For ordered alternatives - Section 6.6 - use cHaySton and pHaySton (or cHayStonLSA) in R
For treatments vs. control - Section 6.7
- use cMaxNorm and cNDWol in R
Exercises 63 and 64 on page 277
One Sided All Treatment Multiple Comparisons
After rejecting the null hypothesis in favor of the ordered alternative with the Jonckheere-Terpstra procedure, we identify the significant differences by
computing the pairwiseWilcoxon rank sum statistics and standardized versions as in Section 6.5
deciding tau (v)>tau(u) if the standardized rank sum is > the C.V.
L.S.A. - we decide tau(v)>tau(u) if the standardized rank sum is > the C.V.
Ties - Use average ranks and the same correction as in Section 6.5 in the denominator of the standardized rank sums
One Sided Treatment Versus Control Multiple Comparisons
After rejecting the null hypothesis in favor of the alternative that the treatments are at least as large as the control with the Fligner-Wolfe procedure, we identify the significant differences by
jointly ranking all the sample observations from 1, …, N
computing the averages of the ranks assigned to each treatment
calculating the differences between the treatment averages and the control average
deciding tau(u)>tau(1) (at an experimentwise error rate of alpha) if N*(R.u-R.1) > the C.V., where N* is the least common multiple of n1, n2, …, nk Note: N* is defined differently in Section 6.4.
Final Comments on Treatment versus Control Comparisons
If the null hypothesis is rejected in favor of the alternative that the treatments are at least as small as the control with the Fligner-Wolfe procedure, then we apply the procedures above with (R.u-R.1) replaced by (R.1-R.u).
Use average ranks for ties
L.S.A.
Please complete your reading of Chapter 6 for class on Wednesday.