mfrmr Workflow and Method Map

Quick reference for end-to-end mfrmr analysis and for checking which output objects support summary() and plot().

Canonical reporting route

For the clearest default route in RSM / PCM, use fit_mfrm() with method = "MML" -> diagnose_mfrm() with diagnostic_mode = "both" -> reporting_checklist() -> plot_qc_dashboard() and, when flagged, plot_marginal_fit() / plot_marginal_pairwise() -> build_apa_outputs() -> build_summary_table_bundle() -> apa_table() or export_summary_appendix().

Use JML only when you explicitly want a faster exploratory pass and are willing to defer strict marginal follow-up and formal precision language to a later MML run.

Canonical operational review route

When the main question is scale maintenance rather than manuscript reporting, branch after diagnose_mfrm() into: review_mfrm_anchors() and/or detect_anchor_drift() -> build_equating_chain() when adjacent-link review is needed -> build_linking_review() -> inspect review$group_view_index for stable wave / link / facet rollups and summary(review)$plot_routes for the next plot helper -> plot_anchor_drift() or plot(anchor_review, ...) for the specific flagged evidence family.

For bounded GPCM, use build_linking_review() as a caveated exploratory synthesis over direct anchor, drift, and chain evidence. It is not an operational GPCM linking decision or evidence that anchor drift is absent.

Canonical misfit case-review route

When the main question is which observations, facet levels, or pairwise structures deserve follow-up, branch after diagnose_mfrm() into: build_misfit_casebook() -> inspect casebook$group_view_index, casebook$group_views, and summary(casebook)$plot_routes for stable person / facet / wave rollups and the next plot helper -> plot_unexpected(), plot_displacement(), plot_marginal_fit(), or plot_marginal_pairwise() according to casebook$plot_map -> build_summary_table_bundle() / export_summary_appendix() when the flagged cases need appendix-style reporting support.

build_misfit_casebook() can still be used for bounded GPCM, but it should be read as an operational exploratory screen rather than as a strict Rasch-style invariance report.

Latent-regression route

When the fit uses population_formula = ..., keep the distinction between the estimator and the forecast helpers explicit:

fit_mfrm() estimates the current narrow latent-regression MML branch. In the returned fit object, fit$population$person_table is the complete-case estimation table, while fit$population$person_table_replay retains the observed-person-aligned pre-omit background-data table for replay/export provenance.
predict_mfrm_units() and sample_mfrm_plausible_values() can then score under the fitted population model when scored units also supply one-row-per-person background data. That scoring-time person_data contract remains separate from the fit object's stored replay table.
predict_mfrm_population() remains a scenario-level simulation/refit helper rather than the latent-regression estimator itself.

Score-category support

If the intended rating scale includes categories not observed in the current data, make that support explicit. For example, use rating_min = 1, rating_max = 5 for a 1-5 scale with only 2-5 observed. If an intermediate category is unobserved (for example 1, 2, 4, 5 with no 3), also set keep_original = TRUE if the zero-count category should remain in the fitted support. summary(describe_mfrm_data(...)) reports retained zero-count categories in Notes, printed Caveats, and $caveats; summary(fit) carries full structured rows into printed Caveats and $caveats, with Key warnings as a short triage subset. Summary-table exports route those rows through score_category_caveats or analysis_caveats. Adjacent threshold estimates should still be treated as weakly identified when an intermediate category is unobserved.

