plot(Delta.0) Similarly, the following code would allow the determination of the familywise error rate under the global null hypothesis (when analysing using maximum likelihood estimation and using quantile substi- tution on the identified boundaries) of the design discussed in Appendix B In order to ease the understanding of the forms of Equations (4.1) through (4.4), Matlab code employing symbolic algebra to return their forms is available. The user details a value for D , and four matrices are then returned. For example, consider the case D = 4 eq4 2 contains the Σ$_{r}$ for r = 2 , 3 , 4. Specifically 4.2. Matlab 34 >> eq4_2 eq4_2 = [ b^2 + e^2, b^2, b^2, b^2] [ b^2, b^2 + e^2, b^2, b^2] [ b^2, b^2, b^2 + e^2, b^2] [ b^2, b^2, b^2, b^2 + e^2] [ b^2 + e^2, b^2, b^2, 0] [ b^2, b^2 + e^2, b^2, 0] [ b^2, b^2, b^2 + e^2, 0] [ 0, 0, 0, 0] [ b^2 + e^2, b^2, 0, 0] [ b^2, b^2 + e^2, 0, 0] [ 0, 0, 0, 0] [ 0, 0, 0, 0] # Plot E(N | tau_1 = ... = tau_(D-1) = theta) simulate.fwer <- simulategsco(REML = F, adjust = T) >> [eq4_1, eq4_2, eq4_3, eq4_4] = groupSeqCrossoverMatrices(4);