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Build a measurable-data summary

Source: R/api-tables.R
measurable_summary_table.Rd

Build a measurable-data summary

Usage

measurable_summary_table(fit, diagnostics = NULL)

Arguments

fit

Output from fit_mfrm().

diagnostics

Optional output from diagnose_mfrm(). Must match this fitted analysis; see mfrmr_compatibility_layer for migration of saved diagnostics.

Value

A named list with:

  • summary: one-row measurable-data summary

  • facet_coverage: per-facet coverage summary

  • category_stats: category-level usage/fit summary

  • subsets: subset summary table (when available)

Details

This helper consolidates measurable-data diagnostics into a dedicated report bundle: run-level summary, facet coverage, category usage, and subset (connected-component) information.

summary(t5) is supported through summary(). plot(t5) is dispatched through plot() for class mfrm_measurable (type = "facet_coverage", "category_counts", "subset_observations").

Interpreting output

  • summary: overall measurable design status.

  • facet_coverage: spread/precision by facet.

  • category_stats: category usage and fit context.

  • subsets: connectivity diagnostics (fragmented subsets reduce comparability).

Typical workflow

  1. Run measurable_summary_table(fit).

  2. Check summary(t5) for subset/connectivity warnings.

  3. Use plot(t5, ...) to inspect facet/category/subset views.

Further guidance

For a plot-selection guide and a longer walkthrough, see mfrmr_visual_diagnostics and vignette("mfrmr-visual-diagnostics", package = "mfrmr").

Output columns

The summary data.frame (one row) contains:

Observations, TotalWeight

Total observations and summed weight.

Persons, Facets, Categories

Design dimensions.

ConnectedSubsets

Number of connected subsets.

LargestSubsetObs, LargestSubsetPct

Largest subset coverage.

The facet_coverage data.frame contains:

Facet

Facet name.

Levels

Number of estimated levels.

MeanSE

Mean standard error across levels.

MeanInfit, MeanOutfit

Mean fit statistics across levels.

MinEstimate, MaxEstimate

Measure range for this facet.

The category_stats data.frame contains:

Category

Score category value.

Count, Percent

Observed count and percentage.

Infit, Outfit

Descriptive category residual mean squares.

InfitZSTD, OutfitZSTD

Unavailable (NA); conditioning on the observed category does not preserve a universal unit-residual reference.

ExpectedCount, DiffCount, LowCount

Expected-observed comparison and low-count flag.

See also

diagnose_mfrm(), rating_scale_table(), describe_mfrm_data(), mfrmr_visual_diagnostics

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("measurable_summary_table", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# Saved synthetic JML/RSM analysis of example_core.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "core-jml.rds", package = "mfrmr"))
fit <- example_fit$fit
t5 <- measurable_summary_table(fit)
summary(t5)
p_t5 <- plot(t5, draw = FALSE)
p_t5$data$plot
} # }