Typical workflow

Fit a model with fit_mfrm(). For final reporting, prefer method = "MML" unless you explicitly want a fast exploratory JML pass.
(Optional) Use run_mfrm_facets() or mfrmRFacets() for a legacy-compatible one-shot workflow wrapper.
For RSM / PCM, build diagnostics with diagnose_mfrm(). For final reporting, prefer diagnostic_mode = "both" so the legacy residual path and the strict marginal screen remain visible side by side. For bounded GPCM, diagnostics are now available through diagnose_mfrm() together with analyze_residual_pca(), interrater_agreement_table(), unexpected_response_table(), displacement_table(), measurable_summary_table(), rating_scale_table(), facet_quality_dashboard(), reporting_checklist(), and plot_qc_dashboard() – the fair-average panel of the dashboard reports an explicit unavailability indicator under GPCM. Use fair_average_table() directly when you need the supported slope-aware element-conditional fair averages. Treat those residual-based summaries as exploratory screens because the discrimination parameter is free. Full FACETS-style score-side contract review remains blocked for bounded GPCM; package-native scorefile export, fit-based reporting bundles, direct fair-average tables, and bias-screening tables carry their own caveats. Posterior scoring with predict_mfrm_units() / sample_mfrm_plausible_values(), design-weighted information via compute_information() / plot_information(), Wright/pathway/CCC plots via plot.mfrm_fit(), direct category reports via category_structure_report() / category_curves_report(), and direct data generation through build_mfrm_sim_spec(), extract_mfrm_sim_spec(), and simulate_mfrm_data() are also available when the simulation specification stores both thresholds and slopes. Use evaluate_mfrm_recovery() and assess_mfrm_recovery() for direct recovery checks plus caveated role-based design evaluation, population forecasting, diagnostic-screening, and signal-detection helpers. Caveated APA/QC/export bundles are available for sensitivity reporting, while score-side FACETS helpers remain outside the validated GPCM boundary. Use gpcm_capability_matrix() as the formal capability map before branching into less common helpers.
(Optional, RSM / PCM; bounded GPCM with caveat) Estimate interaction bias with estimate_bias().
Choose a downstream branch: reporting_checklist() for direct report preparation, or build_weighting_review() for Rasch-versus-bounded-GPCM weighting review, or build_misfit_casebook() / build_linking_review() for operational case review. For bounded GPCM, use build_linking_review() only as an exploratory index over direct anchor/drift/chain evidence.
Generate reporting bundles: build_summary_table_bundle(), apa_table(), export_summary_appendix(), build_fixed_reports(), build_visual_summaries(). For bounded GPCM, use the APA, visual, QC, and fit-based export bundles as caveated sensitivity-reporting surfaces; full score-side FACETS review stays blocked, while diagnostic/signal-detection design screening has its own caveated operating-characteristic route.
(Optional, RSM / PCM) Review report completeness with reference_case_review(). Use facets_output_contract_review() only when you explicitly need the compatibility layer.
(Optional, RSM / PCM) For operational linking follow-up, combine review_mfrm_anchors(), detect_anchor_drift(), and build_equating_chain() inside build_linking_review() before exporting appendix-style tables.
(Optional) Check packaged reference cases with reference_case_benchmark() when you want package-side reference checks.
(Optional) For design planning or future scoring, move to the simulation/prediction layer: build_mfrm_sim_spec() / extract_mfrm_sim_spec() -> evaluate_mfrm_recovery() -> assess_mfrm_recovery() / evaluate_mfrm_design() / predict_mfrm_population() -> predict_mfrm_units() / sample_mfrm_plausible_values(). Current fit-derived simulation specs include direct GPCM data generation and recovery checks. Design-evaluation, population-forecasting, diagnostic- screening, and signal-detection helpers also support bounded GPCM as caveated role-based simulation/refit evidence; inspect gpcm_boundary before using those results in design claims. Unit scoring can use an ordinary MML fit directly, a latent-regression MML fit when you also supply one-row-per-person background data for the scored units, or a JML fit when a post hoc reference-prior EAP layer is acceptable. Intercept-only latent-regression fits (population_formula = ~ 1) can reconstruct that minimal person table from the scored person IDs. Keep predict_mfrm_population() conceptually separate from that scoring layer: it is a simulation-based scenario forecast helper, not the latent-regression estimator itself. Prediction export still requires actual prediction objects in addition to include = "predictions".
Use summary() for compact text checks and plot() (or dedicated plot helpers) for base-R visual diagnostics.

