powerbrmsINLA (version 1.3.0)

brms_inla_power_sequential: Sequential Bayesian Assurance Simulation Engine (Modern, Multi-Effect Ready)

Description

Simulates assurance sequentially in batches, stopping early per cell based on Wilson confidence intervals.

Usage

brms_inla_power_sequential(
  formula,
  family = gaussian(),
  family_control = NULL,
  Ntrials = NULL,
  E = NULL,
  scale = NULL,
  priors = NULL,
  data_generator = NULL,
  effect_name,
  effect_grid,
  sample_sizes,
  metric = c("direction", "threshold", "rope", "bf"),
  target = 0.8,
  prob_threshold = 0.95,
  effect_threshold = 0,
  rope_bounds = NULL,
  credible_level = 0.95,
  compute_bayes_factor = FALSE,
  error_sd = 1,
  group_sd = 0.5,
  obs_per_group = 10,
  predictor_means = NULL,
  predictor_sds = NULL,
  seed = 123,
  batch_size = 20,
  min_sims = 40,
  max_sims = 600,
  ci_conf = 0.95,
  margin = 0.02,
  inla_num_threads = NULL,
  family_args = list(),
  progress = TRUE
)

Value

A list of class "brms_inla_power" with a per-cell summary (including the conditional_power column, the Monte Carlo estimate at each fixed effect value) and simulation settings. Note this is conditional power at each grid value, not unconditional assurance; pass the result to compute_assurance() for the latter.

Arguments

formula

brms-style model formula.

family

GLM family (e.g., gaussian(), binomial()).

family_control

Optional list for INLA's control.family.

Ntrials

Optional vector of binomial trial counts (for binomial families).

E

Optional vector of exposures (for Poisson families).

scale

Optional numeric vector for scale parameter in INLA.

priors

brms prior specification object. Supported priors are translated to INLA controls where possible and audited in settings$prior_translation.

data_generator

Optional function(n, effect) to simulate data.

effect_name

Character vector of fixed effects to assess.

effect_grid

Data frame or vector of effect values.

sample_sizes

Vector of sample sizes.

metric

Character; one of "direction", "threshold", "rope", or "bf" for Bayesian decision metric.

target

Target conditional power for the stopping rule (0-1).

prob_threshold

Posterior probability threshold for decision metrics.

effect_threshold

Effect-size threshold.

rope_bounds

Numeric length-2 vector defining ROPE.

credible_level

Credible interval level for Bayesian inference.

compute_bayes_factor

Logical; TRUE if metric is "bf".

error_sd

Residual standard deviation.

group_sd

Standard deviation of random effects.

obs_per_group

Number of observations per group.

predictor_means

Optional named list of predictor means.

predictor_sds

Optional named list of predictor standard deviations.

seed

Random seed.

batch_size

Number of simulations per sequential look.

min_sims

Minimum simulations before early stopping.

max_sims

Maximum simulations per cell.

ci_conf

Confidence level for Wilson confidence intervals.

margin

Margin around target for early stopping decision.

inla_num_threads

Character string specifying INLA threading (e.g., "4:1"). If NULL (default), automatically detects optimal setting based on CPU cores.

family_args

List of family-specific args passed to data generator.

progress

Logical; if TRUE, show progress messages.

Details

Sequential Bayesian Assurance Simulation Engine (Modern, Multi-Effect Ready)

Simulates assurance sequentially in batches, stopping early per cell based on Wilson confidence intervals.

Examples

if (FALSE) {
# Sequential design with automatic threading
results <- brms_inla_power_sequential(
  formula = outcome ~ treatment,
  effect_name = "treatment",
  effect_grid = c(0.2, 0.5, 0.8),
  sample_sizes = c(50, 100, 200),
  metric = "direction",
  target = 0.80
)
print(results$summary)
}