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Search for the sample size needed for target ANOVA power

Source: R/power_curve.R
power_n.Rd

Adaptive simulation search for the per-between-cell sample size needed to reach a requested power for a balanced factorial ANOVA design. The search doubles upward from n_start until it brackets the target or reaches n_max, then bisects the bracket.

Usage

power_n(
  between = NULL,
  within = NULL,
  term,
  target_pes,
  power = 0.8,
  n_sims = 10000,
  alpha = 0.05,
  ss_type = "III",
  n_start = NULL,
  n_max = 1000,
  tol = 0.01,
  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.

power

Desired target power.

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.

n_start

Starting sample size per between-subject cell. If NULL, starts at the smallest value that can support empirical calibration for the requested design.

n_max

Maximum sample size per between-subject cell.

tol

Stop when estimated power is within tol of power.

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 with n_needed and total_n_needed.

Examples

power_n(
  between = c(cond = 2),
  within = c(stim = 4),
  term = "cond:stim",
  target_pes = 0.14,
  alpha = 0.05,
  power = 0.80,
  n_sims = 1000, # use 5000+ for a more precise estimate
  seed = 123 # for reproducibility
)