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facets_positioning_guide() gives user-facing wording for the relationship between mfrmr and FACETS. Use it when a report, migration note, or methods appendix must make clear that mfrmr is not a FACETS numerical clone.

Usage

facets_positioning_guide()

Value

A data.frame with columns:

  • Topic

  • Position

  • RecommendedWording

  • PrimaryRoute

Details

The guide separates five ideas that are easy to conflate:

  • estimation authority: fitted values come from mfrmr unless external FACETS output is explicitly supplied;

  • compatibility purpose: FACETS-style names and files are transition, handoff, and report-organization surfaces;

  • external comparison: FACETS comparisons require a supplied external table and should separate MnSq differences from df/ZSTD convention differences;

  • current model boundary: one response-model family and one observed score scale are used per fit; mixed families, multiple independent scales, general threshold anchoring, and fixed-calibration scoring are not part of the current public estimator;

  • extension surface: native R tables, plot data, GPCM diagnostics, network views, and G/D-study helpers are package extensions, not promises of FACETS menu-level reproduction.

Examples

facets_positioning_guide()
#>                                    Topic
#> 1                   Estimation authority
#> 2                  Compatibility purpose
#> 3             External FACETS comparison
#> 4 Current model and calibration boundary
#> 5              Reporting source of truth
#> 6                Extension beyond FACETS
#>                                                                                                                                                                                                           Position
#> 1                                                                                                              mfrmr estimates are package-native; FACETS-style names do not mean that FACETS estimated the model.
#> 2                                                                           FACETS-style wrappers, table labels, and files support transition, handoff, and report organization, not optimizer-level reproduction.
#> 3                                                                                                                     Numerical comparison requires an explicit external FACETS output table supplied by the user.
#> 4 Each fit uses one response-model family and one observed score scale; mixed families, multiple independent scales, general threshold anchors, and fixed-calibration scoring are not current public capabilities.
#> 5                                                                                                        Inference and reporting should be based on native fit, diagnostics, review, table, and plot-data objects.
#> 6                                                                                                     GPCM, D-study, network, and reusable visualization data are extension routes rather than FACETS menu clones.
#>                                                                                                                                   RecommendedWording
#> 1                                                The model was estimated with mfrmr; FACETS-style output names are used only to organize the report.
#> 2                      FACETS-style outputs were generated for handoff or reader familiarity; they are not evidence of FACETS numerical equivalence.
#> 3                                  When external FACETS output is supplied, compare MnSq first and report df/ZSTD convention sensitivity separately.
#> 4 Describe mfrmr as a native R RSM/PCM analysis, diagnostic, and reporting environment, not as a general FACETS operational-calibration replacement.
#> 5                                                         Report estimates, standard errors, fit summaries, and plots from documented mfrmr objects.
#> 6                                                             Use package-native extensions as additional evidence and label them as mfrmr analyses.
#>                                                                                PrimaryRoute
#> 1                                        fit_mfrm(); diagnose_mfrm(); reporting_checklist()
#> 2                 facets_feature_coverage(); run_mfrm_facets(); facets_output_file_bundle()
#> 3   read_facets_fit_table(); facets_fit_review(); fit_measures_table(df_sensitivity = TRUE)
#> 4                               fit_mfrm(); facets_feature_coverage(); mfrmr_output_guide()
#> 5                       build_summary_table_bundle(); build_visual_summaries(); plot_data()
#> 6 gpcm_capability_matrix(); mfrm_d_study(); mfrm_network_analysis(); plot_data_components()