Skip to contents

Plot power across sample sizes at the calibrated alpha

Source: R/alphaN_power.R
alphaN_power_plot.Rd

Draws, for each requested effect size, the power of the two-sided single-coefficient test as a function of the sample size, where every calibration method is evaluated at its own alpha level for the given Bayes factor target. A fixed reference level (default 0.05) is drawn as a dashed curve. This is the design-time companion of alphaN_plot(): one figure shows what each calibration costs in power. Colors follow the colorblind-safe Okabe-Ito palette; the effect-size and moment curves use the same log-spaced spline interpolation as alphaN_plot().

Usage

alphaN_power_plot(
  d = c(0.1, 0.5),
  BF = 3,
  max = 10000,
  methods = c("JAB", "min", "robust", "balanced"),
  de = 0.5,
  ref = 0.05
)

Arguments

d

Standardized effect sizes to draw, one panel per element, on the scale of alphaN_power()'s d. Defaults to c(0.1, 0.5).

BF

Target Bayes factor for the calibration. Defaults to 3.

max

The maximum number of sample size. Defaults to 10,000.

methods

Character vector with the methods to draw, any subset of c("JAB", "min", "robust", "balanced", "ES", "moment"). Defaults to the four prior-fraction methods, matching the behavior of earlier package versions.

de

The prespecified (targeted) effect size in standardized units: Cohen's d for q = 1 and Cohen's f for joint tests (the scales coincide at q = 1). Only used by methods "ES" and "moment". Defaults to 0.5, a medium effect; use 0.2 for small and 0.8 for large effects (Cohen, 1988).

ref

A fixed significance level drawn as a dashed reference curve, or NULL to omit it. Defaults to 0.05.

Value

Prints a plot.

See also

alphaN_power(), alphaN_plot()

Examples

# The power cost of evidence calibration for a small and a medium effect
alphaN_power_plot(d = c(0.1, 0.5), BF = 3,
                  methods = c("JAB", "balanced", "moment"), max = 2000)