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", "beginner", "psychometric", "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 canonical six-step route for most users;"beginner"returns the same compact route rather than combining every beginner-labelled specialist row."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") or
mfrmr_output_guide("beginner") for the shortest top-level API map:
an explicit describe_mfrm_data() check and fit_mfrm() MML fit, the lightweight fit summary, the
comprehensive FACETS-organized summary, the required native Wright map
with SE/CI, optional FACETS-style Wright and person-inclusive Infit views,
and finally report/export. Use mfrmr_output_guide("entry") when you
specifically need alternative first-screen creation routes, including
existing result objects, the optional viewer, or interactive console work.
After creating res, use summary(res)$next_actions to choose a
purpose-specific specialist helper.
Use mfrmr_output_guide("viewer") when the next step is the optional local
Shiny reader; it shows which include preset to use before calling
launch_mfrmr_viewer().
Use mfrmr_output_guide("psychometric") for the technical table,
review, and reporting routes whose interpretation boundaries should be
checked before manuscript use.
How to use this guide
Treat MainFunction as the route to try next and UseWhen as the guardrail.
The guide is not a replacement for the help pages of the listed functions;
it is a namespace map for deciding which page to open.
For bounded GPCM, use scope = "gpcm" to find both the support matrix
and the table that explains how out-of-scope routes are handled.
Examples
public <- mfrmr_output_guide("public")
public[, c("Question", "APILayer", "ObjectRole", "MainFunction")]
#> Question
#> 1 1. Check score support and fit with explicit data roles
#> 2 2. Check convergence and fitted-model settings
#> 3 3. Build the comprehensive FACETS-organized review
#> 4 4. Create the required native Wright map with SE or CI
#> 5 5. Add optional FACETS-style and Infit pathway views
#> 6 6. Review, report, and export the completed results
#> APILayer ObjectRole
#> 1 top_level_public_surface model estimation and result-object entry
#> 2 top_level_public_surface fit summary surface
#> 3 top_level_public_surface comprehensive result object
#> 4 top_level_public_surface specialist evidence component
#> 5 top_level_public_surface specialist evidence component
#> 6 top_level_public_surface file export surface
#> MainFunction
#> 1 describe_mfrm_data(data, person = ..., facets = ..., score = ...); fit <- fit_mfrm(data, person = ..., facets = ..., score = ..., method = "MML")
#> 2 summary(fit, profile = "fit", detail = "brief")
#> 3 review <- summary(fit, profile = "facets", detail = "brief"); res <- review$results
#> 4 plot(res, type = "wright", renderer = "native", show_ci = TRUE, top_n = Inf, preset = "publication")
#> 5 fit$prep$score_map; plot(res, type = "wright", renderer = "facets", category_labels = rubric_labels, show_ci = FALSE); plot(res, type = "fit_pathway", fit_stat = "Infit", include_person = TRUE, person_labels = "none")
#> 6 report <- mfrm_report(res); summary(report, view = "reader"); export_mfrm_results(res, preset = "starter")
entry <- mfrmr_output_guide("entry")
entry[, c("Question", "Lifecycle", "UserLevel", "MainFunction")]
#> Question
#> 7 Start with explicit model roles and a comprehensive first screen
#> 8 Open a FACETS-style result surface from an existing fit
#> 9 Browse the comprehensive result in a local Shiny viewer
#> 10 Choose the next purpose-specific helper without scanning the namespace
#> 11 Use column-selection prompts for exploratory data-frame input
#> Lifecycle UserLevel MainFunction
#> 7 stable beginner fit_mfrm(); diagnose_mfrm(); mfrm_results()
#> 8 stable beginner mfrm_results()
#> 9 stable beginner res <- mfrm_results(fit); launch_mfrmr_viewer(res)
#> 10 stable beginner mfrmr_output_guide(); summary(res)$next_actions
#> 11 stable beginner mfrm_results_interactive()
reviews <- mfrmr_output_guide("reviews")
reviews[, c("Question", "MainFunction", "UseWhen")]
#> Question
#> 24 Review response-time metadata as descriptive QC context
#> 25 Decide how strongly precision claims can be phrased
#> 28 Screen bias, DFF, or interaction evidence
#> 29 Review anchors, drift, and linking readiness
#> 30 Compare equal-weighting and bounded-GPCM routes
#> MainFunction
#> 24 response_time_review(); mfrm_results(include = "response_time", response_time = ...); plot_response_time_review(); plot_data_components()
#> 25 precision_review_report()
#> 28 mfrm_results(fit, include = "bias"); estimate_bias(); analyze_dff(); bias_interaction_report()
#> 29 review_mfrm_anchors(); detect_anchor_drift(); build_linking_review()
#> 30 build_model_choice_review(); build_weighting_review(); compare_mfrm()
#> UseWhen
#> 24 You have event-level timing metadata and need rapid/slow-response screening outside the fitted MFRM likelihood.
