1d:T799,# Calculate power for global main effects and interaction from two-way factorial simulated data This functions takes the output of either the `twoway_simulation_independent` or the `twoway_simulation_correlated` functions and calculates the power of the sample size used in the simulation under parametric analysis of variance, rank based analysis of variance or permutation testing. ```r twoway_simulation_testing(data, test = "ANOVA", alpha = 0.05) ``` ## Arguments - `data`: * Simulation obtained from the `twoway_simulation_independent` or `twoway_simulation_correlated` - `test`: * The test to be applied. Possible values are "ANOVA" (default), "rank" and "permutation". - `alpha`: * Type I error rate. Default is 0.05. ## Returns A `data.frame` with the power and 95% confidence interval for each of the main effects and their interaction. ## Examples ```r ## After creating a 'matrices_obj' with the 'calculate_mean_matrix' function. refmean <- 1 treatgroups <- 4 timepoints <- 5 treateff <- 1.5 timeeff <- 0.85 rho <- 0.8 withinf <- "fB" factors_levels_names <- list(treatment=letters[1:treatgroups], time=1:timepoints) effects_treat_time <- calculate_mean_matrix(refmean = refmean, fAeffect = treateff, fBeffect = timeeff, nlfA = treatgroups, nlfB = timepoints, rho = rho, withinf = withinf, label_list = factors_levels_names) n <- 7 correlated_sim <- twoway_simulation_correlated(group_size=n, matrices_obj=effects_treat_time, nsims=20) ##used smaller number of iterations to reduce computation time twoway_simulation_testing(correlated_sim) ## defaults to parametric analysis of variance twoway_simulation_testing(correlated_sim, test="rank") ## rank based analysis of variance ## permutation test is another option ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081

twoway_simulation_testing function