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".

Value

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

Details

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

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

Run facet_statistics_report(fit).
Inspect summary/ranges for anomalous facets.
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

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
out <- facet_statistics_report(fit)
summary(out)
p_fs <- plot(out, draw = FALSE)
p_fs$data$plot
}