Dear Gijs:
 
lme fits models using restricted maximum likelihood by default. So, I believe this is why you have a different DF. If you include method="ML" in the modeling function the DF should be similar to aov.
 
I think your lme code is incorrect given my understanding of your problem. You can use all data points using a repeated measures design. Each subject is measured on multiple occasions. So, observations are nested within individual. You need to add a time variable to indicate the measurement occasion for each subject (e.g., t={0,...,T}). You might try the following:
 
fm1.lme<-lme(rt~time*fact1*fact2*fact3, data=mydata, random=~time|sub, method="ML")
 
Use summary() to get the entire output
 
hope this helps
 
Harold

	-----Original Message----- 
	From: r-help-bounces@stat.math.ethz.ch on behalf of Gijs Plomp 
	Sent: Thu 8/12/2004 4:13 AM 
	To: r-help@stat.math.ethz.ch 
	Cc: 
	Subject: Fwd: [R] Enduring LME confusion… or Psychologists and Mixed-Effects 
	
	

	I will follow the suggestion of John Maindonald and present the problem
	by example with some data.
	
	I also follow the advice to use mean scores, somewhat reluctantly
	though. I know it is common practice in psychology, but wouldn’t it be
	more elegant if one could use all the data points in an analysis?
	
	The data for 5 subjects (myData) are provided at the bottom of this
	message. It is a crossed within-subject design with three factors and
	reaction time (RT) as the dependent variable.
	
	An initial repeated-measures model would be:
	aov1<-aov(RT~fact1*fact2*fact3+Error(sub/(fact1*fact2*fact3)),data=myData)
	
	Aov complains that the effects involving fact3 are unbalanced:
	 > aov1
	…
	Stratum 4: sub:fact3
	Terms:
	                      fact3   Residuals
	Sum of Squares  4.81639e-07 5.08419e-08
	Deg. of Freedom           2           8
	
	Residual standard error: 7.971972e-05
	
	6 out of 8 effects not estimable
	Estimated effects may be unbalanced
	…
	
	Presumably this is because fact3 has three levels and the other ones
	only two, making this a ‘non-orthogonal’ design.
	
	I then fit an equivalent mixed-effects model:
	lme1<-lme(RT~fact1*fact2*fact3,data=meanData2,random=~1|sub)
	
	Subsequently I test if my factors had any effect on RT:
	 > anova(lme1)
	                  numDF denDF   F-value p-value
	
	(Intercept)           1    44 105294024  <.0001
	fact1                 1    44        22  <.0001
	fact2                 1    44         7  0.0090
	fact3                 2    44        19  <.0001
	fact1:fact2           1    44         9  0.0047
	fact1:fact3           2    44         1  0.4436
	fact2:fact3           2    44         1  0.2458
	fact1:fact2:fact3     2    44         0  0.6660
	
	Firstly, why are the F-values in the output whole numbers?
	
	The effects are estimated over the whole range of the dataset and this
	is so because all three factors are nested under subjects, on the same
	level. This, however, seems to inflate the F-values compared to the
	anova(aov1) output, e.g.
	 >  anova(aov1)
	…
	Error: sub:fact2
	          Df     Sum Sq    Mean Sq F value Pr(>F)
	fact2      1 9.2289e-08 9.2289e-08  2.2524 0.2078
	Residuals  4 1.6390e-07 4.0974e-08
	…
	
	I realize that the (probably faulty) aov model may not be directly
	compared to the lme model, but my concern is if the lme estimation of
	the effects is right, and if so, how can a naïve skeptic be convinced of
	this?
	
	The suggestion to use simulate.lme() to find this out seems good, but I
	can’t seem to get it working (from "[R] lme: simulate.lme in R" it seems
	it may never work in R).
	
	I have also followed the suggestion to fit the exact same model with
	lme4. However, format of the anova output does not give me the
	estimation in the way nlme does. More importantly, the degrees of
	freedom in the denominator don’t change, they’re still large:
	 > library(lme4)
	 > lme4_1<-lme(RT~fact1*fact2*fact3,random=~1|sub,data=myData)
	 > anova(lme4_1)
	Analysis of Variance Table
	
