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Summary and Print Methods for BEFA Objects

Source: R/summary_befa.R
summary.befa.Rd

Methods to summarize and display results from Bayesian Exploratory Factor Analysis.

Computes posterior summaries for a fitted Bayesian EFA model, including point estimates, credible intervals, and convergence diagnostics for factor loadings.

Usage

# S3 method for class 'befa'
summary(
  object,
  probs = c(0.025, 0.975),
  cutoff = 0,
  sort = FALSE,
  signif_stars = FALSE,
  ...
)

Arguments

object

A befa object returned by befa().

probs

Numeric vector of length 2. Quantiles for credible intervals (default: 0.025, 0.975).

cutoff

Numeric. Loadings with absolute value below this threshold are hidden in print output.

sort

Logical. If TRUE, items are sorted by their primary factor loading.

signif_stars

Logical. If TRUE, marks loadings whose CI excludes zero with an asterisk.

...

Ignored.

Value

A summary.befa object containing:

  • estimates: Data frame with loadings, h2, u2, and diagnostics per item.

  • sig_matrix: Logical matrix indicating which loadings have CIs excluding zero.

  • probs: The quantile probabilities used for credible intervals.

  • phi: Factor correlation matrix (identity for orthogonal rotations).

  • variance: List with per-factor and total explained variance proportions.

  • fit_indices: Fit measures from the befa object (if available).

  • reliability: Reliability estimates from the befa object (if available).

  • header: List with model metadata (model_type, n_factors, N, rotation).

  • print_options: List of display options (cutoff, sort, signif_stars).

  • tables: Pre-built display tables for loadings, fit measures, and reliability.

Details

The summary extracts posterior draws for loadings (Lambda), computes means and quantiles, and reports MCMC diagnostics (Rhat, ESS). Communalities (h2) and uniquenesses (u2) are derived from the squared loadings.

Examples

if (FALSE) { # \dontrun{
befa_fit <- befa(
  data = HS_data, n_factors = 3, model = "cor",
  iter = 500, chains = 2, seed = 123
)

# Get and print the summary
befa_summary <- summary(befa_fit, sort = TRUE, signif_stars = TRUE)
print(befa_summary)
} # }