Control parameters for BSDA sample-size planning

Lists the sampler, model-fit, and search-algorithm settings for design(method = "BSDA") and its validation. These are the deeper knobs a user rarely changes; the planning targets (measure, measure_value, target_pow) stay in design's own arguments.

Usage

bsda_control(
  gwish_sampler = "direct",
  gwish_tol = 1e-08,
  gwish_iter = 500L,
  gwish_burnin = 500L,
  edge_threshold = 0.5,
  fit_iterations = 10000L,
  fit_burnin = 5000L,
  alpha = 0.05,
  n_scout = 6L,
  n_main = 10L,
  scout_frac = 1/3,
  max_iter = 10L,
  n_boot = 5000L,
  eps = 0.001,
  tol_frac = 0.01,
  verbose = FALSE,
  seed = NULL
)

Arguments

gwish_sampler

G-Wishart sampler, "direct" or "block".

gwish_tol, gwish_iter, gwish_burnin

Tolerance, iterations, and burn-in for the G-Wishart sampler.

edge_threshold

Posterior inclusion probability above which an edge is selected. Default 0.5.

fit_iterations, fit_burnin

MCMC length and burn-in for fitting each simulated study.

alpha

Significance level used in the fit. Default 0.05.

n_scout, n_main

Grid sizes for the scout and main search passes.

scout_frac

Fraction of fit_iterations used in the scout pass.

max_iter

Maximum refine-evaluate-invert iterations.

n_boot

Bootstrap resamples for the probit-inversion CI.

eps

Numerical tolerance guarding the probit inversion.

tol_frac

Tolerance fraction of the relative sample size difference used to stop the search. Default 0.01, matched to typical H = 50. Lower it if H raises substantially.

verbose

Stream progress from the C++ routine. Default FALSE.

seed

Random seed for reproducibility. Default NULL (no seed).

Value

A named list of control settings.

See also

Other sample size planning: design(), design.ggm_elicited(), validate(), validate.ggm_design()