Greetings R Community My goal is to make orthogonal contrasts among simple effects in analysis of repeated measures data. The SAS publication, on page 1224, shows how to make this type of contrasts in SAS. But, my search of books about repeated measures analysis using R, and on-line has not yielded a methodology. Hopefully, someone can direct me to a book or publication that will show me a methodology.
Statistical Analysis of Repeated Measures Data Using SAS Procedures http://cslras.pbworks.com/f/littell_j_anim_sci_76_4_analysis_of_repeated_measures_using_sas.pdf Attached is a csv data file (file name = heartRate.csv). My code for the repeated measures analysis is below. library("nlme") # with AR1 variance/covariance structure, with ordered statement heartRate$time <- factor(heartRate$time) model2a <- lme(HR ~ drug*ordered(time), random =~1|person, correlation =corAR1(, form=~1|person), data = heartRate) summary(model2a) anova(model2a) Making a new variable ‘simple’ that merges the variables drug and time will enable me to make orthogonal contrasts among the simple effects. But, when using the variable ‘simple’ as the independent variable, the data will no longer be fitted to the AR1 variance/co-variance structure. Thanks. Best regards, James F.Henson ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
