brms_inla_power_two_stage: Two-Stage Bayesian Assurance Simulation (Multi-Effect,...

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

Two-Stage Bayesian Assurance Simulation (Multi-Effect, User-Friendly API)

Description

Runs a two-stage Bayesian assurance simulation with formula-based multi-effect grids and adaptive refinement.

Usage

brms_inla_power_two_stage(
  formula,
  effect_name,
  effect_grid,
  n_range,
  stage1_k_n = 8,
  stage1_nsims = 100,
  stage2_nsims = 400,
  refine_metric = c("direction", "threshold", "rope"),
  refine_target = 0.8,
  prob_threshold = 0.95,
  effect_threshold = 0,
  obs_per_group = 10,
  error_sd = 1,
  group_sd = 0.5,
  band = 0.06,
  expand = 1L,
  inla_num_threads = NULL,
  ...
)

Arguments

formula	Model formula.
effect_name	Character vector of fixed effect names; must match formula terms.
effect_grid	Data frame with columns named by effect_name specifying effect values.
n_range	Numeric length-2 vector specifying sample size range.
stage1_k_n	Number of grid points in stage 1.
stage1_nsims	Number of simulations per cell in stage 1.
stage2_nsims	Number of simulations per cell in stage 2.
refine_metric	Metric used for refinement; one of "direction", "threshold", or "rope".
refine_target	Target assurance for refined cells.
prob_threshold	Posterior probability threshold for decision.
effect_threshold	Effect-size threshold for decision metric.
obs_per_group	Number of observations per group for grouping factors.
error_sd	Residual standard deviation.
group_sd	Standard deviation of random effects.
band	Numeric width of the target refinement band.
expand	Integer; how much to expand the refinement grid around candidates.
inla_num_threads	Character string specifying INLA threading (e.g., "4:1"). If NULL (default), automatically detects optimal setting based on CPU cores.
...	Additional arguments passed to internal functions.

Value

A list with combined simulation results, summary, and stage parameters.

Examples

## Not run: 
# Two-stage design with threading
effect_grid <- expand.grid(
  treatment = c(0.2, 0.5, 0.8),
  covariate = c(0.1, 0.3)
)
results <- brms_inla_power_two_stage(
  formula = outcome ~ treatment + covariate,
  effect_name = c("treatment", "covariate"),
  effect_grid = effect_grid,
  n_range = c(50, 200),
  stage1_nsims = 3,
  stage2_nsims = 3,
   error_sd = 1 
)
print(results$summary)

## End(Not run)

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