repeated measures anova, sphericity, epsilon, etc
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 <pgribble at uwo.ca> 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 <pgribble at uwo.ca> wrote:
Have a look at
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 at 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 ? ? ? ? ? ? ?ster Farimagsgade 5, Entr.B ?c/ /'_ --- Dept. of Biostatistics ? ? PO Box 2099, 1014 Cph. K ?(*) \(*) -- University of Copenhagen ? Denmark ? ? ?Ph: ?(+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at 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 at uwo.ca 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 pgribble at uwo.ca http://gribblelab.org ? ? ? ?[[alternative HTML version deleted]]
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