data_quality_report: Build a data quality summary report (preferred alias)

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

Build a data quality summary report (preferred alias)

Description

Build a data quality summary report (preferred alias)

Usage

data_quality_report(
  fit,
  data = NULL,
  person = NULL,
  facets = NULL,
  score = NULL,
  weight = NULL,
  min_category_count = 10,
  dominant_category_cutoff = 0.95,
  include_fixed = FALSE
)

Arguments

fit	Output from fit_mfrm().
data	Optional raw data frame used for row-level review.
person	Optional person column name in data.
facets	Optional facet column names in data.
score	Optional score column name in data.
weight	Optional weight column name in data.
min_category_count	Minimum raw or weighted count used to label a non-zero facet-level score category as sparse. Default 10.
dominant_category_cutoff	Proportion in ⁠(0, 1]⁠ used to flag a facet level whose responses are dominated by one score category. Default 0.95.
include_fixed	If TRUE, include a legacy-compatible fixed-width text block.

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_data_quality (type = "dashboard", "quality_flags", "row_review", "category_counts", "score_support", "facet_category_usage", "facet_response_patterns", "score_map", "missing_rows").

Value

A named list with data-quality report components. Class: mfrm_data_quality.

Interpreting output

• summary: retained/dropped row overview.

• quality_overview: area-level QC status for rows, score support, facet-category use, and design matching.

• quality_flags: prioritized QC flags with counts and recommended next actions. This is not an item/person/rater table.

• row_review: reason-level breakdown for data issues.

• category_counts: post-filter category usage, including retained zero-count score-support categories.

• score_support_review: quick view of zero-count boundary/intermediate categories and their threshold-functioning caveats.

• category_usage_by_facet: facet-level category counts over the retained score support.

• category_usage_summary: per-facet-level zero/sparse category summary.

• facet_response_patterns: facet-level response-pattern summaries, including single-category and dominant-category use.

• caveats: user-facing score-support warnings, including cases where non-consecutive original labels such as ⁠1, 2, 4, 5⁠ were recoded because keep_original = FALSE.

• score_map: original-to-internal score mapping used when labels are recoded.

• unknown_elements: facet levels in raw data but not in fitted design.

Typical workflow

1. Run data_quality_report(...) with raw data.

2. Check summary(out) and plot(out, type = "dashboard"), then inspect quality_flags, score-support, score-map, facet-response-pattern, and missing/unknown element sections as needed.

3. Resolve missing values, score-support gaps, and sparse categories before final estimation/reporting.

See Also

fit_mfrm(), describe_mfrm_data(), specifications_report(), mfrmr_reports_and_tables, mfrmr_compatibility_layer

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
out <- data_quality_report(
  fit, data = toy, person = "Person",
  facets = c("Rater", "Criterion"), score = "Score"
)
summary(out)
p_dq <- plot(out, draw = FALSE)
p_dq$data$plot

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