reference_case_benchmark: Benchmark packaged reference cases

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

Benchmark packaged reference cases

Description

Benchmark packaged reference cases

Usage

reference_case_benchmark(
  cases = c("synthetic_truth", "synthetic_bias_contract", "study1_itercal_pair",
    "study2_itercal_pair", "combined_itercal_pair"),
  method = "MML",
  model = "RSM",
  quad_points = 7,
  maxit = 40,
  reltol = 1e-06
)

Arguments

cases	Reference cases to run. Defaults to all package-native benchmark cases.
method	Estimation method passed to fit_mfrm(). Defaults to "MML".
model	Model family passed to fit_mfrm(). Defaults to "RSM".
quad_points	Quadrature points for method = "MML".
maxit	Maximum optimizer iterations passed to fit_mfrm().
reltol	Convergence tolerance passed to fit_mfrm().

Details

This function audits mfrmr against the package's curated internal benchmark cases in three ways:

• synthetic_truth: checks whether recovered facet measures align with the known generating values from the package's internal synthetic design.

• synthetic_bias_contract: checks whether package-native bias tables and pairwise local comparisons satisfy the identities documented in the bias help workflow.

• ⁠*_itercal_pair⁠: compares a baseline packaged dataset with its iterative recalibration counterpart to review fit stability, facet-measure alignment, and linking coverage together.

The resulting object is intended as an internal benchmark harness for package QA and regression auditing. It does not by itself establish external validity against FACETS, ConQuest, or published calibration studies, and it does not assume any familiarity with external table numbering or printer layouts.

Value

An object of class mfrm_reference_benchmark.

Interpreting output

• overview: one-row internal-benchmark summary.

• case_summary: pass/warn/fail triage by reference case.

• fit_runs: fitted-run metadata (fit, precision tier, convergence).

• design_checks: exact design recovery checks for each dataset.

• recovery_checks: known-truth recovery metrics for the internal synthetic case.

• bias_checks: source-backed bias/local-measure identity checks.

• pair_checks: paired-dataset stability screens for the iterated cases.

• linking_checks: common-element audits for paired calibration datasets.

• source_profile: source-backed rules that define the internal benchmark contract.

Examples

bench <- reference_case_benchmark(
  cases = "synthetic_truth",
  method = "JML",
  maxit = 30
)
summary(bench)

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