mca_pa: Parallel Analysis for MCA Dimensionality Assessment

In alsi: Aggregated Latent Space Index for Multiple Correspondence Analysis

Parallel Analysis for MCA Dimensionality Assessment

Description

Compares observed MCA eigenvalues against reference distributions from permuted data to identify statistically meaningful dimensions.

Usage

mca_pa(
  data,
  vars,
  B = 2000,
  q = 0.95,
  seed = 20260123,
  max_dims = 20,
  verbose = TRUE
)

Arguments

data	Data frame or path to .xlsx file
vars	Character vector of binary variable names
B	Integer, number of permutations (default: 2000)
q	Numeric, reference quantile for retention (default: 0.95)
seed	Integer, random seed for reproducibility
max_dims	Integer, maximum dimensions to display in plot
verbose	Logical, print progress messages

Value

S3 object of class mca_pa containing:

eig_obs	Observed eigenvalues from the MCA of the original data
eig_q	Reference quantiles from permutation distribution
eig_perm	Matrix of permutation eigenvalues (B x dimensions)
K_star	Suggested number of dimensions to retain (where observed > reference)
fit	MCA fit object (class mca_fit) from original data
q	Quantile threshold used for comparison
B	Number of permutations performed

Examples

# Using included ANR2 dataset
data(ANR2)
pa <- mca_pa(ANR2, vars = names(ANR2), B = 100)
print(pa$K_star)

alsi documentation built on Feb. 17, 2026, 5:07 p.m.