
To account for the strong serial correlation you 
could try the lme() function of the nlme package. 
There you can apply different covariance 
structures in your linear model such as a 
first-order autoregressive covariance structure (AR1).

example:

model.fit <- lme(response ~ condition * time, 
data = time.series.data, random=~1|case, correlation = corCAR1());

This model uses an autoregressive process for 
continous data. The random expression defines the 
intercept for each case (or observation, subject) 
as a random factor. Condition and time would be 
fixed factors in this case. See also help(lme) and help(corClasses).

Hopes that helps,

Stephan




Stephan Moratti, PhD
Centro de Magnetoencefalografía Dr. Perez Modrego
Faculdad de Medicina
Universidad Complutense de Madrid
Pabellón 8
Avda. Complutense, s/n
28040 Madrid
Spain
email: moratti@med.ucm.es
Tel.:   +34 91 394 2292
Fax.:   +34 91 394 2294  
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