
I would like to use mixed models in R to analyze EEG data, but I don't  
know if it is more correct to use lme or lmer.
My data have the following structure


Subject	Electrode	 Interval		Trial 	Condition		Ampl
1		Fp1		200-300		1		A			3.5
1		Fp1		200-300		2		B			4.2
1		Fp2		400-600		1		A			6.5
1		Fp2		400-600		2		B			3.3
2		Fp1		200-300		1		A			2.1
2		Fp1		200-300		2		B			-5.4
2		Fp2		400-600		1		A			-5.6
2		Fp2		400-600		2		B			-3.2
.
.
.

For sake of simplicity, here I include only 2 Trials but in the real  
dataset they are many more.
In this hypothetical dataset Ampl is the depentent variable. Electrode  
and Interval are two predictors. I expect that levels of Electrode  
will be highly correlated as well the levels of Interval.
My goal is to study if Condition influence Ampl and if interact with  
Electrode variable and Interval Variable.


I would fit a model on these data with lmer with the following structure

mod=lmer(Ampl~Electrode*Interval*Condition+(1+Electrode|Subject) 
+(1+Interval|Subject))

If I'm correct the corresponding lme model would be

mod=lme(Ampl~Electrode*Interval*Condition, random=list(~1+condition| 
Subject, ~1+Interval|Subject))


So my questions are:
Are these specification corrects?
Should I use lmer or lme?
Any suggestion for covariance matrix specification in lme?

Thanks in advance!!!














___________

Giorgio Arcara
Ph.D.

Department of General Psychology, University of Padua
Via Venezia 15, 35131 Padova - Italy
e-mail: giorgio.arcara@unipd.it
Phone:  +39 049 8276149
http://lcnl.psy.unipd.it/people/arcara.htm


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