Dear Gabriel and Bert, Bert's points are well taken, but you can compute tests of linear hypotheses for a repeated-measures MANOVA using the linear.hypothesis function in the car package. I'm not sure how you'd correct these tests for simultaneous inference with anything other than a Bonferroni adjustment.
Regards, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Bert Gunter > Sent: April-16-10 4:19 PM > To: 'Kaufman Gabriel'; r-help@r-project.org > Subject: Re: [R] Multiple comparisons on Anova.mlm object > > Gabriel: > > The post hoc comparison tests that you reference are of doubtful validity or > utility in anything but balanced designs with simple covariance structures. > With missing data there are two critical issues: why are the data missing > and how do they need to be handled as a result? -- just ignoring them may > produce biased results if it's "informative" missingness, and inference is > even more of a headache(it's difficult, unknown, or unresolvable depending > on the details). I strongly suggest you consult a local statistical expert. > > > Bert Gunter > Genentech Nonclinical Statistics > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Kaufman Gabriel > Sent: Friday, April 16, 2010 12:32 PM > To: r-help@r-project.org > Subject: [R] Multiple comparisons on Anova.mlm object > > I would like to perform multiple comparisons or post-hoc testing on the > independent variable in an Anova.mlm object generated by the Anova function > of the car package. I have defined a multivariate linear model and > subsequently performed a repeated measures ANOVA as per the instructions in > section #3 of the following comprehensive tutorial on the subject from the > Gribble lab at UWO: > http://gribblelab.org/2009/03/09/repeated-measures-anova-using-r > Unfortunately, since my data has missing values,I can't seem to use the > classical univariate approaches of aov() or lme() (suggested in sections #1 > and #2 of the tutorial linked to above). > > The relevant portions of the R console output are copied below (redacted > somewhat for intellectual property considerations). In sum, I am stuck at > the Anova.mlm object, as I cannot seem to apply any of the standard multiple > comparisons functions such as pairwise.t.test or p.adjust.... > > > Thank you in advance for your help. > > > Gabriel Kaufman > Orthopedic Molecular Biology Laboratory > Centre de recherche CHU Sainte-Justine > Montreal, Quebec > > -------------------------- > > R console output > > > # define Treatment group as the factor defining the intra-subject model > > as.factor(Treatment) > > ## define repeated measures linear model > > # define repeated-measures data as matrix vector > > RM <- cbind(repeated_measure_1, repeated_measure_2, repeated_measure_3, > repeated_measure_4, repeated_measure_5) > > mlm <- lm(RM ~ Treatment, data = RMdata.file) > > # load required package car > > library(car) > > ## Define Anova model object for repeated-measures ANOVA > > # define idata data frame > > idata <- data.frame(RM = factor(1:5)) > > # define Anova object > > mlm.aov <- Anova(mlm, idata = idata,idesign = ~RM, type = "II") > > # display class of Anova object > > class(mlm.aov) > [1] "Anova.mlm" > > # display session information > > sessionInfo() > R version 2.10.1 (2009-12-14) > i386-apple-darwin9.8.0 > > locale: > [1] en_CA.UTF-8/en_CA.UTF-8/C/C/en_CA.UTF-8/en_CA.UTF-8 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] car_1.2-16 > > ______________________________________________ > 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. ______________________________________________ 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.
