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.