#> 25 You need to separate model-based, hybrid, and exploratory precision evidence.
#> 28 You need screening evidence for follow-up fairness or interaction review.
#> 29 You need operational scale-maintenance checks for RSM/PCM workflows.
#> 30 You need to review whether discrimination-based reweighting changes conclusions.
mfrmr_output_guide("gpcm")[, c("Question", "MainFunction", "GPCMStatus")]
#> Question
#> 1 1. Check score support and fit with explicit data roles
#> 2 2. Check convergence and fitted-model settings
#> 3 3. Build the comprehensive FACETS-organized review
#> 4 4. Create the required native Wright map with SE or CI
#> 5 5. Add optional FACETS-style and Infit pathway views
#> 6 6. Review, report, and export the completed results
#> 7 Start with explicit model roles and a comprehensive first screen
#> 8 Open a FACETS-style result surface from an existing fit
#> 9 Browse the comprehensive result in a local Shiny viewer
#> 11 Use column-selection prompts for exploratory data-frame input
#> 12 Open the standard first-screen viewer
#> 13 Prepare publication-oriented viewer sections
#> 14 Check validation, fit, and separation surfaces before reporting
#> 15 Inspect bias-screen prompts without choosing contrasts automatically
#> 16 Inspect pathway-map and row-level misfit prompts
#> 17 Inspect anchor and linking readiness
#> 18 Prepare a broad reviewer-facing viewer object
#> 19 Fit ordinary person-item binary responses
#> 21 Open the first-screen results for a binary Rasch run
#> 25 Decide how strongly precision claims can be phrased
#> 26 Summarize facet variability, separation, and measurability
#> 27 Review category functioning and expected-score curves
#> 28 Screen bias, DFF, or interaction evidence
#> 29 Review anchors, drift, and linking readiness
#> 30 Compare equal-weighting and bounded-GPCM routes
#> 31 Turn summaries into reusable appendix tables
#> 32 Assemble manuscript-oriented narrative output
#> 33 Write files for appendix, replay, or handoff
#> 34 Serve a legacy-compatible downstream layout
#> 35 Open first-screen anchor and linking readiness from an existing fit
#> 36 Review intended anchor and group-anchor tables before fitting
#> 37 Check drift across separately fitted waves or forms
#> 38 Build a screened equating chain across ordered calibrations
#> 39 Generate planned, sparse, or peer-review response data
#> 40 Evaluate design and recovery operating behavior
#> 41 Screen diagnostic behavior under misspecification scenarios
#> 42 Export simulation operating-characteristic tables for appendices
#> 43 Review co-observation connectivity as design evidence
#> 44 Review peer-review assignment topology
#> 45 Check the bounded GPCM support matrix
#> 46 Find alternatives for unavailable bounded GPCM routes
#> 47 1. Check score support, then open a FACETS-organized review from an explicit MML fit
#> 48 2. Draw the required native Wright map with facet SE or CI
#> 49 3. Add the FACETS-style ruler with rubric-labelled steps
#> 50 4. Review Infit by measure, adding persons explicitly when needed
#> 51 1. Fit the supported overlap model explicitly with MML in mfrmr
#> 52 2. State the ConQuest comparison scope before preparing external review
#> 53 State the FACETS relationship before using FACETS-style routes
#> 54 Translate FACETS direct and group anchor blocks
