facet_quality_dashboard: Facet-quality dashboard for facet-level screening

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

Facet-quality dashboard for facet-level screening

Description

Build a compact dashboard for one facet at a time, combining facet severity, misfit, central-tendency screening, and optional bias counts.

Usage

facet_quality_dashboard(
  fit,
  diagnostics = NULL,
  facet = NULL,
  bias_results = NULL,
  severity_warn = 1,
  misfit_warn = 1.5,
  central_tendency_max = 0.25,
  bias_count_warn = 1L,
  bias_abs_t_warn = 2,
  bias_abs_size_warn = 0.5,
  bias_p_max = 0.05
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
facet	Optional facet name. When NULL, the function tries to infer a rater-like facet and otherwise falls back to the first modeled facet.
bias_results	Optional output from estimate_bias() or a named list of such outputs. Non-matching bundles are skipped quietly.
severity_warn	Absolute estimate cutoff used to flag severity outliers.
misfit_warn	Mean-square cutoff used to flag misfit. Values above this cutoff or below its reciprocal are flagged.
central_tendency_max	Absolute estimate cutoff used to flag central tendency. Levels near zero are marked.
bias_count_warn	Minimum flagged-bias row count required to flag a level.
bias_abs_t_warn	Absolute t cutoff used when deriving bias-row flags from a raw bias bundle.
bias_abs_size_warn	Absolute bias-size cutoff used when deriving bias-row flags from a raw bias bundle.
bias_p_max	Probability cutoff used when deriving bias-row flags from a raw bias bundle.

Details

The dashboard screens individual facet elements across four complementary criteria:

• Severity: elements with |\mathrm{Estimate}| > severity_warn logits are flagged as unusually harsh or lenient.

• Misfit: elements with Infit or Outfit MnSq outside [1/misfit_warn,\;misfit_warn] (default 0.67–1.5) are flagged.

• Central tendency: elements with |\mathrm{Estimate}| < central_tendency_max logits are flagged. Near-zero estimates may indicate a rater who avoids extreme categories, producing artificially narrow score ranges.

• Bias: elements involved in \ge bias_count_warn screen-positive interaction cells (from estimate_bias()) are flagged.

A flag density score counts how many of the four criteria each element triggers. Elements flagged on multiple criteria warrant priority review (e.g., rater retraining, data exclusion).

Default thresholds are designed for moderate-stakes rating contexts. Adjust for your application: stricter thresholds for high-stakes certification, more lenient for formative assessment.

Value

An object of class mfrm_facet_dashboard (also inheriting from mfrm_bundle and list). The object summarizes one target facet: overview reports the facet-level screening totals, summary provides aggregate estimates and flag counts, detail contains one row per facet level with the computed screening indicators, ranked orders levels by review priority, flagged keeps only levels requiring follow-up, bias_sources records which bias-result bundles contributed to the counts, settings stores the resolved thresholds, and notes gives short interpretation messages about how to read the dashboard.

Output

The returned object is a bundle-like list with class mfrm_facet_dashboard and components such as:

• overview: one-row structural overview

• summary: one-row screening summary

• detail: level-level detail table

• ranked: detail ordered by flag density / severity

• flagged: flagged levels only

• bias_sources: per-bundle bias aggregation metadata

• settings: resolved threshold settings

• notes: short interpretation notes

See Also

diagnose_mfrm(), estimate_bias(), plot_qc_dashboard()

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")
dash <- facet_quality_dashboard(fit, diagnostics = diag)
summary(dash)

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