plot_power_heatmap: Plot Conditional Bayesian Power Heatmap (Multi-Effect Grid...

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

Plot Conditional Bayesian Power Heatmap (Multi-Effect Grid Friendly)

Description

Displays the conditional Bayesian power — the probability of meeting the decision criterion at each fixed effect size — as a colour-filled heatmap.

Usage

plot_power_heatmap(
  power_results,
  power_metric = c("direction", "threshold", "rope"),
  x_effect = NULL,
  y_effect = "n",
  facet_by = NULL,
  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.
title, subtitle	Optional plot labels.

Details

Heatmap of conditional Bayesian power for a chosen metric across two selected effect grid variables and sample sizes.

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.