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 <pgrib...@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 <pgrib...@uwo.ca> 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 >> <p.dalga...@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.dalga...@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 >> pgrib...@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 > pgrib...@uwo.ca > http://gribblelab.org > > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help@r-project.org 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. > > ______________________________________________ R-help@r-project.org 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.
