>
> Have a look at
>
> http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf
>

Wow. I think my students would keel over.


Anova() from the car package looks promising - I will check it out. Thanks


On Tue, Mar 3, 2009 at 4:00 PM, Peter Dalgaard <p.dalgaard@biostat.ku.dk>wrote:

> 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
>
> --
>   O__  ---- Peter Dalgaard             Øster Farimagsgade 5, Entr.B
>  c/ /'_ --- Dept. of Biostatistics     PO Box 2099, 1014 Cph. K
>  (*) \(*) -- University of Copenhagen   Denmark      Ph:  (+45) 35327918
> ~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk)              FAX: (+45) 35327907
>



-- 
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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