exact_twoway_anova_power: Two-way factorial ANOVA exact sample size calculation for...

In extraSuperpower: Power Calculation for Two-Way Factorial Designs

Two-way factorial ANOVA exact sample size calculation for independent samples

Description

This functions takes the effect sizes (Cohen's f) for two main effects and their interaction and estimates power a range of sample sizes. The input for this function can be generated by effsize.

Usage

exact_twoway_anova_power(
  a,
  b,
  effect_sizes,
  n,
  alpha = 0.05,
  factor_names = NULL,
  plot = TRUE,
  title = NULL,
  target_power = NULL,
  target_line = TRUE,
  alpha_line = TRUE
)

Arguments

a	Number of levels of the first factor
b	Number of levels of the second factor
effect_sizes	Numeric vector of length 3. The first two elements are the effect sizes for the main effects of the first and second factors, respectively. The third element is the interaction effect size.
n	Number of experimental units in each group for which power (1-beta) will be calculated.
alpha	numeric - Type I error probability. Defaults to 0.05
factor_names	character - vector of length 2. Names of the 2 factors to be evaluated. Default is to inherit names from effect_sizes. If effect_sizes has no names and no factor_names are provided, factors will be named 'FactorA' and 'FactorB'.
plot	logical - Should the power curve be plotted. Default is TRUE.
title	character - Title for the graph. Defaults to 'Power curve from exact ANOVA test'
target_power	numeric - Desired power to be attained. Accepts values between 0 and 1, defaults to 0.8.
target_line	logical - Set to TRUE. If FALSE no target line will be drawn. Overrides target_power.
alpha_line	logical - Should a line at the set type I error be plotted

Details

Probably the best way to calculate power for independent balanced designs

Value

A list that contains the number of levels for each factor, the chosen significance level and a data.frame in which the first column is the group sample size and the remaining three columns are the power for the main effect of the first factor, the main effect of the second factor and their interaction, respectively.

Optionally, a graph that displays the power curves.

Examples

refmean <- 1
treatgroups <- 4
timepoints <- 5
treateff <- 1.25
timeeff <- 0.85
cellswithinteraction <- matrix(c(rep(2,3), 3:5), 3,2)
#second level of factor A interacts with 3rd, 4th and 5th level of factor B

factors_levels_names <- list(treatment=letters[1:treatgroups], time=1:timepoints)

effects_treat_time_interact <- calculate_mean_matrix(refmean = refmean,
                                                     nlfA = treatgroups, nlfB = timepoints,
                                                     fAeffect = treateff, fBeffect = timeeff,
                                                     label_list = factors_levels_names,
                                                     groupswinteraction = cellswithinteraction,
                                                     interact=1.3)

fxs <- effsize(effects_treat_time_interact)
exact_twoway_anova_power(a= treatgroups, b=timepoints, effect_sizes=fxs, n=5:20)

extraSuperpower documentation built on Aug. 8, 2025, 6:44 p.m.