Choose an mfrmr output helper by user goal
Source:R/help_reports_and_tables.R
mfrmr_output_guide.Rdmfrmr_output_guide() returns a compact table for choosing among the main
table, report, review, bundle, export, and compatibility helpers. It is a
user-facing map, not an analysis result.
Usage
mfrmr_output_guide(
scope = c("all", "public", "entry", "viewer", "binary", "tables", "reports", "reviews",
"bundles", "exports", "compatibility", "gpcm", "simulation", "linking", "network",
"response_time", "facets", "conquest", "r")
)Arguments
- scope
Which rows to return.
"all"returns the full guide."public"returns the small top-level public surface for most users."entry"returns the recommended first-screen routes."viewer"returns local-viewer routes built aroundmfrm_results(include = ...)."binary"returns the two-category person-item Rasch route and checks. Other values filter to one output family or to bounded-GPCM-relevant routes."linking"returns anchor, drift, and equating route rows."simulation"and"network"return advanced design-review rows."response_time"returns descriptive response-time QC rows."facets","conquest", and"r"return user-pathway rows for people arriving from those workflows.
Value
A data.frame with one row per recommended route and columns:
ScopeQuestionOutputFamilyLifecycleUserLevelAPILayerObjectRoleDecisionBoundaryRecommendedEntryMainFunctionUseWhenTypicalInputNextStepGPCMStatusNotes
Details
Naming convention used by the guide:
*_table: focused table or table-like result for one evidence source*_report: multi-table evidence bundle for a reporting question*_review: status, interpretation, or decision-support object*_bundle: reusable collection of tables/metadata for handoffexport_*: writes files or appendix artifacts
First-screen route
Use mfrmr_output_guide("public") when you want the shortest top-level API
map. Use mfrmr_output_guide("entry") when you specifically want
first-screen creation routes. The entry guide points new scripts to
fit_mfrm() -> diagnose_mfrm() -> mfrm_results(), existing fits to
mfrm_results(), and exploratory console
work to mfrm_results_interactive(). After creating res, use
summary(res)$next_actions to choose the next purpose-specific helper.
Use mfrmr_output_guide("viewer") when the next step is the optional local
Shiny reader; it shows which include preset to use before calling
launch_mfrmr_viewer().
How to use this guide
Treat MainFunction as the route to try next and UseWhen as the guardrail.
The guide is not a replacement for the help pages of the listed functions;
it is a namespace map for deciding which page to open.
For bounded GPCM, use scope = "gpcm" to find both the support matrix
and the table that explains how out-of-scope routes are handled.
Examples
public <- mfrmr_output_guide("public")
public[, c("Question", "APILayer", "ObjectRole", "MainFunction")]
#> Question
#> 1 Start a new reproducible analysis
#> 2 Open the comprehensive first-screen result object
#> 3 Read report readiness before writing
#> 4 Open a local point-and-click reader
#> 5 Download results, report tables, HTML, and replay code
#> 6 Find the next specialized helper
#> 7 Use exploratory prompts only when explicitly requested
#> APILayer ObjectRole
#> 1 top_level_public_surface model estimation and result-object entry
#> 2 top_level_public_surface comprehensive result object
#> 3 top_level_public_surface report-readiness surface
#> 4 top_level_public_surface local reader over existing results
#> 5 top_level_public_surface file export surface
#> 6 top_level_public_surface route-selection guide
#> 7 top_level_public_surface explicit opt-in interactive entry
#> MainFunction
#> 1 fit_mfrm(); diagnose_mfrm(); res <- mfrm_results(fit)
#> 2 res <- mfrm_results(fit, include = ...); summary(res)
#> 3 report <- mfrm_report(res); summary(report); mfrm_report(res, output = "html")
#> 4 launch_mfrmr_viewer(res)
#> 5 export_mfrm_results(res, include = c("default", "report"))
#> 6 mfrmr_output_guide(scope); summary(res)$next_actions
#> 7 mfrm_results_interactive(df)
