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Simulate ANOVA power from a balanced factorial design

Source: R/power_curve.R
power_curve.Rd

Simulation-based power estimation for balanced factorial designs under sphericity. Users specify the between- and within-subject factors, the ANOVA term to test, a target partial eta squared, and explicit sample sizes. The function creates a default contrast pattern for the target term, scales it to the requested partial eta squared, simulates datasets, refits stats::aov(), and estimates power by counting p < alpha.

Usage

power_curve(
  between = NULL,
  within = NULL,
  term,
  target_pes,
  n_range,
  n_sims = 10000,
  alpha = 0.05,
  ss_type = "III",
  gpower = FALSE,
  progress = interactive(),
  parallel = FALSE,
  cores = NULL,
  seed = NULL
)

Arguments

between

Named integer vector of between-subject factor level counts, e.g. c(group = 2). Use NULL for no between-subject factors.

within

Named integer vector of within-subject factor level counts, e.g. c(time = 3, condition = 4). Use NULL for no within-subject factors.

term

Character scalar naming the ANOVA term to test, e.g. "group:time". Interaction terms are order-insensitive; "time:group" resolves to "group:time" when that is the design's factor order.

target_pes

Target partial eta squared for term.

n_range

Integer vector of sample sizes per between-subject cell. For pure within-subject designs, this is the total sample size.

n_sims

Number of simulated datasets per sample size.

alpha

Significance threshold.

ss_type

Sums-of-squares type for the tested ANOVA term. "III" is the default for order-invariant tests in unbalanced designs. Use "I" to reproduce sequential stats::aov() tests.

gpower

Logical; if TRUE, calibrate means to the G*Power-style noncentrality convention lambda = total_n * f^2. The default FALSE calibrates the empirical reference dataset to target_pes, equivalent to lambda = den_df * f^2 for the fitted ANOVA.

progress

Logical; if TRUE, show a text progress bar.

parallel

Logical; if TRUE, run simulations for each sample size via the future ecosystem.

cores

Optional positive integer number of cores to use when parallel = TRUE. If NULL, uses one fewer than the number of available cores, with a minimum of one.

seed

Optional integer seed for reproducibility.

Value

An anovapowersim_curve object. The $results tibble contains n_per_cell, total_n, n_sims, numerator and denominator degrees of freedom (num_df, den_df), the noncentrality parameter (ncp), calculated power (power_calc), and simulated power (power_sim).

Examples

power_curve(
  between = c(cond = 2),
  within = c(stim = 2),
  term = "cond:stim",
  target_pes = 0.14,
  n_range = c(16, 20, 23, 28), # n per between-subject cell
  n_sims = 1000,
  seed = 123
)

power_curve(
  between = c(group = 2),
  within = c(time = 2),
  term = "group:time",
  target_pes = 0.14,
  n_range = c(12, 16, 20),
  n_sims = 5000,
  parallel = TRUE,
  cores = 4,
  seed = 123
)