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Summarize a data-description object

Source: R/api-estimation.R
summary.mfrm_data_description.Rd

Summarize a data-description object

Usage

# S3 method for class 'mfrm_data_description'
summary(object, digits = 3, top_n = 10, ...)

Arguments

object

Output from describe_mfrm_data().

digits

Number of digits for numeric rounding.

top_n

Maximum rows shown in preview blocks.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_data_description.

  • overview: design/sample counts

  • missing: top columns by missingness

  • score_distribution: compact score-usage table, including zero-count categories retained by the prepared score support

  • facet_overview: facet-level coverage summary

  • structural_missingness: declared assignment coverage summary; status is "not_declared" when no assignment roster was supplied

  • structural_level_coverage: expected versus observed level counts

  • design_connectivity: Person-facet component counts for observed and declared-expected designs

  • design_components: component sizes and facet-level labels; person labels are suppressed unless explicitly requested in describe_mfrm_data()

  • linkage_summary: sparse-support and shared-person counts by facet

  • duplicate_cell_summary: aggregate duplicate-cell counts

  • agreement: selected-facet agreement summary when available

  • agreement_settings: selected scorer facet, matching context, and status

  • row_retention: row counts before and after preparation filters

  • preparation_notes: structured preparation notes retained from describe_mfrm_data()

  • reporting_map: manuscript-oriented guide to what is covered here versus which companion outputs should be consulted

  • caveats: structured warning/review rows for score-support issues; print(summary(ds)) shows a compact Caveats block when rows are present

Details

This summary is intended as a compact pre-fit quality snapshot for manuscripts and analysis logs.

Interpreting output

Recommended read order:

  • overview: sample size, persons/facets/categories.

  • missing: missingness hotspots by selected input columns.

  • score_distribution: category usage balance.

  • notes / printed Caveats: retained zero-count score categories and related score-support caveats; intermediate unused categories should be treated as threshold-functioning warnings before model fitting.

  • facet_overview: coverage per facet (minimum/maximum weighted counts).

  • agreement: observed-score agreement for the selected scorer facet (when available).

Very low MinWeightedN in facet_overview is a practical warning for unstable downstream facet estimates.

Typical workflow

  1. Run describe_mfrm_data() on raw long-format data.

  2. Inspect summary(ds) before model fitting.

  3. Resolve sparse/missing issues, then run fit_mfrm().

See also

describe_mfrm_data(), summary.mfrm_fit()

Examples

toy <- load_mfrmr_data("example_core")
ds <- describe_mfrm_data(toy, "Person", c("Rater", "Criterion"), "Score")
summary(ds)
#> mfrm Data Description Summary
#> 
#> Overview
#>  Observations TotalWeight Persons Facets Categories RatingMin RatingMax
#>           768         768      48      2          4         1         4
#>  RatingRangeSource RatingMinSource RatingMaxSource
#>           observed        observed        observed
#> 
#> Missing by column
#>     Column Missing
#>  Criterion       0
#>     Person       0
#>      Rater       0
#>      Score       0
#> 
#> Planned assignment coverage
#>        Status ExpectedCells ObservedCells MatchedCells MissingExpectedCells
#>  not_declared            NA           768           NA                   NA
#>  UnexpectedObservedCells CoverageRate ExpectedOnlyPersons UnexpectedPersons
#>                       NA           NA                  NA                NA
#> 
#> Person-facet connectivity
#>     Basis     Facet PersonNodes FacetLevelNodes Edges Components
#>  observed     Rater          48               4   192          1
#>  observed Criterion          48               4   192          1
#>  LargestComponentPersons LargestComponentLevels LargestComponentPercent
#>                       48                      4                     100
#>                       48                      4                     100
#>  Connected
#>       TRUE
#>       TRUE
#> 
#> Facet linkage support
#>      Facet Levels Persons MinPersonsPerLevel MedianPersonsPerLevel
#>      Rater      4      48                 48                    48
#>  Criterion      4      48                 48                    48
#>  MinLevelsPerPerson MedianLevelsPerPerson LinkingPersons LinkingPersonRate
#>                   4                     4             48                 1
#>                   4                     4             48                 1
#>  SingleLevelPersons SparseLevels SparseLevelThreshold
#>                   0            0                    2
#>                   0            0                    2
#> 
#> Score distribution
#>  Score RawN WeightedN Percent
#>      1  139       139  18.099
#>      2  241       241  31.380
#>      3  252       252  32.812
#>      4  136       136  17.708
#> 
#> Facet coverage
#>      Facet Levels TotalWeightedN MeanWeightedN MinWeightedN MaxWeightedN
#>  Criterion      4            768           192          192          192
#>      Rater      4            768           192          192          192
#> 
#> Observed agreement by Rater
#>  RaterFacet Raters Pairs Contexts TotalPairs OpportunityCount ExactAgreements
#>       Rater      4     6      192       1152             1152             417
#>  ExpectedAgreements ExactAgreement ExpectedExactAgreement
#>                  NA          0.362                     NA
#>  AgreementMinusExpected AdjacentAgreements AdjacentAgreement MeanAbsDiff
#>                      NA                956              0.83       0.826
#>  MeanCorr
#>     0.378
#> 
#> Paper reporting map
#>                                 Area  CoveredHere
#>               Sample / design counts          yes
#>                   Missingness review          yes
#>          Planned assignment coverage not declared
#>            Person-facet connectivity          yes
#>  Score usage / category distribution          yes
#>                       Facet coverage          yes
#>                Rater-facet agreement          yes
#>     Fit / reliability / residual PCA           no
#>                                                 CompanionOutput
#>                                summary(describe_mfrm_data(...))
#>                                summary(describe_mfrm_data(...))
#>       data_review$structural_missingness$missing_expected_cells
#>                                   data_review$design_components
#>                                summary(describe_mfrm_data(...))
#>                                summary(describe_mfrm_data(...))
#>  summary(describe_mfrm_data(...)) / plot_interrater_agreement()
#>                                     summary(diagnose_mfrm(fit))
#> 
#> Notes
#>  - No missing values were detected in selected input columns.
#>  - Structural missingness was not assessed because `expected_design` was not supplied. Absent rows cannot be distinguished from cells that were never assigned.