summary.mfrm_bundle: Summarize report/table bundles in a user-friendly format

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Summarize report/table bundles in a user-friendly format

Description

Summarize report/table bundles in a user-friendly format

Usage

## S3 method for class 'mfrm_bundle'
summary(object, digits = 3, top_n = 10, ...)

Arguments

object	Any report bundle produced by mfrmr table/report helpers.
digits	Number of digits for printed numeric values.
top_n	Number of preview rows shown from the main table component.
...	Reserved for generic compatibility.

Details

This method provides a compact summary for bundle-like outputs (for example: unexpected-response, fair-average, chi-square, and category report objects). It extracts:

• object class and available components

• one-row summary table when available

• preview rows from the main data component

• resolved settings/options

Branch-aware summaries are provided for:

• mfrm_bias_count (branch = "original" / "facets")

• mfrm_fixed_reports (branch = "original" / "facets")

• mfrm_visual_summaries (branch = "original" / "facets")

Additional class-aware summaries are provided for:

• mfrm_unexpected, mfrm_fair_average, mfrm_displacement

• mfrm_interrater, mfrm_facets_chisq, mfrm_bias_interaction

• mfrm_rating_scale, mfrm_category_structure, mfrm_category_curves

• mfrm_measurable, mfrm_unexpected_after_bias, mfrm_output_bundle

• mfrm_residual_pca, mfrm_specifications, mfrm_data_quality

• mfrm_iteration_report, mfrm_subset_connectivity, mfrm_facet_statistics

• mfrm_parity_report

Value

An object of class summary.mfrm_bundle.

Interpreting output

• overview: class, component count, and selected preview component.

• summary: one-row aggregate block when supplied by the bundle.

• preview: first top_n rows from the main table-like component.

• settings: resolved option values if available.

Typical workflow

1. Generate a bundle table/report helper output.

2. Run summary(bundle) for compact QA.

3. Drill into specific components via $ and visualize with plot(bundle, ...).

See Also

unexpected_response_table(), fair_average_table(), plot()

Examples

toy_full <- load_mfrmr_data("example_core")
toy_people <- unique(toy_full$Person)[1:12]
toy <- toy_full[toy_full$Person %in% toy_people, , drop = FALSE]
fit <- suppressWarnings(
  fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 10)
)
t4 <- unexpected_response_table(fit, abs_z_min = 1.5, prob_max = 0.4, top_n = 5)
summary(t4)
diag <- diagnose_mfrm(fit, residual_pca = "none")
bias <- estimate_bias(fit, diag, facet_a = "Rater", facet_b = "Criterion", max_iter = 2)
t11 <- bias_count_table(bias, branch = "facets")
summary(t11)

mfrmr documentation built on March 31, 2026, 1:06 a.m.