entry <- mfrmr_output_guide("entry")
entry[, c("Question", "Lifecycle", "UserLevel", "MainFunction")]
#> Question
#> 8 Start with explicit model roles and a comprehensive first screen
#> 9 Open a FACETS-style result surface from an existing fit
#> 10 Browse the comprehensive result in a local Shiny viewer
#> 11 Choose the next purpose-specific helper without scanning the namespace
#> 12 Use column-selection prompts for exploratory data-frame input
#> Lifecycle UserLevel MainFunction
#> 8 stable beginner fit_mfrm(); diagnose_mfrm(); mfrm_results()
#> 9 stable beginner mfrm_results()
#> 10 stable beginner res <- mfrm_results(fit); launch_mfrmr_viewer(res)
#> 11 stable beginner mfrmr_output_guide(); summary(res)$next_actions
#> 12 stable beginner mfrm_results_interactive()
reviews <- mfrmr_output_guide("reviews")
reviews[, c("Question", "MainFunction", "UseWhen")]
#> Question
#> 25 Review response-time metadata as descriptive QC context
#> 26 Decide how strongly precision claims can be phrased
#> 29 Screen bias, DFF, or interaction evidence
#> 30 Review anchors, drift, and linking readiness
#> 31 Compare equal-weighting and bounded-GPCM routes
#> MainFunction
#> 25 response_time_review(); mfrm_results(include = "response_time", response_time = ...); plot_response_time_review(); plot_data_components()
#> 26 precision_review_report()
#> 29 mfrm_results(fit, include = "bias"); estimate_bias(); analyze_dff(); bias_interaction_report()
#> 30 review_mfrm_anchors(); detect_anchor_drift(); build_linking_review()
#> 31 build_model_choice_review(); build_weighting_review(); compare_mfrm()
#> UseWhen
#> 25 You have event-level timing metadata and need rapid/slow-response screening outside the fitted MFRM likelihood.
#> 26 You need to separate model-based, hybrid, and exploratory precision evidence.
#> 29 You need screening evidence for follow-up fairness or interaction review.
#> 30 You need operational scale-maintenance checks for RSM/PCM workflows.
#> 31 You need to review whether discrimination-based reweighting changes conclusions.
mfrmr_output_guide("gpcm")[, c("Question", "MainFunction", "GPCMStatus")]
#> Question
#> 1 Start a new reproducible analysis
#> 2 Open the comprehensive first-screen result object
#> 3 Read report readiness before writing
#> 4 Open a local point-and-click reader
#> 5 Download results, report tables, HTML, and replay code
#> 7 Use exploratory prompts only when explicitly requested
#> 8 Start with explicit model roles and a comprehensive first screen
#> 9 Open a FACETS-style result surface from an existing fit
#> 10 Browse the comprehensive result in a local Shiny viewer
#> 12 Use column-selection prompts for exploratory data-frame input
#> 13 Open the standard first-screen viewer
#> 14 Prepare publication-oriented viewer sections
#> 15 Check validation, fit, and separation surfaces before reporting
#> 16 Inspect bias-screen prompts without choosing contrasts automatically
#> 17 Inspect pathway-map and row-level misfit prompts
#> 18 Inspect anchor and linking readiness
#> 19 Prepare a broad reviewer-facing viewer object
#> 20 Fit ordinary person-item binary responses
#> 22 Open the first-screen results for a binary Rasch run
#> 26 Decide how strongly precision claims can be phrased
#> 27 Summarize facet variability, separation, and measurability
#> 28 Review category functioning and expected-score curves
#> 29 Screen bias, DFF, or interaction evidence
#> 30 Review anchors, drift, and linking readiness
#> 31 Compare equal-weighting and bounded-GPCM routes
#> 32 Turn summaries into reusable appendix tables
#> 33 Assemble manuscript-oriented narrative output
#> 34 Write files for appendix, replay, or handoff
#> 35 Serve a legacy-compatible downstream layout
#> 36 Open first-screen anchor and linking readiness from an existing fit
#> 37 Review intended anchor and group-anchor tables before fitting
#> 38 Check drift across separately fitted waves or forms
#> 39 Build a screened equating chain across ordered calibrations
#> 40 Generate planned, sparse, or peer-review response data