#> 55 Review anchor drift across forms, raters, or waves
#> 56 List fit measures and misfit flags by facet
#> 57 Explain FACETS df and ZSTD conversion
#> 58 Bring an external FACETS fit table into the review
#> 59 Review rating-scale categories, fair averages, and expected curves
#> 60 Review FACETS Table 14-style bias and interaction signals
#> 61 Draw a Wright map / variable map on the common logit scale
#> 64 Prepare a scoped ConQuest overlap case
#> 65 Compare extracted ConQuest tables after the external run
#> 66 State where mfrmr is less flexible than ConQuest
#> 70 Combine tables and plot data for reports
#> MainFunction
#> 1 describe_mfrm_data(data, person = ..., facets = ..., score = ...); fit <- fit_mfrm(data, person = ..., facets = ..., score = ..., method = "MML")
#> 2 summary(fit, profile = "fit", detail = "brief")
#> 3 review <- summary(fit, profile = "facets", detail = "brief"); res <- review$results
#> 4 plot(res, type = "wright", renderer = "native", show_ci = TRUE, top_n = Inf, preset = "publication")
#> 5 fit$prep$score_map; plot(res, type = "wright", renderer = "facets", category_labels = rubric_labels, show_ci = FALSE); plot(res, type = "fit_pathway", fit_stat = "Infit", include_person = TRUE, person_labels = "none")
#> 6 report <- mfrm_report(res); summary(report, view = "reader"); export_mfrm_results(res, preset = "starter")
#> 7 fit_mfrm(); diagnose_mfrm(); mfrm_results()
#> 8 mfrm_results()
#> 9 res <- mfrm_results(fit); launch_mfrmr_viewer(res)
#> 11 mfrm_results_interactive()
#> 12 res <- mfrm_results(fit, include = "standard"); launch_mfrmr_viewer(res)
#> 13 res <- mfrm_results(fit, include = "publication"); launch_mfrmr_viewer(res)
#> 14 res <- mfrm_results(fit, include = "validation"); launch_mfrmr_viewer(res)
#> 15 res <- mfrm_results(fit, include = "bias"); launch_mfrmr_viewer(res)
#> 16 res <- mfrm_results(fit, include = "misfit_review"); launch_mfrmr_viewer(res)
#> 17 res <- mfrm_results(fit, include = "linking"); launch_mfrmr_viewer(res)
#> 18 res <- mfrm_results(fit, include = c("publication", "bias", "misfit_review", "linking")); launch_mfrmr_viewer(res)
#> 19 fit_mfrm(data, person = ..., facets = "Item", score = ..., model = "RSM"); mfrm_results()
#> 21 mfrm_results(fit); plot(res, type = "wright"); plot(res, type = "qc")
#> 25 precision_review_report()
#> 26 facet_statistics_report()
#> 27 rating_scale_table(); category_structure_report(); category_curves_report()
#> 28 mfrm_results(fit, include = "bias"); estimate_bias(); analyze_dff(); bias_interaction_report()
#> 29 review_mfrm_anchors(); detect_anchor_drift(); build_linking_review()
#> 30 build_model_choice_review(); build_weighting_review(); compare_mfrm()
#> 31 build_summary_table_bundle()
#> 32 mfrm_report(); reporting_checklist(); build_apa_outputs()
#> 33 export_mfrm_results(); export_summary_appendix(); export_mfrm_bundle(); build_mfrm_manifest()
#> 34 run_mfrm_facets(); facets_output_file_bundle(); facets_output_contract_review()
#> 35 mfrm_results(fit, include = "linking"); plot(res, type = "anchors")
#> 36 make_anchor_table(); review_mfrm_anchors(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 37 detect_anchor_drift(list(Wave1 = fit1, Wave2 = fit2)); build_linking_review(drift = ...); plot_anchor_drift()
#> 38 build_equating_chain(list(Form1 = fit1, Form2 = fit2)); build_linking_review(chain = ...); plot_anchor_drift(type = "chain")
