Determine sample size with the Region of Practical Equivalence Method (ROPE)

The present method provides an expected sample size such that compelling evidence in the form of the Highest Density Interval can be collected for a given eq band with a certain long-run probability.

Usage

ssp_anova_rope(
  mu,
  effect,
  eq_band,
  tpr,
  ci,
  prior_scale,
  iter,
  post_iter = 1000,
  sigma = 1,
  prior_location = 0,
  max_n = 10001,
  seed = NULL
)

twoway_ANOVA_rope_pwr(
  n = n1,
  effect = effect,
  eq_band = eq_band,
  iter = iter,
  post_iter = post_iter,
  mu = mu,
  sigma = sigma,
  ci = ci,
  prior_scale = prior_scale,
  prior_location = prior_location,
  seed = NULL
)

Arguments

mu: Numeric. The mean of the DV for each group.
effect: Character. The effect of interest (main effect A, main effect B).
eq_band: Numeric. The margin of the standardized ROPE interval.
tpr: Numeric. The desired long-run probability of the HDI fully falling inside the ROPE, given the means.
ci: Numeric. The percentage of the HDI which is fixed to 0.95 in the ShinyApp.
prior_scale: Numeric. The scale of the Cauchy prior which is fixed to 1 / sqrt(2) in the ShinyApp.
iter: Numeric. The number of iterations to calculate the TPR which is fixed to 1000 in the ShinyApp.
post_iter: Numeric. The number of iterations to estimate the posterior distribution.
sigma: Numeric. The standard deviation of the DV for the groups.
prior_location: Numeric. The scale of the Cauchy prior which is fixed to 1 / sqrt(2) in the ShinyApp.
max_n: Numeric. The maximum group size which is fixed to 500 in the ShinyApp.
seed: Numeric. Seed for the random calculations.
n: Numeric. The sample size per group during the tpr optimization process.

Value

The function returns a list of two named numeric vectors. The second `n1` the determined sample size per group. The third `tpr_out` is the TPR corresponding to the determined sample sizes.