Skip to contents

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 matching output from diagnose_mfrm(). Recompute it after refitting; mismatched or outdated readiness records are rejected.

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 distinct reporting categories.

What this review means

precision_review_report() is a structured prerequisite review 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.

Regularized or fallback SEs remain diagnostic only. Numerical convergence does not establish inferential support. Older reports without the recorded regularization distinction must be recreated with precision_review_report(fit); the existing fit can be reused without refitting.

See also

diagnose_mfrm(), facet_statistics_report(), reporting_checklist()

Examples

library(mfrmr)

# Saved synthetic MML/RSM analysis of example_operational.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "operational-mml.rds", package = "mfrmr"))
fit <- example_fit$fit

# Review support for standard errors, intervals, and reliability summaries
diagnostics <- example_fit$diagnostics
precision <- precision_review_report(fit, diagnostics = diagnostics)
review <- summary(precision)
review$checks # Check statuses and reasons before making precision claims
#>                      Check Status
#> 1           Precision tier   pass
#> 2    Optimizer convergence   pass
#> 3     ModelSE availability   pass
#> 4 Fit-adjusted SE ordering   pass
#> 5     Reliability ordering   pass
#> 6 Facet precision coverage   pass
#> 7         SE source labels   pass
#>                                                                                                                                                                                                    Detail
#> 1 Uncertainty is conditional on the fitted model. Person posterior SDs condition on the fitted calibration; facet standard errors use observed information. Review interval assumptions before reporting.
#> 2                                                                                                              Numerical convergence checks passed; this alone does not establish valid SEs or intervals.
#> 3                                                                                                                                     Finite standard errors were available for 100.0% of non-fixed rows.
#> 4                                                                                                              Among available pairs, fit-adjusted SEs were at least as large as their unadjusted values.
#> 5                                                                                                     Among available pairs, fit-adjusted reliability values were not larger than the model-based values.
#> 6                                                                                                                    Each facet had sample/population summaries for both model and fit-adjusted SE modes.
#> 7                                                                                                                     Person uncertainty uses posterior SDs; facet uncertainty uses observed information.