Build a facet statistics report (preferred alias)

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() for the same fit. Mismatched or outdated diagnostics are rejected. Recompute diagnostics after refitting; the model itself does not need to be fitted again.

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

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(x, type = "facets_chisq"), mfrmr_reports_and_tables

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("facet_statistics_report", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# Saved synthetic JML/RSM analysis of example_core.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "core-jml.rds", package = "mfrmr"))
fit <- example_fit$fit
out <- facet_statistics_report(fit)
summary(out)
p_fs <- plot(out, draw = FALSE)
p_fs$data$plot
} # }