Three practical routes

Quick first pass: RSM / PCM: fit_mfrm() -> diagnose_mfrm() -> plot_qc_dashboard() -> reporting_checklist() when you want the package to route the next figures. bounded GPCM: fit_mfrm() -> diagnose_mfrm() -> plot_qc_dashboard() / unexpected_response_table() -> rating_scale_table() -> compute_information() -> plot_information() -> plot.mfrm_fit() / category_curves_report() -> fair_average_table() / estimate_bias() when those screening tables answer the question. For bounded GPCM, the fit-based export family (build_mfrm_manifest(), build_mfrm_replay_script(), export_mfrm_bundle()) is available as caveated sensitivity-reporting output with explicit gpcm_boundary rows.
Linking and coverage review: subset_connectivity_report() -> plot(..., type = "design_matrix") -> plot_wright_unified().
Manuscript prep: RSM / PCM: reporting_checklist() -> inspect the "Visual Displays" and "Method Section" rows -> build_apa_outputs() -> build_summary_table_bundle() -> apa_table() or export_summary_appendix(). First-release GPCM: reporting_checklist() -> direct table/plot helpers -> build_apa_outputs() / build_visual_summaries() -> export_mfrm_bundle() with gpcm_boundary caveats.
Weighting-policy review: compare_mfrm() -> build_weighting_review() -> compute_information() / plot_information() when you want to inspect whether bounded GPCM is introducing substantively acceptable discrimination-based reweighting relative to the Rasch-family reference.
Design planning and forecasting: build_mfrm_sim_spec() or extract_mfrm_sim_spec() -> evaluate_mfrm_recovery() -> assess_mfrm_recovery() for parameter-recovery checks, then evaluate_mfrm_design() -> predict_mfrm_population() -> predict_mfrm_units() or sample_mfrm_plausible_values() under the fitted scoring basis (ordinary MML, latent-regression MML with person-level background data, or JML with the documented post hoc EAP approximation). Here again, predict_mfrm_population() is the scenario-level forecast helper, whereas predict_mfrm_units() / sample_mfrm_plausible_values() are the scoring layer. Prediction export requires actual prediction objects. First-release GPCM now supports direct data generation via build_mfrm_sim_spec(), extract_mfrm_sim_spec(), and simulate_mfrm_data(), evaluate_mfrm_recovery(), assess_mfrm_recovery(), caveated role-based design evaluation and population forecasting, diagnostic/signal-detection design screening, residual diagnostics, and direct curve/report helpers. The current planning layer remains role-based for two non-person facets even though estimation itself supports arbitrary facet counts; future arbitrary-facet planning fields should be treated as design metadata rather than finished public behavior.

Interpreting output

This help page is a map, not an estimator:

use it to decide function order,
confirm which objects have summary()/plot() defaults,
identify when dedicated helper functions are needed,
and treat reporting_checklist() as the package's readiness router for plot and report follow-up.

