precision_audit_report: Build a precision audit report

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

Build a precision audit report

Description

Build a precision audit report

Usage

precision_audit_report(fit, diagnostics = NULL)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().

Details

This helper summarizes how mfrmr derived SE, CI, and reliability values for the current run. It is package-native and is intended to help users distinguish model-based precision paths from exploratory ones without requiring external software conventions.

Value

A named list with:

• profile: one-row precision overview

• checks: package-native precision audit checks

• approximation_notes: detailed method notes

• settings: resolved model and method labels

What this audit means

precision_audit_report() is a reporting gatekeeper for precision claims. It tells you how the package derived uncertainty summaries for the current run and how cautiously those summaries should be written up.

What this audit does not justify

• It does not, by itself, validate the measurement model or substantive conclusions.

• A favorable precision tier does not override convergence, fit, linking, or design problems elsewhere in the analysis.

Interpreting output

• profile: one-row overview of the active precision tier and recommended use.

• checks: package-native audit checks for SE ordering, reliability ordering, coverage of sample/population summaries, and SE source labels.

• approximation_notes: method notes copied from diagnose_mfrm().

Recommended next step

Use the profile$PrecisionTier and checks table to decide whether SE, CI, and reliability language can be phrased as model-based, should be qualified as hybrid, or should remain exploratory in the final report.

Typical workflow

1. Run diagnose_mfrm() for the fitted model.

2. Build precision_audit_report(fit, diagnostics = diag).

3. Use summary() to see whether the run supports model-based reporting language or should remain in exploratory/screening mode.

See Also

diagnose_mfrm(), facet_statistics_report(), reporting_checklist()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
diag <- diagnose_mfrm(fit, residual_pca = "none")
out <- precision_audit_report(fit, diagnostics = diag)
summary(out)

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