Hi Paul,

Note that in your example subject/myfactor is conflated with the error

term. The error thrown when you use intervals on the lme object is a

result:

> am2 <- lme(dv ~ myfactor, random = ~1|subject/myfactor, data=mydata)

> intervals(am2)

Error in intervals.lme(am2) :

Cannot get confidence intervals on var-cov components: Non-positive

definite approximate variance-covariance

Also it's worth pointing out that the REML approach used by lme has

some advantages. For example:

- It is not sensitve to lack of balance in the design

- Variance estimates are restricted to the parameter space (i.e. no

negative variance estimates)

- Allows for estimation of parameters to model non-constant variance

within or between subjects via the weights argument.

- The correlation argument allows for estimation of correlation

structures when, for example, observations within subjects are

temporally or spatially correlated.

Kingsford Jones

On Mon, Mar 9, 2009 at 1:15 PM, Paul Gribble <

[hidden email]> wrote:

> After much research I've listed a couple of ways to do repeated measures

> anova here:

>

>

http://gribblelab.org/2009/03/09/repeated-measures-anova-using-r/>

> including univariate and multivariate methods, post-hoc tests, sphericity

> test, etc.

>

> It appears to me that the most useful way is a multivariate model and then

> using Anova() from the car package.

>

> -Paul

>

>

> On Tue, Mar 3, 2009 at 5:37 PM, Paul Gribble <

[hidden email]> wrote:

>

>> 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 <

[hidden email]>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

>>> ~~~~~~~~~~ - (

[hidden email]) 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

>>

[hidden email]
>>

http://gribblelab.org>>

>

>

>

> --

> 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

>

[hidden email]
>

http://gribblelab.org>

> [[alternative HTML version deleted]]

>

>

> ______________________________________________

>

[hidden email] 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.

>

>

______________________________________________

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https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide

http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.