#> 39 build_mfrm_sim_spec(); simulate_mfrm_data(); extract_mfrm_sim_spec()
#> 40 evaluate_mfrm_design(); evaluate_mfrm_recovery(); assess_mfrm_recovery()
#> 41 evaluate_mfrm_diagnostic_screening(); summary(); plot(..., draw = FALSE); plot_data()
#> 42 summary(diag_eval); build_summary_table_bundle(diag_eval); export_summary_appendix(diag_eval)
#> 43 mfrm_network_analysis(); build_mfrm_network_review()
#> 44 build_peer_review_sim_spec(); build_peer_review_design_review(); build_mfrm_network_review(peer_review_design = ...)
#> 45 gpcm_capability_matrix()
#> 46 gpcm_runtime_guard_coverage()
#> 47 describe_mfrm_data(data, person = ..., facets = ..., score = ...); fit <- fit_mfrm(data, person = ..., facets = ..., score = ..., method = "MML"); review <- summary(fit, profile = "facets", detail = "brief"); res <- review$results
#> 48 plot(res, type = "wright", renderer = "native", show_ci = TRUE, top_n = Inf, preset = "publication")
#> 49 fit$prep$score_map; plot(res, type = "wright", renderer = "facets", category_labels = rubric_labels, show_ci = FALSE)
#> 50 plot(res, type = "fit_pathway", fit_stat = "Infit", include_person = TRUE, top_n_person = 12, person_labels = "none", facet_labels = "flagged")
#> 51 fit_lr <- fit_mfrm(data, person = "Person", facets = "Item", score = "Score", method = "MML", model = "RSM", population_formula = ~ X, person_data = person_data)
#> 52 mfrmr_output_guide("conquest"); build_conquest_overlap_bundle(fit_lr)
#> 53 facets_positioning_guide(); facets_feature_coverage(); run_mfrm_facets(); mfrmRFacets(); facets_output_file_bundle()
#> 54 review_mfrm_anchors(); make_anchor_table(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 55 anchor_to_baseline(); detect_anchor_drift(); build_equating_chain(); plot_anchor_drift()
#> 56 fit_measures_table(); facets_chisq_table(); displacement_table()
#> 57 facets_fit_df_guide(); diagnose_mfrm(fit_df_method = "both")
#> 58 read_facets_fit_table(); facets_fit_review(); plot(..., type = "df_sensitivity")
#> 59 rating_scale_table(); category_structure_report(); category_curves_report(); fair_average_table(); plot_fair_average()
#> 60 mfrm_results(fit, include = "bias"); estimate_bias(); bias_interaction_report(); bias_pairwise_report(); plot_bias_interaction()
#> 61 plot(fit, type = "wright"); plot_wright_unified(); plot_data(type = "wright")
#> 64 build_conquest_overlap_bundle()
#> 65 normalize_conquest_overlap_exports(); review_conquest_overlap()
#> 66 reporting_checklist(); reference_case_benchmark()
#> 70 build_summary_table_bundle(); build_visual_summaries()
#> GPCMStatus
#> 1 supported_with_caveat
#> 2 supported_with_caveat
#> 3 supported_with_caveat
#> 4 supported_with_caveat
#> 5 supported_with_caveat
#> 6 summary_appendix_supported; fit_bundle_supported_with_caveat
#> 7 supported_with_caveat
#> 8 supported_with_caveat
#> 9 viewer_only_uses_existing_results
#> 11 supported_with_caveat
#> 12 viewer_only_uses_existing_results
#> 13 supported_with_caveat
#> 14 supported_with_caveat
#> 15 supported_with_caveat
#> 16 supported_with_caveat
#> 17 anchor_readiness_supported; exploratory_linking_review_supported_with_caveat
#> 18 viewer_only_uses_existing_results
#> 19 rsm_recommended_for_ordinary_binary
#> 21 supported_with_caveat
#> 25 supported_with_caveat
#> 26 supported_with_caveat
#> 27 supported_with_caveat
