I am following the example in this webpage about Single Factor Designs With Repeated Measures. I the end i wish to compute the Grand mean using model.tables. Everytime i get this error message:
model.tables(aov.out,"means")
Error in FUN(X[[1L]], ...) :
eff.aovlist: non-orthogonal contrasts would give an incorrect answer
these are my data:
subject<- c(1,2,3,4,5,6,7,8,9,10)
time1 <- c(5040,3637,6384,5309,5420,3549,2385,5140,3890,3910)
time2 <- c(5067, 3668, 6689, 6489, 5246, 3922, 3408, 6613, 4063, 3937)
time3 <- c( 3278, 3814, 8745, 4760, 4911, 5716, 5547, 5844, 4914, 4390)
time4 <- c( 0, 2971, 0, 2776, 2128, 1208, 2935, 2739, 3054, 3363)
time5 <- c(4161, 3483, 6728, 5008, 5562, 4380, 4006, 7536, 3805, 3923)
time6 <- c( 3604, 3411, 2523, 3264, 3578, 2941, 2939, 47, 3612, 3604)
mydata <- data.frame(time1, time2, time3, time4, time5, time6)
mydata2 = stack(mydata)
subject = rep(subject,6)
mydata2[3] = subject
colnames(mydata2) = c("values", "time", "subject")
aov.out = aov(values ~ time + Error(subject/time), data=mydata2)
summary(aov.out)
model.tables(aov.out,"means")
You probably should be treating "subject" as a categorical variable rather than numeric. You can make this clear to R using
in your above example.