Summarize a data-description object

Read a compact summary of the checks from describe_mfrm_data() before fitting a model. Save it with review <- summary(data_review) and select the tables you need with $, as in the example.

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

• notes: plain-language explanations of missingness, preparation, score support, and design-review findings

Details

data_review holds the complete data checks; review holds summary tables and notes. Neither object contains model estimates. The default top_n = 10 limits the missing-column and score-distribution previews; use data_review$missing_by_column and data_review$score_distribution to see the complete tables.

Interpreting output

Recommended read order:

• overview: retained ratings (Observations), persons, facets, and categories. Compare input and retained Rows in row_retention and investigate unexpected DroppedRows. CategoryPolicy and ScoreRecoded distinguish the selected policy from actual changes to score values. Older results without the policy record report "not_recorded"; an absent map gives NA for recoding.

• missing: input NA counts by column. This table is named missing_by_column in the original data_review object. Declared missing-code replacements and invalid score text can cause additional row loss; inspect data_review$missing_recoding and preparation_notes.

• score_distribution: category usage balance.

• notes / printed Caveats: retained zero-count score categories and related score-support caveats. With keep_original = TRUE, a retained unused internal category stops fitting; review the data and rubric first.

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

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

• design_connectivity: check for more than one observed component before comparing facet levels. structural_missingness reports planned omissions only when an assignment roster was supplied; not_declared does not mean that no ratings are missing.

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 review <- summary(data_review) before model fitting.

3. Correct input issues and repeat the review, then pass the corrected rating data to fit_mfrm() with the same preparation settings.

See also

describe_mfrm_data(), summary.mfrm_fit()

Examples

library(mfrmr)
ratings <- load_mfrmr_data("example_operational")

# Demonstrate two missing scores in a copy of the example data
ratings$Score[1:2] <- NA
data_review <- describe_mfrm_data(
  data = ratings,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  rating_min = 1,
  rating_max = 4,
  category_policy = "preserve"
)
review <- summary(data_review)
review$row_retention # 282 input rows, 280 retained rows
#>                             Stage Rows DroppedRows
#> 1          input_selected_columns  282           0
#> 2 after_missing_and_weight_filter  280           2
#>                            DroppedReason
#> 1                                       
#> 2 missing values or non-positive weights
review$missing       # Score has 2 missing input values
#>      Column Missing
#> 1     Score       2
#> 2 Criterion       0
#> 3    Person       0
#> 4     Rater       0
review$overview      # Counts describe the retained ratings
#>   Observations TotalWeight Persons Facets Categories RatingMin RatingMax
#> 1          280         280      48      2          4         1         4
#>   RatingRangeSource RatingMinSource RatingMaxSource CategoryPolicy ScoreRecoded
#> 1          declared        declared        declared       preserve        FALSE
review$notes         # Explanations to read before fitting
#> [1] "Missing values were detected in one or more input columns."                                                                                                                                               
#> [2] "Structural missingness was not assessed because `expected_design` was not supplied. Absent rows cannot be distinguished from cells that were never assigned."                                             
#> [3] "Dropped 2 row(s) with missing values or non-positive weights before estimation. Pass `missing_codes = ...` to recode user-specified missing markers, or pre-process upstream if you need to keep the row."

# The original description retains the full missingness table
data_review$missing_by_column
#> # A tibble: 4 × 2
#>   Column    Missing
#>   <chr>       <int>
#> 1 Person          0
#> 2 Rater           0
#> 3 Criterion       0
#> 4 Score           2
# Investigate missingness before using ratings in fit_mfrm()