#> 28 supported_with_caveat
#> 29 supported_with_caveat; exploratory_gpcm_linking_review
#> 30 supported_with_caveat
#> 31 supported_for_direct_outputs
#> 32 supported_with_caveat
#> 33 summary_appendix_supported; fit_bundle_supported_with_caveat
#> 34 graph_only_or_blocked_by_score_side_semantics
#> 35 anchor_readiness_supported; exploratory_linking_review_supported_with_caveat
#> 36 supported_with_caveat
#> 37 supported_with_caveat; exploratory_gpcm_linking_review
#> 38 supported_with_caveat; exploratory_gpcm_linking_review
#> 39 supported_with_caveat
#> 40 supported_with_caveat
#> 41 supported_with_caveat
#> 42 supported_with_caveat
#> 43 design_diagnostic_not_measurement_gate
#> 44 design_diagnostic_not_measurement_gate
#> 45 bounded_support_matrix
#> 46 out_of_scope_route_guidance
#> 47 supported_with_caveat
#> 48 supported_with_caveat
#> 49 supported_with_caveat
#> 50 supported_with_caveat
#> 51 blocked_for_gpcm; rsm_pcm_overlap_only
#> 52 blocked_for_gpcm; rsm_pcm_overlap_only
#> 53 graph_only_or_blocked_by_score_side_semantics
#> 54 supported_with_caveat
#> 55 supported_with_caveat; exploratory_gpcm_linking_review
#> 56 supported_with_caveat
#> 57 supported_with_caveat
#> 58 supported_with_caveat
#> 59 supported_with_caveat
#> 60 supported_with_caveat
#> 61 supported_with_caveat
#> 64 blocked_for_gpcm; rsm_pcm_overlap_only
#> 65 blocked_for_gpcm; rsm_pcm_overlap_only
#> 66 blocked_for_gpcm; rsm_pcm_overlap_only
#> 70 supported_for_direct_outputs
mfrmr_output_guide("simulation")[, c("Question", "Lifecycle")]
#> Question Lifecycle
#> 39 Generate planned, sparse, or peer-review response data advanced
#> 40 Evaluate design and recovery operating behavior advanced
#> 41 Screen diagnostic behavior under misspecification scenarios advanced
#> 42 Export simulation operating-characteristic tables for appendices advanced
mfrmr_output_guide("linking")[, c("Question", "MainFunction")]
#> Question
#> 35 Open first-screen anchor and linking readiness from an existing fit
#> 36 Review intended anchor and group-anchor tables before fitting
#> 37 Check drift across separately fitted waves or forms
#> 38 Build a screened equating chain across ordered calibrations
#> MainFunction
#> 35 mfrm_results(fit, include = "linking"); plot(res, type = "anchors")
#> 36 make_anchor_table(); review_mfrm_anchors(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 37 detect_anchor_drift(list(Wave1 = fit1, Wave2 = fit2)); build_linking_review(drift = ...); plot_anchor_drift()
#> 38 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
#> 47 1. Check score support, then open a FACETS-organized review from an explicit MML fit
#> 48 2. Draw the required native Wright map with facet SE or CI
#> 49 3. Add the FACETS-style ruler with rubric-labelled steps
#> 50 4. Review Infit by measure, adding persons explicitly when needed
#> 53 State the FACETS relationship before using FACETS-style routes
#> 54 Translate FACETS direct and group anchor blocks
#> 55 Review anchor drift across forms, raters, or waves
#> 56 List fit measures and misfit flags by facet
#> 57 Explain FACETS df and ZSTD conversion
#> 58 Bring an external FACETS fit table into the review
#> 59 Review rating-scale categories, fair averages, and expected curves
#> 60 Review FACETS Table 14-style bias and interaction signals
#> 61 Draw a Wright map / variable map on the common logit scale
