Iteratively run BootEGA to ensure structural stability of items

Usage

iterative_stability_check(
  embedding_matrix,
  items,
  cut.off = 0.75,
  model = "NULL",
  algorithm = "",
  uni.method,
  corr = "auto",
  ncores = NULL,
  boot.iter = 500,
  EGA.type = "EGA.fit",
  silently
)

Arguments

embedding_matrix: Numeric matrix of item embeddings (columns = items).
items: Data frame containing at least ID and statement.
cut.off: Numeric. Minimum stability required to retain an item.
model: Network estimation model (e.g., "glasso", "TMFG").
algorithm: Community detection algorithm.
uni.method: Unidimensionality method.
corr: Character. Correlation method. Default "auto" uses EGAnet's automatic detection.
ncores: Numeric. Number of cores for parallel processing. Default NULL uses EGAnet default.
boot.iter: Numeric. Number of bootstrap iterations. Default 500.
EGA.type: Type of EGA (default "EGA.fit").
silently: Logical. Suppress output.

Value

A list containing the final embedding, initial/final bootEGA objects, and an items_removed data frame. For each removed item, the table retains the bootstrap run, empirical item stability, cutoff, stability deficit, and removal reason. Zero-removal runs return an empty data frame, not NULL.