bootStrap: Bootstrap procedure for variance estimation

In WMAP: Weighted Meta-Analysis with Pseudo-Populations

Bootstrap procedure for variance estimation

Description

Bootstrap procedure for variance estimation

Usage

bootStrap(
  data,
  moments.ar,
  moreFeatures.v,
  B,
  method,
  method_wt.v,
  naturalGroupProp,
  num.random.b,
  gammaMin,
  gammaMax,
  seed,
  verbose,
  parallel = FALSE,
  n_cores = NULL,
  outcome_model = FALSE,
  crossfit_folds = NULL,
  nodesize = NULL
)

Arguments

data	Structured data list
moments.ar	Point estimate moments array
moreFeatures.v	Point estimate mean differences
B	Number of bootstrap samples
method	Weighting method ("IC", "IGO", or "FLEXOR")
method_wt.v	Original weights
naturalGroupProp	FLEXOR theta parameter
num.random.b	Number of random starts for FLEXOR in bootstrap
gammaMin	FLEXOR gamma lower bound
gammaMax	FLEXOR gamma uppper bound
seed	Random seed
verbose	Logical; if TRUE, display progress
parallel	Logical; if TRUE, use parallel processing (default: FALSE)
n_cores	Number of cores to use for parallel processing (default: availableCores() - 1)
outcome_model	Logical; if TRUE, uses RF-predicted Y in place of observed Y. Default FALSE.
crossfit_folds	Integer >= 2 enabling K-fold cross-fitting for the outcome model, or NULL to fit on the full bootstrap sample. Default is NULL.
nodesize	Integer nodesize for the propensity score random forests; reuses the value selected on the original data to skip re-tuning in each bootstrap iteration.

Value

List with three elements:

• collatedMoments.ar - Array of bootstrap moments

• collatedMoreFeatures.mt - Matrix of bootstrap mean differences

• collatedESS - Vector of bootstrap ESS values

WMAP documentation built on Oct. 6, 2026, 1:07 a.m.