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Summarize an mfrm_diagnostics object in a user-friendly format

Source: R/api-methods.R
summary.mfrm_diagnostics.Rd

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.

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()

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)

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

if (FALSE) { # interactive()
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.
}