#> 62 Check score support and rater response patterns before fitting claims
#> 63 Write residual and subset files for external review
#> MainFunction
#> 47 describe_mfrm_data(data, person = ..., facets = ..., score = ...); fit <- fit_mfrm(data, person = ..., facets = ..., score = ..., method = "MML"); review <- summary(fit, profile = "facets", detail = "brief"); res <- review$results
#> 48 plot(res, type = "wright", renderer = "native", show_ci = TRUE, top_n = Inf, preset = "publication")
#> 49 fit$prep$score_map; plot(res, type = "wright", renderer = "facets", category_labels = rubric_labels, show_ci = FALSE)
#> 50 plot(res, type = "fit_pathway", fit_stat = "Infit", include_person = TRUE, top_n_person = 12, person_labels = "none", facet_labels = "flagged")
#> 53 facets_positioning_guide(); facets_feature_coverage(); run_mfrm_facets(); mfrmRFacets(); facets_output_file_bundle()
#> 54 review_mfrm_anchors(); make_anchor_table(); fit_mfrm(anchors = ..., group_anchors = ...)
#> 55 anchor_to_baseline(); detect_anchor_drift(); build_equating_chain(); plot_anchor_drift()
#> 56 fit_measures_table(); facets_chisq_table(); displacement_table()
#> 57 facets_fit_df_guide(); diagnose_mfrm(fit_df_method = "both")
#> 58 read_facets_fit_table(); facets_fit_review(); plot(..., type = "df_sensitivity")
#> 59 rating_scale_table(); category_structure_report(); category_curves_report(); fair_average_table(); plot_fair_average()
#> 60 mfrm_results(fit, include = "bias"); estimate_bias(); bias_interaction_report(); bias_pairwise_report(); plot_bias_interaction()
#> 61 plot(fit, type = "wright"); plot_wright_unified(); plot_data(type = "wright")
#> 62 data_quality_report(); plot(..., type = "dashboard")
#> 63 write_mfrm_residual_file(); write_mfrm_subset_file(); facets_output_file_bundle()
mfrmr_output_guide("binary")[, c("Question", "MainFunction")]
#> Question
#> 19 Fit ordinary person-item binary responses
#> 20 Confirm the two-category score support
#> 21 Open the first-screen results for a binary Rasch run
#> MainFunction
#> 19 fit_mfrm(data, person = ..., facets = "Item", score = ..., model = "RSM"); mfrm_results()
#> 20 describe_mfrm_data(); fit$prep$score_map; summary(fit)$settings_overview
#> 21 mfrm_results(fit); plot(res, type = "wright"); plot(res, type = "qc")
mfrmr_output_guide("viewer")[, c("Question", "MainFunction")]
#> Question
#> 12 Open the standard first-screen viewer
#> 13 Prepare publication-oriented viewer sections
#> 14 Check validation, fit, and separation surfaces before reporting
#> 15 Inspect bias-screen prompts without choosing contrasts automatically
#> 16 Inspect pathway-map and row-level misfit prompts
#> 17 Inspect anchor and linking readiness
#> 18 Prepare a broad reviewer-facing viewer object
#> MainFunction
#> 12 res <- mfrm_results(fit, include = "standard"); launch_mfrmr_viewer(res)
#> 13 res <- mfrm_results(fit, include = "publication"); launch_mfrmr_viewer(res)
#> 14 res <- mfrm_results(fit, include = "validation"); launch_mfrmr_viewer(res)
#> 15 res <- mfrm_results(fit, include = "bias"); launch_mfrmr_viewer(res)
#> 16 res <- mfrm_results(fit, include = "misfit_review"); launch_mfrmr_viewer(res)
#> 17 res <- mfrm_results(fit, include = "linking"); launch_mfrmr_viewer(res)
#> 18 res <- mfrm_results(fit, include = c("publication", "bias", "misfit_review", "linking")); launch_mfrmr_viewer(res)
