pwranova (version 1.0.3)

pwrcontrast: Power Analysis for Planned Contrast in Between- or Within-Factor ANOVA

Description

Computes power, required total sample size, alpha, or minimal detectable effect size for a single planned contrast (1 df) in between-participants or paired/repeated-measures settings.

Usage

pwrcontrast(
  weight = NULL,
  paired = FALSE,
  n_total = NULL,
  cohensf = NULL,
  peta2 = NULL,
  alpha = NULL,
  power = NULL,
  nlim = c(2, 10000)
)

Value

A one-row data frame with class:

• "cal_power" when power is calculated given n_total, alpha, and effect size;

• "cal_n" when n_total is solved;

• "cal_alpha" when alpha is solved;

• "cal_es" when minimal detectable effect sizes are solved.

Columns: term (always "contrast"), weight (comma-separated string), df_num, df_denom, n_total, alpha, power, cohensf, peta2, F_critical, ncp.

Arguments

weight

Numeric vector (length \(K \ge 2\)). Contrast weights whose sum must be zero.

paired

Logical. FALSE for between-subjects (default), TRUE for paired/repeated-measures.

n_total

Integer or integer vector. Total sample size(s). If NULL, the function solves for n_total.

cohensf

Numeric (non-negative). Cohen's \(f\). If NULL, it is derived from peta2 when available.

peta2

Numeric in \((0,1)\). Partial eta squared. If NULL, it is derived from cohensf when available.

alpha

Numeric in \((0,1)\). If NULL, it is solved for.

power

Numeric in \((0,1)\). If NULL, it is computed; if n_total is NULL, n_total is solved to achieve this power.

nlim

Integer length-2. Search range of total n when solving sample size.

Details

For a contrast with weights \(w_1, \dots, w_K\) that sum to zero, the numerator df is 1. The denominator df is \(n - K\) for between-subjects (unpaired) designs and \(n - 1\) for paired/repeated-measures designs. Power uses the noncentral F-with \(\lambda = f^2 \cdot n_{\mathrm{total}}\).

• Contrast weights (weight) are not centered internally; only the zero-sum condition is enforced (up to numerical tolerance).

• When paired = FALSE, the total sample size n_total must be a multiple of the number of contrast groups \(K\).

• Exactly one of n_total, an effect-size specification (cohensf/peta2), alpha, or power must be NULL; that quantity is then solved.

• Critical values are computed from the central F-distribution; power is based on the noncentral F-distribution with noncentrality parameter \(\lambda = f^2 \cdot n_{\mathrm{total}}\).

• Effect-size inputs can be given as Cohen’s \(f\) or partial eta-squared \(\eta_p^2\) (internally converted via \(f = \sqrt{\eta_p^2/(1-\eta_p^2)}\)). If both are NULL, the minimal detectable effect size is solved for given n_total, alpha, and power.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Examples

# Two-group contrast (1, -1), between-subjects: compute power
pwrcontrast(weight = c(1, -1), paired = FALSE,
            n_total = 40, cohensf = 0.25, alpha = 0.05)

# Four-level contrast (e.g., Helmert-like), solve required N for target power
pwrcontrast(weight = c(3, -1, -1, -1), paired = FALSE,
            n_total = NULL, peta2 = 0.06, alpha = 0.05, power = 0.80)

# Paired contrast across K=3 conditions
pwrcontrast(weight = c(1, 0, -1), paired = TRUE,
            n_total = NULL, cohensf = 0.2, alpha = 0.05, power = 0.9)