#> 41 Evaluate design and recovery operating behavior
#> 42 Screen diagnostic behavior under misspecification scenarios
#> 43 Export simulation operating-characteristic tables for appendices
#> 44 Review co-observation connectivity as design evidence
#> 45 Review peer-review assignment topology
#> 46 Check the bounded GPCM support matrix
#> 47 Review out-of-scope bounded GPCM route guidance
#> 48 State the FACETS relationship before using FACETS-style routes
#> 49 Translate FACETS direct and group anchor blocks
#> 50 Review anchor drift across forms, raters, or waves
#> 51 List fit measures and misfit flags by facet
#> 52 Explain FACETS df and ZSTD conversion
#> 53 Bring an external FACETS fit table into the review
#> 54 Review rating-scale categories, fair averages, and expected curves
#> 55 Review FACETS Table 14-style bias and interaction signals
#> 56 Draw a Wright map / variable map on the common logit scale
#> 59 Prepare a scoped ConQuest overlap case
#> 60 Compare extracted ConQuest tables after the external run
#> 61 State where mfrmr is less free than ConQuest
#> 65 Combine tables and plot data for reports
#> MainFunction
#> 1 fit_mfrm(); diagnose_mfrm(); res <- mfrm_results(fit)
#> 2 res <- mfrm_results(fit, include = ...); summary(res)
#> 3 report <- mfrm_report(res); summary(report); mfrm_report(res, output = "html")
#> 4 launch_mfrmr_viewer(res)
#> 5 export_mfrm_results(res, include = c("default", "report"))
#> 7 mfrm_results_interactive(df)
#> 8 fit_mfrm(); diagnose_mfrm(); mfrm_results()
#> 9 mfrm_results()
#> 10 res <- mfrm_results(fit); launch_mfrmr_viewer(res)
#> 12 mfrm_results_interactive()
#> 13 res <- mfrm_results(fit, include = "standard"); launch_mfrmr_viewer(res)
#> 14 res <- mfrm_results(fit, include = "publication"); launch_mfrmr_viewer(res)
#> 15 res <- mfrm_results(fit, include = "validation"); launch_mfrmr_viewer(res)
#> 16 res <- mfrm_results(fit, include = "bias"); launch_mfrmr_viewer(res)
#> 17 res <- mfrm_results(fit, include = "misfit_review"); launch_mfrmr_viewer(res)
#> 18 res <- mfrm_results(fit, include = "linking"); launch_mfrmr_viewer(res)
#> 19 res <- mfrm_results(fit, include = c("publication", "bias", "misfit_review", "linking")); launch_mfrmr_viewer(res)
#> 20 fit_mfrm(data, person = ..., facets = "Item", score = ..., model = "RSM"); mfrm_results()
#> 22 mfrm_results(fit); plot(res, type = "wright"); plot(res, type = "qc")
#> 26 precision_review_report()
#> 27 facet_statistics_report()
#> 28 rating_scale_table(); category_structure_report(); category_curves_report()
#> 29 mfrm_results(fit, include = "bias"); estimate_bias(); analyze_dff(); bias_interaction_report()
#> 30 review_mfrm_anchors(); detect_anchor_drift(); build_linking_review()
#> 31 build_model_choice_review(); build_weighting_review(); compare_mfrm()
#> 32 build_summary_table_bundle()
#> 33 mfrm_report(); reporting_checklist(); build_apa_outputs()
#> 34 export_mfrm_results(); export_summary_appendix(); export_mfrm_bundle(); build_mfrm_manifest()
#> 35 run_mfrm_facets(); facets_output_file_bundle(); facets_output_contract_review()
#> 36 mfrm_results(fit, include = "linking"); plot(res, type = "anchors")
#> 37 make_anchor_table(); review_mfrm_anchors(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 38 detect_anchor_drift(list(Wave1 = fit1, Wave2 = fit2)); build_linking_review(drift = ...); plot_anchor_drift()
#> 39 build_equating_chain(list(Form1 = fit1, Form2 = fit2)); build_linking_review(chain = ...); plot_anchor_drift(type = "chain")
#> 40 build_mfrm_sim_spec(); simulate_mfrm_data(); extract_mfrm_sim_spec()
#> 41 evaluate_mfrm_design(); evaluate_mfrm_recovery(); assess_mfrm_recovery()
#> 42 evaluate_mfrm_diagnostic_screening(); summary(); plot(..., draw = FALSE); plot_data()
#> 43 summary(diag_eval); build_summary_table_bundle(diag_eval); export_summary_appendix(diag_eval)
#> 44 mfrm_network_analysis(); build_mfrm_network_review()
#> 45 build_peer_review_sim_spec(); build_peer_review_design_review(); build_mfrm_network_review(peer_review_design = ...)
