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Build a precision review report

Source: R/api-reports.R
precision_review_report.Rd

Build a precision review report

Usage

precision_review_report(fit, diagnostics = NULL)

Arguments

fit

Output from fit_mfrm().

diagnostics

Optional output from diagnose_mfrm().

Value

A named list with:

  • profile: one-row precision overview

  • checks: package-native precision review checks

  • fit_separation_basis: source-grounded fit/separation reporting boundary

  • approximation_notes: detailed method notes

  • settings: resolved model and method labels

Details

This helper summarizes how mfrmr derived SE, CI, and reliability values for the current run. It also includes a source-grounded fit/separation basis table so users can keep mean-square fit, ZSTD standardization, Rasch/FACETS-style separation, and package QC thresholds in separate reporting lanes.

What this review means

precision_review_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 review 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.

  • Fit and separation rows in this report are reporting/validation boundaries, not standalone success criteria.

Interpreting output

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

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

  • fit_separation_basis: source-grounded boundary table for fit and separation reporting.

  • approximation_notes: method notes copied from diagnose_mfrm().

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_review_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

if (FALSE) { # interactive()
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")
out <- precision_review_report(fit, diagnostics = diag)
summary(out)
}