summary.mfrm_diagnostics: Summarize an 'mfrm_diagnostics' object in a user-friendly...

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

Summarize an mfrm_diagnostics object in a user-friendly format

Description

Summarize an mfrm_diagnostics object in a user-friendly format

Usage

## S3 method for class 'mfrm_diagnostics'
summary(object, digits = 3, top_n = 10, ...)

Arguments

object	Output from diagnose_mfrm().
digits	Number of digits for printed numeric values.
top_n	Number of highest-absolute-Z fit rows to keep.
...	Reserved for generic compatibility.

Details

This method returns a compact diagnostics summary designed for quick review:

• design overview (observations, persons, facets, categories, subsets)

• diagnostic-basis guide for legacy versus strict fit paths

• global fit statistics

• approximate reliability/separation by facet

• top facet/person fit rows by absolute ZSTD

• counts of flagged diagnostics (unexpected, displacement, interactions)

Value

An object of class summary.mfrm_diagnostics with:

• overview: design-level counts and residual-PCA mode

• status: concise front-door status block for quick review

• key_warnings: highest-priority warnings to review first

• next_actions: recommended follow-up helpers

• diagnostic_basis: guide to legacy versus strict diagnostic targets

• fit_standardization: guide to the df convention used for fit ZSTD

• overall_fit: global fit block

• precision_profile: design-weighted precision summary across the information curve at decile theta points

• precision_review: separation / reliability / strata review for the sample- and population-basis modes (paired with precision_profile)

• reliability: facet-level separation/reliability summary

• facets_chisq: facets-style fixed-effect chi-square heterogeneity screen across non-person facets

• interrater: inter-rater agreement / pairwise correlation / rater separation overview when a Rater facet is present

• misfit_flagged: rows flagged by the Infit / Outfit / ZSTD misfit thresholds active for this fit

• misfit_thresholds: named numeric vector with the misfit lower / upper thresholds used to populate misfit_flagged

• category_usage: per-category response-frequency summary used to flag empty / collapsed categories

• top_fit: top ⁠|ZSTD|⁠ rows

• marginal_fit: optional strict marginal-fit overview when requested

• top_marginal_cells: largest strict marginal residual cells when requested

• marginal_pairwise: optional strict pairwise local-dependence overview

• top_marginal_pairs: largest strict pairwise residual summaries

• marginal_guidance: interpretation labels for strict marginal diagnostics

• reporting_map: manuscript-oriented guide to what is covered here versus which companion outputs should be consulted

• flags: compact flag counts for major diagnostics

• notes: short interpretation notes

• digits: numeric-print precision threaded through to print.summary.mfrm_diagnostics()

Interpreting output

• overview: analysis scale, subset count, and residual-PCA mode.

• diagnostic_basis: plain-language map of which fit path was computed and what each path means statistically.

• overall_fit: global fit indices.

• reliability: facet separation/reliability block, including model and real bounds when available.

• top_fit: highest ⁠|ZSTD|⁠ elements for immediate inspection.

• flags: compact counts for key warning domains.

Typical workflow

1. Run diagnostics with diagnose_mfrm(), using diagnostic_mode = "both" for RSM / PCM when you want legacy continuity plus strict marginal screening.

2. Review summary(diag) for major warnings and inspect diagnostic_basis before comparing legacy and strict outputs.

3. Follow up with dedicated tables/plots for flagged domains.

See Also

diagnose_mfrm(), summary.mfrm_fit()

Examples

toy <- load_mfrmr_data("example_core")
toy <- toy[toy$Person %in% unique(toy$Person)[1:4], ]
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
s <- summary(diag, top_n = 3)
s$key_warnings
# Look for: lines beginning with "MnSq misfit:" name the worst
#   element + Infit / Outfit values; "Unexpected responses flagged"
#   counts how many cell-level surprises the screen returned.
s$top_fit
# Look for: rows with |InfitZSTD| or |OutfitZSTD| > 2 are misfitting
#   at the 5% level; > 3 is misfitting at the 1% level. Investigate
#   in order of the AbsZ column.
s$facets_chisq
# Look for: FixedProb < 0.05 in each non-Person facet means the
#   facet contributes meaningful spread; FixedProb >= 0.05 means
#   that facet is statistically indistinguishable.

mfrmr documentation built on June 13, 2026, 1:07 a.m.