#> 46 gpcm_capability_matrix()
#> 47 gpcm_runtime_guard_coverage()
#> 48 facets_positioning_guide(); facets_feature_coverage(); run_mfrm_facets(); mfrmRFacets(); facets_output_file_bundle()
#> 49 review_mfrm_anchors(); make_anchor_table(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 50 anchor_to_baseline(); detect_anchor_drift(); build_equating_chain(); plot_anchor_drift()
#> 51 fit_measures_table(); facets_chisq_table(); displacement_table()
#> 52 facets_fit_df_guide(); diagnose_mfrm(fit_df_method = "both")
#> 53 read_facets_fit_table(); facets_fit_review(); plot(..., type = "df_sensitivity")
#> 54 rating_scale_table(); category_structure_report(); category_curves_report(); fair_average_table(); plot_fair_average()
#> 55 mfrm_results(fit, include = "bias"); estimate_bias(); bias_interaction_report(); bias_pairwise_report(); plot_bias_interaction()
#> 56 plot(fit, type = "wright"); plot_wright_unified(); plot_data(type = "wright")
#> 59 build_conquest_overlap_bundle()
#> 60 normalize_conquest_overlap_files(); review_conquest_overlap()
#> 61 reporting_checklist(); reference_case_benchmark()
#> 65 build_summary_table_bundle(); build_visual_summaries()
#> GPCMStatus
#> 1 supported_with_caveat
#> 2 supported_with_caveat
#> 3 direct_outputs_supported; APA route scoped
#> 4 viewer_only_uses_existing_results
#> 5 summary_appendix_supported; fit_bundle_supported_with_caveat
#> 7 supported_with_caveat
#> 8 supported_with_caveat
#> 9 supported_with_caveat
#> 10 viewer_only_uses_existing_results
#> 12 supported_with_caveat
#> 13 viewer_only_uses_existing_results
#> 14 supported_with_caveat
#> 15 supported_with_caveat
#> 16 supported_with_caveat
#> 17 supported_with_caveat
#> 18 anchor_readiness_supported; exploratory_linking_review_supported_with_caveat
#> 19 viewer_only_uses_existing_results
#> 20 rsm_recommended_for_ordinary_binary
#> 22 supported_with_caveat
#> 26 supported_with_caveat
#> 27 supported_with_caveat
#> 28 supported_with_caveat
#> 29 supported_with_caveat
#> 30 supported_with_caveat; exploratory_gpcm_linking_review
#> 31 supported_with_caveat
#> 32 supported_for_direct_outputs
#> 33 supported_with_caveat
#> 34 summary_appendix_supported; fit_bundle_supported_with_caveat
#> 35 graph_only_or_blocked_by_score_side_semantics
#> 36 anchor_readiness_supported; exploratory_linking_review_supported_with_caveat
#> 37 supported_with_caveat
#> 38 supported_with_caveat; exploratory_gpcm_linking_review
#> 39 supported_with_caveat; exploratory_gpcm_linking_review
#> 40 supported_with_caveat
#> 41 supported_with_caveat
#> 42 supported_with_caveat
#> 43 supported_with_caveat
#> 44 design_diagnostic_not_measurement_gate
#> 45 design_diagnostic_not_measurement_gate
#> 46 bounded_support_matrix
#> 47 out_of_scope_route_guidance
#> 48 graph_only_or_blocked_by_score_side_semantics
#> 49 supported_with_caveat
#> 50 supported_with_caveat; exploratory_gpcm_linking_review
#> 51 supported_with_caveat
#> 52 supported_with_caveat
#> 53 supported_with_caveat
#> 54 supported_with_caveat
#> 55 supported_with_caveat
#> 56 supported_with_caveat
#> 59 blocked_for_gpcm; rsm_pcm_overlap_only
#> 60 blocked_for_gpcm; rsm_pcm_overlap_only
#> 61 blocked_for_gpcm; rsm_pcm_overlap_only
#> 65 supported_for_direct_outputs
mfrmr_output_guide("simulation")[, c("Question", "Lifecycle")]
#> Question Lifecycle
#> 40 Generate planned, sparse, or peer-review response data advanced
#> 41 Evaluate design and recovery operating behavior advanced
#> 42 Screen diagnostic behavior under misspecification scenarios advanced
#> 43 Export simulation operating-characteristic tables for appendices advanced
mfrmr_output_guide("linking")[, c("Question", "MainFunction")]
#> Question
#> 36 Open first-screen anchor and linking readiness from an existing fit
#> 37 Review intended anchor and group-anchor tables before fitting
#> 38 Check drift across separately fitted waves or forms
#> 39 Build a screened equating chain across ordered calibrations
#> MainFunction
#> 36 mfrm_results(fit, include = "linking"); plot(res, type = "anchors")
