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,
  include_fixed = FALSE
)

Arguments

fit	Output from fit_mfrm().
data	Optional raw data frame used for row-level audit.
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.
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 = "row_audit", "category_counts", "missing_rows").

Value

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

Interpreting output

• summary: retained/dropped row overview.

• row_audit: reason-level breakdown for data issues.

• category_counts: post-filter category usage.

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

Typical workflow

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

2. Check row-audit and missing/unknown element sections.

3. Resolve issues 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 = 25)
out <- data_quality_report(
  fit, data = toy, person = "Person",
  facets = c("Rater", "Criterion"), score = "Score"
)
summary(out)
p_dq <- plot(out, draw = FALSE)
class(p_dq)

mfrmr documentation built on March 31, 2026, 1:06 a.m.