summary.mfrm_data_description: Summarize a data-description object

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Summarize a data-description object

Description

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.

Details

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

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

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)

mfrmr documentation built on June 13, 2026, 1:07 a.m.