Build a package-native reference review for report completeness

Build a package-native reference review for report completeness

Usage

reference_case_review(
  fit,
  diagnostics = NULL,
  bias_results = NULL,
  reference_profile = c("core", "compatibility"),
  include_metrics = TRUE,
  top_n_attention = 15L
)

Arguments

fit

Output from fit_mfrm().

diagnostics

Optional output from diagnose_mfrm(). If omitted, diagnostics are computed internally with residual_pca = "none".

bias_results

Optional output from estimate_bias(). If omitted and at least two facets exist, a 2-way interaction screen is computed internally.

reference_profile

Review profile. "core" emphasizes package-native report contracts. "compatibility" exposes the manual-aligned compatibility layer used by facets_output_contract_review(branch = "facets").

include_metrics

If TRUE, run numerical consistency checks in addition to schema coverage checks.

top_n_attention

Number of lowest-coverage components to keep in attention_items.

Value

An object of class mfrm_reference_review.

Details

This function repackages the output-contract review into package-native terminology so users can review output completeness without needing external manual/table numbering. It reports:

• component-level schema coverage

• numerical consistency checks for derived report tables

• the highest-priority attention items for follow-up

It is a package-output completeness review, not an external validation study.

Use reference_profile = "core" for ordinary mfrmr workflows. Use reference_profile = "compatibility" only when you explicitly want to inspect the compatibility layer.

Interpreting output

• overall: one-row review summary with schema coverage and metric pass rate.

• component_summary: per-component coverage summary.

• attention_items: direct list of components needing review.

• metric_summary / metric_checks: numerical consistency status.

See also

facets_output_contract_review(), diagnose_mfrm(), build_fixed_reports()

Examples

if (FALSE) { # \dontrun{
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
review <- reference_case_review(fit, diagnostics = diag)
summary(review)
} # }