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Doing a mixed-ANOVA after accounting for a covariate
6 messages · Dror D Lev, David Winsemius, Richard M. Heiberger
On Dec 26, 2010, at 7:42 AM, Dror D Lev wrote:
Dear r helpers, I would like to look at the interaction between two two-level factors, one between and one within participants, after accounting for any variance due to practice (31 trials in each of two blocks) in the task. It seems to require treating practice as a covariate. All the examples I noticed for handling covariates (i.e. ANCOVA, including the ones in Faraway's "Practical regression and anova using r") use lm(), but this doesn't handle repeated-measures.
See if Dalgaard's piece in R-News offers better guidance: http://www.r-project.org/doc/Rnews/Rnews_2007-2.pdf
I thought of a solution in the form of first running a regression on the covariate:
cov.accnt = lm (myMeasure ~ myCovMeasure, data=dat)
and then run the aov() on the residuals:
m.aov = aov (cov.accnt$residuals ~ withinVar*betweenVar +
Error(subj/withinVar, data=dat) Does it seem to be a valid answer to my problem? Is there an existing function that can do this (perhaps more appropriately)? Thank you for any help, dror
David Winsemius, MD West Hartford, CT
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On Dec 26, 2010, at 9:55 AM, Dror D Lev wrote:
Thank you David, for the reference to Dalgaard's paper in Rnews_2007-2. Unfortunately I don't seem to have the mathematical-statistical sophistication required to adapt the example in Dalgaard's paper for my case. I hope someone can suggest a less-mathematical direction for solution.
Here's what I would suggest if you want to stay more concrete. If you are not prepared to offer a minimal subset of your own data and also provide working or non-working code that uses it, then pick an available dataset that resembles it in structure and autocorrelation. One possibility would be the BodyWeight dataset in either the nlme or the MEMSS packages (although see below for my current level of uncertainty regarding your data). require(nlme) plot(BodyWeight)
Thanks again, dror ---------------------------- On Sun, Dec 26, 2010 at 3:59 PM, David Winsemius <dwinsemius at comcast.net
wrote:
On Dec 26, 2010, at 7:42 AM, Dror D Lev wrote: Dear r helpers, I would like to look at the interaction between two two-level factors, one between and one within participants, after accounting for any variance due to practice (31 trials in each of two blocks) in the task. It seems to require treating practice as a covariate.
I had trouble figuring out exactly what you meant by 31 trials in two blocks. Was that 31 trials by each participant? Or was it two trials by each of 31 participants divided unequally into two groups?
David. > > All the examples I noticed for handling covariates (i.e. ANCOVA, > including > the ones in Faraway's "Practical regression and anova using r") use > lm(), > but this doesn't handle repeated-measures. > > See if Dalgaard's piece in R-News offers better guidance: > > http://www.r-project.org/doc/Rnews/Rnews_2007-2.pdf > > > > > I thought of a solution in the form of first running a regression on > the > covariate: > cov.accnt = lm (myMeasure ~ myCovMeasure, data=dat) > > and then run the aov() on the residuals: > m.aov = aov (cov.accnt$residuals ~ withinVar*betweenVar + > Error(subj/withinVar, data=dat) > > Does it seem to be a valid answer to my problem? > > Is there an existing function that can do this (perhaps more > appropriately)? > > Thank you for any help, > dror > -- > > David Winsemius, MD > West Hartford, CT > > David Winsemius, MD West Hartford, CT
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