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: [EMAIL PROTECTED]
Tel.: +34 91 394 2292
Fax.: +34 91 394 2294
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