[R] repeated measures regression
jc at or.psychology.dal.ca
Thu May 24 03:08:34 CEST 2007
Hmmm, been away and got this... I appreciate the effort but there
wasn't anything, in principle, in MASS on this I didn't already
know. My question is just more about the functioning of the lm
command and deriving these values. I understand that its the wrong
approach for repeated measures design and lme is more appropriate.
But, I wanted to examine / compare. So, my question still stands.
How does one get something like the subject x effect interaction term
Also, while I'm at it, anyone familiar with Blouin & Riopelle on
confidence intervals and repeated measures deigns? Is there a reason
the intervals() command should give me different values for the
narrow inference confidence intervals than they get from SAS?
On May 17, 2007, at 2:20 PM, Bert Gunter wrote:
> You need to gain some background. MIXED EFFECTS MODELS in S and S-
> PLUS by
> Pinheiro and Bates is a canonical reference for how to do this with R.
> Chapter 10 of Venables and Ripley's MASS(4th ed.) contains a more
> but very informative overview that may suffice. Other useful
> references can
> also be found on CRAN.
> Bert Gunter
> Genentech Nonclinical Statistics
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of John Christie
> Sent: Thursday, May 17, 2007 10:06 AM
> To: R-help at stat.math.ethz.ch
> Subject: [R] repeated measures regression
> 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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