#> 37 make_anchor_table(); review_mfrm_anchors(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 38 detect_anchor_drift(list(Wave1 = fit1, Wave2 = fit2)); build_linking_review(drift = ...); plot_anchor_drift()
#> 39 build_equating_chain(list(Form1 = fit1, Form2 = fit2)); build_linking_review(chain = ...); plot_anchor_drift(type = "chain")
mfrmr_output_guide("facets")[, c("Question", "MainFunction")]
#> Question
#> 48 State the FACETS relationship before using FACETS-style routes
#> 49 Translate FACETS direct and group anchor blocks
#> 50 Review anchor drift across forms, raters, or waves
#> 51 List fit measures and misfit flags by facet
#> 52 Explain FACETS df and ZSTD conversion
#> 53 Bring an external FACETS fit table into the review
#> 54 Review rating-scale categories, fair averages, and expected curves
#> 55 Review FACETS Table 14-style bias and interaction signals
#> 56 Draw a Wright map / variable map on the common logit scale
#> 57 Check score support and rater response patterns before fitting claims
#> 58 Write residual and subset files for external review
#> MainFunction
#> 48 facets_positioning_guide(); facets_feature_coverage(); run_mfrm_facets(); mfrmRFacets(); facets_output_file_bundle()
#> 49 review_mfrm_anchors(); make_anchor_table(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 50 anchor_to_baseline(); detect_anchor_drift(); build_equating_chain(); plot_anchor_drift()
#> 51 fit_measures_table(); facets_chisq_table(); displacement_table()
#> 52 facets_fit_df_guide(); diagnose_mfrm(fit_df_method = "both")
#> 53 read_facets_fit_table(); facets_fit_review(); plot(..., type = "df_sensitivity")
#> 54 rating_scale_table(); category_structure_report(); category_curves_report(); fair_average_table(); plot_fair_average()
#> 55 mfrm_results(fit, include = "bias"); estimate_bias(); bias_interaction_report(); bias_pairwise_report(); plot_bias_interaction()
#> 56 plot(fit, type = "wright"); plot_wright_unified(); plot_data(type = "wright")
#> 57 data_quality_report(); plot(..., type = "dashboard")
#> 58 write_mfrm_residual_file(); write_mfrm_subset_file(); facets_output_file_bundle()
mfrmr_output_guide("binary")[, c("Question", "MainFunction")]
#> Question
#> 20 Fit ordinary person-item binary responses
#> 21 Confirm the two-category score support
#> 22 Open the first-screen results for a binary Rasch run
#> MainFunction
#> 20 fit_mfrm(data, person = ..., facets = "Item", score = ..., model = "RSM"); mfrm_results()
#> 21 describe_mfrm_data(); fit$prep$score_map; summary(fit)$settings_overview
#> 22 mfrm_results(fit); plot(res, type = "wright"); plot(res, type = "qc")
mfrmr_output_guide("viewer")[, c("Question", "MainFunction")]
#> Question
#> 13 Open the standard first-screen viewer
#> 14 Prepare publication-oriented viewer sections
#> 15 Check validation, fit, and separation surfaces before reporting
#> 16 Inspect bias-screen prompts without choosing contrasts automatically
#> 17 Inspect pathway-map and row-level misfit prompts
#> 18 Inspect anchor and linking readiness
#> 19 Prepare a broad reviewer-facing viewer object
#> MainFunction
#> 13 res <- mfrm_results(fit, include = "standard"); launch_mfrmr_viewer(res)
#> 14 res <- mfrm_results(fit, include = "publication"); launch_mfrmr_viewer(res)
#> 15 res <- mfrm_results(fit, include = "validation"); launch_mfrmr_viewer(res)
#> 16 res <- mfrm_results(fit, include = "bias"); launch_mfrmr_viewer(res)
#> 17 res <- mfrm_results(fit, include = "misfit_review"); launch_mfrmr_viewer(res)
#> 18 res <- mfrm_results(fit, include = "linking"); launch_mfrmr_viewer(res)
#> 19 res <- mfrm_results(fit, include = c("publication", "bias", "misfit_review", "linking")); launch_mfrmr_viewer(res)
mfrmr_output_guide("response_time")[, c("Question", "MainFunction")]
#> Question
#> 25 Review response-time metadata as descriptive QC context
#> 64 Reuse response-time plot data for custom QC graphics
#> MainFunction
#> 25 response_time_review(); mfrm_results(include = "response_time", response_time = ...); plot_response_time_review(); plot_data_components()
#> 64 response_time_review(); plot_response_time_review(..., draw = FALSE); plot_data_components(); plot_data()