Creates a plot of alpha as function of sample size for the chosen methods

Draws alpha as a decreasing function of the sample size for any selection of the calibration methods offered by alphaN(). The prior-fraction curves ("JAB", "min", "robust", "balanced") are evaluated exactly at every sample size; the "ES" and "moment" curves are evaluated at twelve log-spaced sample sizes and interpolated by a spline on the log-log scale, which keeps the plot fast (expect roughly a second of computation per Klauer-type curve). Colors follow the colorblind-safe Okabe-Ito palette.

Usage

alphaN_plot(
  BF = 1,
  max = 10000,
  ylim = NULL,
  methods = c("JAB", "min", "robust", "balanced"),
  de = 0.5,
  log = ""
)

Arguments

BF

Bayes factor you would like to match. 1 to avoid Lindley's Paradox, 3 to achieve moderate evidence and 10 to achieve strong evidence.

max

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

ylim

Limits for the y-axis. The default, NULL, covers all requested curves. Set to e.g. c(0, 0.05) to zoom in on small alpha levels.

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).

log

Passed to plot(): "" (default) for linear axes, "x", "y", or "xy" for logarithmic ones. Logarithmic axes are useful when the "moment" curve is included, since it falls much faster than the others.

Value

Prints a plot.

Examples

# Plot of alpha level as a function of n for a Bayes factor of 3
alphaN_plot(BF = 3)


# Compare JAB with the effect-size and moment calibrations
alphaN_plot(BF = 3, methods = c("JAB", "ES", "moment"), log = "xy")