plot_power_contour: Draw a filled contour plot of conditional Bayesian power for...

In powerbrmsINLA: Bayesian Power Analysis Using 'brms' and 'INLA'

Draw a filled contour plot of conditional Bayesian power for a chosen metric, as a function of two effect grid columns and sample size.

Description

Plots the conditional Bayesian power — the probability of meeting the decision criterion at each fixed effect size and sample size — as a filled contour surface.

Usage

plot_power_contour(
  power_results,
  power_metric = c("direction", "threshold", "rope"),
  x_effect = NULL,
  y_effect = "n",
  facet_by = NULL,
  power_threshold = 0.8,
  show_threshold_line = TRUE,
  title = NULL,
  subtitle = NULL
)

Arguments

power_results	Output from a brms_inla_power function.
power_metric	Which metric to plot: "direction", "threshold", or "rope".
x_effect	Name of effect grid column for x-axis (default = first effect).
y_effect	Name of effect grid column for y-axis (default = "n").
facet_by	Optional effect grid column(s) to facet by.
power_threshold	Optional reference contour line for conditional power (default 0.8).
show_threshold_line	Logical; add a red contour at power_threshold.
title, subtitle	Optional plot labels.

Details

These plots display conditional Bayesian power — the probability of meeting the decision criterion at a fixed effect size. For unconditional assurance (averaged over a design prior on effect size), see plot_assurance_curve().

Value

A ggplot object.

powerbrmsINLA documentation built on July 2, 2026, 5:07 p.m.