itemrest_bootstrap: Bootstrap the Entire Threshold-Driven Candidate Search

In ItemRest: Automated Item Removal Strategies for Exploratory Factor Analysis

Bootstrap the Entire Threshold-Driven Candidate Search

Description

Resamples observations and reruns discovery for each replicate, holding the original baseline factor count, scoring keys, and screening criteria fixed. Each replicate computes its own correlation matrix once and reuses its submatrices throughout that replicate's removal search. This measures search stability rather than merely refitting a selected set. It does not replace independent validation or estimate probabilities of validity.

Usage

itemrest_bootstrap(
  x,
  data,
  n_boot = 100L,
  seed = NULL,
  progress = interactive()
)

Arguments

x	An itemrest_result defining search settings.
data	Original numeric discovery data, including every discovery item.
n_boot	Number of replicates, default 100.
seed	Integer seed; NULL uses the local date in DDMMYYYY format.
progress	Show a text progress bar; default interactive().

Value

A list with item_stability, solution_stability, replicates, settings, and seed. Any/all-candidate frequencies divide by complete successful searches; searches with no candidates count as zero. Mean candidate retention divides only by complete replicates with candidates and weights replicates equally. Failed/incomplete searches are excluded from frequency denominators and reported separately. Solution frequencies use exact retained sets, not ranks. Additional *_All_Attempts columns divide by n_boot, conservatively treating failed and incomplete searches as having no candidate solutions. This is a sensitivity summary, not an estimate of what those searches would yield.

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