plot_assurance_with_robustness: Plot Conditional Power with Robustness Ribbon (Multi-Effect...

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

Plot Conditional Power with Robustness Ribbon (Multi-Effect Grid Friendly)

Description

Compares conditional Bayesian power results from multiple scenarios by showing the range ("ribbon") of values across scenarios for each sample size and effect grid variable.

Usage

plot_assurance_with_robustness(
  power_results_list,
  metric = c("precision", "direction", "threshold", "bf"),
  x_effect = NULL,
  facet_by = NULL,
  precision_target = NULL,
  p_star = 0.95,
  bf_threshold = 10,
  effect_filters = NULL,
  effect_weights = NULL,
  show_individual_scenarios = FALSE,
  title = NULL,
  subtitle = NULL
)

Arguments

power_results_list	Named list of results objects from brms_inla_power or sequential/two-stage variants.
metric	Which conditional power metric to compute: "precision", "direction", "threshold", or "bf".
x_effect	Name of effect grid column for x-axis (default: first detected grid column).
facet_by	Optional effect grid column(s) to facet by.
precision_target	CI width target if metric="precision".
p_star	Posterior probability threshold for "direction"/"threshold".
bf_threshold	BF10 threshold for "bf".
effect_filters	Optional named list for filtering rows (e.g. list(treatment=0)).
effect_weights	Optional named numeric vector for averaging over grid values.
show_individual_scenarios	Logical; if TRUE, overlay each scenario's curve.
title, subtitle	Optional plot labels.

Value

A ggplot object.

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