Objects with default `summary()` and `plot()` routes

mfrm_fit: summary(fit) and plot(fit, ...).
mfrm_diagnostics: summary(diag); plotting via dedicated helpers such as plot_unexpected(), plot_displacement(), plot_qc_dashboard().
mfrm_bias: summary(bias) and plot_bias_interaction().
mfrm_data_description: summary(ds) and plot(ds, ...).
mfrm_anchor_review: summary(review) and plot(review, ...).
mfrm_misfit_casebook: summary(casebook) and print(casebook), with grouping views available through casebook$group_view_index and casebook$group_views, source-specific plotting routed through summary(casebook)$plot_routes and casebook$plot_map, and appendix/report handoff available through build_summary_table_bundle() and export_summary_appendix().
mfrm_weighting_review: summary(review) and print(review), with information follow-up routed through compute_information() and plot_information() according to review$plot_map, and appendix/report handoff available through build_summary_table_bundle() and export_summary_appendix().
mfrm_linking_review: summary(review) and print(review), with grouping views available through review$group_view_index and review$group_views, and plotting routed through summary(review)$plot_routes, plot_anchor_drift(), and plot(anchor_review, ...) according to review$plot_map.
mfrm_facets_run: summary(run) and plot(run, type = c("fit", "qc"), ...).
apa_table: summary(tbl) and plot(tbl, ...).
mfrm_apa_outputs: summary(apa) for compact diagnostics of report text.
mfrm_summary_table_bundle: print(bundle) for manuscript-oriented table index plus named tables from supported summary() outputs, summary(bundle) for table-role/numeric coverage, and plot(bundle, ...) for table-size or numeric-column QC.
mfrm_threshold_profiles: summary(profiles) for preset threshold grids.
mfrm_population_prediction: summary(pred) for design-level forecast tables.
mfrm_unit_prediction: summary(pred) for unit-level posterior summaries under the fitted scoring basis.
mfrm_plausible_values: summary(pv) for draw-level uncertainty summaries.
mfrm_bundle families: summary() and class-aware plot(bundle, ...). Key bundle classes now also use class-aware summary(bundle): mfrm_unexpected, mfrm_fair_average, mfrm_displacement, mfrm_interrater, mfrm_facets_chisq, mfrm_bias_interaction, mfrm_rating_scale, mfrm_category_structure, mfrm_category_curves, mfrm_measurable, mfrm_unexpected_after_bias, mfrm_output_bundle, mfrm_residual_pca, mfrm_specifications, mfrm_data_quality, mfrm_iteration_report, mfrm_subset_connectivity, mfrm_facet_statistics, mfrm_facets_contract_review, mfrm_reference_review, mfrm_reference_benchmark.

`plot.mfrm_bundle()` coverage

Default dispatch now covers:

mfrm_unexpected, mfrm_fair_average, mfrm_displacement
mfrm_interrater, mfrm_facets_chisq, mfrm_bias_interaction
mfrm_bias_count, mfrm_fixed_reports, mfrm_visual_summaries
mfrm_category_structure, mfrm_category_curves, mfrm_rating_scale
mfrm_measurable, mfrm_unexpected_after_bias, mfrm_output_bundle
mfrm_residual_pca, mfrm_specifications, mfrm_data_quality
mfrm_iteration_report, mfrm_subset_connectivity, mfrm_facet_statistics
mfrm_facets_contract_review, mfrm_reference_review, mfrm_reference_benchmark

For unknown bundle classes, use dedicated plotting helpers or custom base-R plots from component tables.

Examples

if (FALSE) { # \dontrun{
toy_full <- load_mfrmr_data("example_core")
keep_people <- unique(toy_full$Person)[1:12]
toy <- toy_full[toy_full$Person %in% keep_people, , drop = FALSE]

fit <- fit_mfrm(
  toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "MML",
  quad_points = 7,
  maxit = 30
)
summary(fit)$next_actions

diag <- diagnose_mfrm(fit, residual_pca = "none", diagnostic_mode = "both")
summary(diag)$next_actions

chk <- reporting_checklist(fit, diagnostics = diag)
subset(
  chk$checklist,
  Section == "Visual Displays",
  c("Item", "DraftReady", "NextAction")
)

qc <- plot_qc_dashboard(fit, diagnostics = diag, draw = FALSE, preset = "publication")
qc$data$preset
p_marg <- plot_marginal_fit(diag, draw = FALSE, preset = "publication")
p_marg$data$preset

sc <- subset_connectivity_report(fit, diagnostics = diag)
p_design <- plot(sc, type = "design_matrix", draw = FALSE, preset = "publication")
p_design$data$plot

bundle <- build_summary_table_bundle(chk, appendix_preset = "recommended")
summary(bundle)$role_summary
plot(bundle, type = "appendix_presets", draw = FALSE)$data$plot
} # }