	                     Df    Sum Sq   Mean Sq Denom F value    Pr(>F) 
	fact1I                1 2.709e-07 2.709e-07    48 21.9205 2.360e-05 ***
	fact2I                1 9.229e-08 9.229e-08    48  7.4665  0.008772 **
	fact3L                1 4.906e-08 4.906e-08    48  3.9691  0.052047 .
	fact3M                1 4.326e-07 4.326e-07    48 34.9972 3.370e-07 ***
	fact1I:fact2I         1 1.095e-07 1.095e-07    48  8.8619  0.004552 **
	fact1I:fact3L         1 8.988e-10 8.988e-10    48  0.0727  0.788577 
	fact1I:fact3M         1 1.957e-08 1.957e-08    48  1.5834  0.214351 
	fact2I:fact3L         1 3.741e-09 3.741e-09    48  0.3027  0.584749 
	fact2I:fact3M         1 3.207e-08 3.207e-08    48  2.5949  0.113767 
	fact1I:fact2I:fact3L  1 2.785e-09 2.785e-09    48  0.2253  0.637162 
	fact1I:fact2I:fact3M  1 7.357e-09 7.357e-09    48  0.5952  0.444206 
	---
	
	I hope that by providing a sample of the data someone can help me out on
	the questions I asked in my previous mail:
	
	 >> 1. When aov’s assumptions are violated, can lme provide the right
	 >> model for within-subjects designs where multiple fixed effects are
	 >> NOT hierarchically ordered?
	 >>
	 >> 2. Are the degrees of freedom in anova(lme1) the right ones to
	 >> report? If so, how do I convince a reviewer that, despite the large
	 >> number of degrees of freedom, lme does provide a conservative
	 >> evaluation of the effects? If not, how does one get the right denDf
	 >> in a way that can be easily understood?
	
	If anyone thinks he can help me better by looking at the entire data
	set, I very much welcome them to email me for further discussion.
	
	In great appreciation of your help and work for the R-community,
	
	Gijs Plomp
	g.plomp@brain.riken.jp
	
	
	
	 >myData
	sub fact1 fact2 fact3        RT
	1   s1     C     C     G 0.9972709
	2   s2     C     C     G 0.9981664
	3   s3     C     C     G 0.9976909
	4   s4     C     C     G 0.9976047
	5   s5     C     C     G 0.9974346
	6   s1     I     C     G 0.9976206
	7   s2     I     C     G 0.9981980
	8   s3     I     C     G 0.9980503
	9   s4     I     C     G 0.9980620
	10  s5     I     C     G 0.9977682
	11  s1     C     I     G 0.9976633
	12  s2     C     I     G 0.9981558
	13  s3     C     I     G 0.9979286
	14  s4     C     I     G 0.9980474
	15  s5     C     I     G 0.9976030
	16  s1     I     I     G 0.9977088
	17  s2     I     I     G 0.9981506
	18  s3     I     I     G 0.9980494
	19  s4     I     I     G 0.9981183
	20  s5     I     I     G 0.9976804
	21  s1     C     C     L 0.9975495
	22  s2     C     C     L 0.9981248
	23  s3     C     C     L 0.9979146
	24  s4     C     C     L 0.9974583
	25  s5     C     C     L 0.9976865
	26  s1     I     C     L 0.9977107
	27  s2     I     C     L 0.9982071
	28  s3     I     C     L 0.9980966
	29  s4     I     C     L 0.9980372
	30  s5     I     C     L 0.9976303
	31  s1     C     I     L 0.9976152
	32  s2     C     I     L 0.9982363
	33  s3     C     I     L 0.9978750
	34  s4     C     I     L 0.9981402
	35  s5     C     I     L 0.9977018
	36  s1     I     I     L 0.9978076
	37  s2     I     I     L 0.9981699
	38  s3     I     I     L 0.9980628
	39  s4     I     I     L 0.9981092
	40  s5     I     I     L 0.9977054
	41  s1     C     C     M 0.9978842
	42  s2     C     C     M 0.9982752
	43  s3     C     C     M 0.9980277
	44  s4     C     C     M 0.9978250
	45  s5     C     C     M 0.9978353
	46  s1     I     C     M 0.9979674
	47  s2     I     C     M 0.9983277
	48  s3     I     C     M 0.9981954
	49  s4     I     C     M 0.9981703
	50  s5     I     C     M 0.9980047
	51  s1     C     I     M 0.9976940
	52  s2     C     I     M 0.9983019
	53  s3     C     I     M 0.9982484
	54  s4     C     I     M 0.9981784
	55  s5     C     I     M 0.9978177
	56  s1     I     I     M 0.9978636
	57  s2     I     I     M 0.9982188
	58  s3     I     I     M 0.9982024
	59  s4     I     I     M 0.9982358
	60  s5     I     I     M 0.9978581
	
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