plot_precision_assurance_curve: Plot Precision Conditional Power Curve (Multi-Effect Grid...

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

Plot Precision Conditional Power Curve (Multi-Effect Grid Friendly)

Description

Plots the conditional power for precision (proportion of runs where CI width <= target) vs. a chosen effect grid variable across sample size(s). Supports faceting, effect filtering, and weights.

Usage

plot_precision_assurance_curve(
  power_results,
  precision_target,
  x_effect = NULL,
  facet_by = NULL,
  effect_filters = NULL,
  effect_weights = NULL,
  title = NULL,
  subtitle = NULL
)

Arguments

power_results	List returned by ⁠brms_inla_power*⁠.
precision_target	Numeric; credible interval width threshold for success.
x_effect	Name of effect grid column for x-axis (default: first grid column).
facet_by	Optional effect grid column(s) for faceting.
effect_filters	Optional named list for filtering rows, e.g. list(treatment=0).
effect_weights	Optional named numeric vector for weights over selected x_effect values.
title, subtitle	Optional plot labels.

Details

These plots display conditional Bayesian power — the probability of achieving the precision 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.