[R] repeated measures regression

[Marco B]

Hi John,

I have collected a few methods for doing this in a very empyrical
fashion. I've asked a few questions on r-help about them, and got
mixed responses. You can find the archived thread at:

http://tolstoy.newcastle.edu.au/R/e2/help/07/05/16660.html

The responses and linked resources might be of some interest to you,
too... Basically, my understanding is that ANOVA procedures are the
most powerful ones, provided you can meet their  assumptions. MANOVA
procedures do not require sphericity, but your design should be
balanced and time intervals should be equally-spaced. Finally,
assumptions for lme(r) models are the most forgiving, but their power
is also reduced.

I may be wrong on my conclusions, though, so I'm looking forward to
comments on this, especially on the lme(r) approaches...

Regards,

-- 
Marco B


On 5/17/07, John Christie <jc at or.psychology.dal.ca> wrote:
>
> How does one go about doing a repeated measure regression? The
> documentation I have on it (Lorch & Myers 1990) says to use linear /
> (subj x linear) to get your F.  However, if I put subject into glm or
> lm I can't get back a straight error term because it assumes
> (rightly) that subject is a nominal predictor of some sort.
>
> In looking at LME it seems like it just does the right thing here if
> I enter the random effect the same as when looking for ANOVA like
> results out of it.  But, part of the reason I'm asking is that I
> wanted to compare the two methods.  I suppose I could get it out of
> aov but isn't that built on lm?  I guess what I'm asking is how to
> calculate the error terms easily with lm.
>
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