You should have better response posting to the r-sig-mixed-models list
(repeated measures are mixed effects models).

-- Bert

On Sun, Dec 2, 2012 at 12:18 PM, Giuseppe Pagnoni <gpagnoni@gmail.com>wrote:

> Dear all,
>
> I am having quite a hard time in trying to figure out how to correctly
> spell out a model in R (a repeated-measures anova with a
> within-subject covariate, I guess).  Even though I have read in the
> posting guide that statistical advice may or may not get an answer on
> this list, I decided to try it anyway, hoping not to incur in
> somebody's ire for misusing the tool.
>
> For the sake of clarity, I will explain the problem.
>
> We conducted an experiment measuring average response times in a
> cognitive task. The task has two types of stimuli (stim1, stim2) and
> was performed in a 6-run session, where subjects performed the task
> under condition A on odd-numbered runs and under condition B on
> even-numbered runs.  Thus, the temporal sequence of the runs was the
> following:
>
> - run 1: cond A
> - run 2: cond B
> - run 3: cond A
> - run 4: cond B
> - run 5: cond A
> - run 6: cond B
>
> where for each run and for each subject, an average RT was collected
> for stim1 and for stim2.
>
> After collecting and plotting the data, an approximately linear
> decrease in RT from run1 to run6 was apparent in most subjects
> (practice effect: subjects become better and faster with time).
>
> Now, I am struggling with how to properly specify a model to perform
> the group analysis by taking into account this confounding practice
> effect, so that the real effects of interest (the main effect of
> condition, and the interaction of condition and stimulus type) can be
> better assessed.
>
>  I used a dataframe in long format, with `subj', `rt' (response time),
> `stim' (stim1, stim2), `cond' (A, B), and `run' (1 to 6) as columns,
> where `run' is coded as an integer so that it can be used for modeling
> a linear trend. The R command I tried is:
>
>   rt.aov <- aov(rt ~ run + stim * cond + Error(subj /(run + stim *
> cond)), data=rt.df)
>
> but I am not at all sure that the error term is correctly specified.
>
> Furthermore, we have also collected data on an additional batch of
> subjects that performed the task in the 6-run session but with the
> order of conditions A and B reversed (A on even-numbered runs and B on
> odd-numbered runs); now, if we wanted to analyze the data from the two
> groups of subjects together, by including a between-subjects group
> factor (groupAB, groupBA), would the model specification become
> something like the following?
>
>     rt.aov <- aov(rt ~ run + group * stim * cond + Error(subj /(run +
> stim * cond)), data=rt.df)
>
> Perhaps should lme() be used instead (and with which formula?)?
>
> Many thanks in advance to anybody who'd be so kind to offer their
> advice or tip on this.  I have scoured the web and some textbooks for
> a few days now, but to little avail.
>
> very best
>
> giuseppe
>
>
> --
> Giuseppe Pagnoni
> Dip. Scienze Biomediche, Metaboliche e Neuroscienze
> Sezione Fisiologia e Neuroscienze
> Univ. di Modena e Reggio Emilia
> Via Campi 287
> I-41125 Modena, Italy
> Tel: +39-059-205-5742
> Fax: +39-059-205-5363
>
> ______________________________________________
> R-help@r-project.org 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.
>



-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm

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