extraSuperpower (version 1.5.0)

twoway_simulation_testing: Calculate power for global main effects and interaction from two-way factorial simulated data

Description

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.

Usage

twoway_simulation_testing(data, test = "ANOVA", alpha = 0.05)

Value

A data.frame with the power and 95% confidence interval for each of the main effects and their interaction.

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.

Examples

## 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