R help - Mar 2009 - repeated measures anova, sphericity, epsilon, etc

I have 3 questions (below).

Background: I am teaching an introductory statistics course in which we are
covering (among other things) repeated measures anova. This time around
teaching it, we are using R for all of our computations. We are starting by
covering the univariate approach to repeated measures anova.

Doing a basic repeated measures anova (univariate approach) using aov()
seems straightforward (e.g.:

+> myModel<-aov(myDV~myFactor+Error(Subjects/myFactor),data=myData)
+> summary(myModel)

Where I am currently stuck is how best to deal with the issue of the
assumption of homogeneity of treatment differences (in other words, the
sphericity assumption) - both how to test it in R and how to compute
corrected df for the F-test if the assumption is violated.

Back when I taught this course using SPSS it was relatively straightforward
- we would look at Mauchly's test of sphericity - if it was significant,
then we would use one of the corrected F-tests (e.g. Greenhouse-Geisser or
Huynh-Feldt) that were spat out automagically by SPSS.

I gather from searching the r-help archives, searching google, and searching
through various books on R, that the only way of using mauchly.test() in R
is on a multivariate model object (e.g. mauchly.test cannot handle an aov()
object).

Question 1: how do you (if you do so), test for sphericity in a repeated
measures anova using R, when using aov()? (or do you test the sphericity
assumption using a different method)?

Question 2: Can someone point me to an example (on the web, in a book,
wherever) showing how to perform a repeated measures anova using the
multivariate approach in R?

Question 3: Are there any existing R functions for calculating adjusted df
for Greenhouse-Geisser, Huynh-Feldt (or calculating epsilon), or is it up to
me to write my own function?

Thanks in advance for any suggestions,

-- 
Paul L. Gribble, Ph.D.
Associate Professor
Dept. Psychology
The University of Western Ontario
London, Ontario
Canada N6A 5C2
Tel. +1 519 661 2111 x82237
Fax. +1 519 661 3961
pgribble@uwo.ca
http://gribblelab.org

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Paul Gribble wrote:
> I have 3 questions (below).
> 
> Background: I am teaching an introductory statistics course in which we are
> covering (among other things) repeated measures anova. This time around
> teaching it, we are using R for all of our computations. We are starting by
> covering the univariate approach to repeated measures anova.
> 
> Doing a basic repeated measures anova (univariate approach) using aov()
> seems straightforward (e.g.:
> 
> +> myModel<-aov(myDV~myFactor+Error(Subjects/myFactor),data=myData)
> +> summary(myModel)
> 
> Where I am currently stuck is how best to deal with the issue of the
> assumption of homogeneity of treatment differences (in other words, the
> sphericity assumption) - both how to test it in R and how to compute
> corrected df for the F-test if the assumption is violated.
> 
> Back when I taught this course using SPSS it was relatively straightforward
> - we would look at Mauchly's test of sphericity - if it was
significant,
> then we would use one of the corrected F-tests (e.g. Greenhouse-Geisser or
> Huynh-Feldt) that were spat out automagically by SPSS.
> 
> I gather from searching the r-help archives, searching google, and
searching
> through various books on R, that the only way of using mauchly.test() in R
> is on a multivariate model object (e.g. mauchly.test cannot handle an aov()
> object).
> 
> Question 1: how do you (if you do so), test for sphericity in a repeated
> measures anova using R, when using aov()? (or do you test the sphericity
> assumption using a different method)?
> 
> Question 2: Can someone point me to an example (on the web, in a book,
> wherever) showing how to perform a repeated measures anova using the
> multivariate approach in R?
> 
> Question 3: Are there any existing R functions for calculating adjusted df
> for Greenhouse-Geisser, Huynh-Feldt (or calculating epsilon), or is it up
to
> me to write my own function?
> 
> Thanks in advance for any suggestions,
Have a look at

http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf

Last time this came up, John Fox also pointed to some of his stuff, see 
http://finzi.psych.upenn.edu/R/Rhelp08/archive/151282.html

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