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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

  • agreement: inter-rater agreement summary when available

  • 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 inter-rater agreement (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
#> 
#> 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
#> 
#> Inter-rater agreement
#>  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
#>  Score usage / category distribution         yes
#>                       Facet coverage         yes
#>                Inter-rater agreement         yes
#>     Fit / reliability / residual PCA          no
#>                                                 CompanionOutput
#>                                summary(describe_mfrm_data(...))
#>                                summary(describe_mfrm_data(...))
#>                                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.