mfrmr_output_guide("response_time")[, c("Question", "MainFunction")]
#> Question
#> 24 Review response-time metadata as descriptive QC context
#> 69 Reuse response-time plot data for custom QC graphics
#> MainFunction
#> 24 response_time_review(); mfrm_results(include = "response_time", response_time = ...); plot_response_time_review(); plot_data_components()
#> 69 response_time_review(); plot_response_time_review(..., draw = FALSE); plot_data_components(); plot_data()
mfrmr_output_guide("beginner")[, c("Question", "MainFunction")]
#> Question
#> 1 1. Check score support and fit with explicit data roles
#> 2 2. Check convergence and fitted-model settings
#> 3 3. Build the comprehensive FACETS-organized review
#> 4 4. Create the required native Wright map with SE or CI
#> 5 5. Add optional FACETS-style and Infit pathway views
#> 6 6. Review, report, and export the completed results
#> MainFunction
#> 1 describe_mfrm_data(data, person = ..., facets = ..., score = ...); fit <- fit_mfrm(data, person = ..., facets = ..., score = ..., method = "MML")
#> 2 summary(fit, profile = "fit", detail = "brief")
#> 3 review <- summary(fit, profile = "facets", detail = "brief"); res <- review$results
#> 4 plot(res, type = "wright", renderer = "native", show_ci = TRUE, top_n = Inf, preset = "publication")
#> 5 fit$prep$score_map; plot(res, type = "wright", renderer = "facets", category_labels = rubric_labels, show_ci = FALSE); plot(res, type = "fit_pathway", fit_stat = "Infit", include_person = TRUE, person_labels = "none")
#> 6 report <- mfrm_report(res); summary(report, view = "reader"); export_mfrm_results(res, preset = "starter")
mfrmr_output_guide("psychometric")[, c("Question", "DecisionBoundary")]
#> Question
#> 22 Document the model setup and run settings
#> 23 Check whether data were filtered, dropped, or remapped
#> 25 Decide how strongly precision claims can be phrased
#> 26 Summarize facet variability, separation, and measurability
#> 27 Review category functioning and expected-score curves
#> 29 Review anchors, drift, and linking readiness
#> 30 Compare equal-weighting and bounded-GPCM routes
#> 37 Check drift across separately fitted waves or forms
#> 38 Build a screened equating chain across ordered calibrations
#> DecisionBoundary
#> 22 Specialist follow-up: inspect the source object and help page before treating output as report evidence.
#> 23 Specialist follow-up: inspect the source object and help page before treating output as report evidence.
#> 25 Precision and separation evidence are not inter-rater agreement or standalone validity proof.
#> 26 Specialist follow-up: inspect the source object and help page before treating output as report evidence.
#> 27 Specialist follow-up: inspect the source object and help page before treating output as report evidence.
#> 29 Anchor and linking evidence support scale-maintenance review; drift and equating claims require explicit multi-fit wave or form designs.
#> 30 Specialist follow-up: inspect the source object and help page before treating output as report evidence.
#> 37 Anchor and linking evidence support scale-maintenance review; drift and equating claims require explicit multi-fit wave or form designs.
#> 38 Anchor and linking evidence support scale-maintenance review; drift and equating claims require explicit multi-fit wave or form designs.