facet_statistics_report: Build a facet statistics report (preferred alias)

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

Build a facet statistics report (preferred alias)

Description

Build a facet statistics report (preferred alias)

Usage

facet_statistics_report(
  fit,
  diagnostics = NULL,
  metrics = c("Estimate", "Infit", "Outfit", "SE"),
  ruler_width = 41,
  distribution_basis = c("both", "sample", "population"),
  se_mode = c("both", "model", "fit_adjusted")
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
metrics	Numeric columns in diagnostics$measures to summarize.
ruler_width	Width of the fixed-width ruler used for M/S/Q/X marks.
distribution_basis	Which distribution basis to keep in the appended precision summary: "both" (default), "sample", or "population".
se_mode	Which standard-error mode to keep in the appended precision summary: "both" (default), "model", or "fit_adjusted".

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_facet_statistics (type = "means", "sds", "ranges").

Value

A named list with facet-statistics components. Class: mfrm_facet_statistics.

Interpreting output

• facet-level means/SD/ranges of selected metrics (Estimate, fit indices, SE).

• fixed-width ruler rows (M/S/Q/X) for compact profile scanning.

Typical workflow

1. Run facet_statistics_report(fit).

2. Inspect summary/ranges for anomalous facets.

3. Cross-check flagged facets with fit and chi-square diagnostics. The returned bundle now includes:

• precision_summary: facet precision/separation indices by DistributionBasis and SEMode

• variability_tests: fixed/random variability tests by facet

• se_modes: compact list of available SE modes by facet

See Also

diagnose_mfrm(), summary.mfrm_fit(), plot_facets_chisq(), mfrmr_reports_and_tables

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
out <- facet_statistics_report(fit)
summary(out)
p_fs <- plot(out, draw = FALSE)
class(p_fs)

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