@stat.math.ethz.ch 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.
-- 
Manuel A. Morales
http://mutualism.williams.edu


	[[alternative HTML version deleted]]

