plot_decision_assurance_curve: Plot Conditional Power Curve for a Decision Rule...

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

Plot Conditional Power Curve for a Decision Rule (Multi-Effect Grid Friendly)

Description

Plots the conditional Bayesian power (proportion of simulation runs meeting a posterior probability decision rule) versus an effect grid variable, for a given metric ("direction", "threshold", or "rope") at a fixed decision probability threshold p_star.

Usage

plot_decision_assurance_curve(
  power_results,
  metric = c("direction", "threshold", "rope"),
  p_star = 0.95,
  x_effect = NULL,
  facet_by = NULL,
  effect_filters = NULL,
  effect_weights = NULL,
  title = NULL,
  subtitle = NULL
)

Arguments

power_results	A list returned by ⁠brms_inla_power*⁠.
metric	Decision metric: "direction", "threshold", or "rope".
p_star	Numeric decision threshold in (0,1).
x_effect	Name of effect grid column for x-axis (default: first grid column).
facet_by	Optional effect grid column(s) to facet by.
effect_filters	Optional named list for filtering rows, e.g. list(treatment=0).
effect_weights	Optional named numeric vector of weights for selected x_effect values.
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.