plot_assurance_curve: Plot Assurance Curve(s) vs Sample Size

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

Plot Assurance Curve(s) vs Sample Size

Description

Plots unconditional Bayesian assurance against sample size for one or more design priors, with an optional horizontal reference line at a target assurance level. Each prior produces one line, making it easy to compare how the choice of design prior changes the required sample size.

Usage

plot_assurance_curve(
  assurance_results,
  labels = NULL,
  target = 0.8,
  title = NULL,
  subtitle = NULL
)

Arguments

assurance_results	A single powerbrmsINLA_assurance object (from compute_assurance()) or a list of such objects.
labels	Character vector naming each design prior, e.g. c("Sceptical", "Primary", "Optimistic"). Defaults to "Design prior" for a single object or "Prior 1", "Prior 2", … for multiple objects.
target	Numeric in (0, 1): horizontal reference line added at this assurance level (default 0.80). Set to NULL to suppress the line.
title, subtitle	Optional plot title and subtitle strings.

Value

A ggplot object.

Examples

syn_summary <- data.frame(
  n               = rep(c(50, 100, 200), each = 3),
  treatment       = rep(c(0.2, 0.5, 0.8), 3),
  power_direction = c(0.40, 0.65, 0.85, 0.60, 0.82, 0.95, 0.72, 0.90, 0.98)
)
pr <- list(summary = syn_summary, settings = list(effect_name = "treatment"))
a1 <- compute_assurance(pr, list(dist = "normal", mean = 0.5, sd = 0.15))
a2 <- compute_assurance(pr, list(dist = "normal", mean = 0.5, sd = 0.30))
plot_assurance_curve(list(a1, a2), labels = c("Informative", "Diffuse"))

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