Bootstrap confidence intervals for layer-specific standardized effects

Constructs nonparametric confidence intervals for Hedges' g using the matched, group-stratified subject bootstrap draws generated by `bootstrap_effect_covariance()`. Percentile and basic intervals reuse those bootstrap draws. BCa intervals additionally estimate the acceleration term from leave-one-subject-out jackknife effects within each omic.

Usage

bootstrap_effect_intervals(
  effects,
  bootstrap,
  method = c("percentile", "basic", "bca"),
  conf_level = 0.95,
  min_boot = 100L,
  data = NULL,
  group = NULL,
  reference = NULL,
  comparison = NULL,
  min_n = 3L
)

Arguments

effects

Output of `estimate_effects()` after any scientifically justified orientation has been applied.

bootstrap

An `omics_braid_covariance` object containing `boot_effects`.

method

One of `"percentile"`, `"basic"`, or `"bca"`.

conf_level

Confidence level.

min_boot

Minimum number of finite bootstrap draws required per entity-by-omic effect.

data

Required only for `method = "bca"`; an `omics_braid_data` object used to calculate leave-one-subject-out jackknife effects.

group

Metadata group column, required for BCa.

reference

Reference-group label, required for BCa.

comparison

Comparison-group label, required for BCa.

min_n

Minimum observations per group used for jackknife effects.

Value

A data frame with one row per usable entity-by-omic effect and bootstrap interval diagnostics.

Details

These intervals are an uncertainty-reporting option. They do not replace the analytic standard errors or p-values used by the current OmicsBraid hypothesis tests unless a future method version explicitly validates such a change.