subset_connectivity_report: Build a subset connectivity report (preferred alias)

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

Build a subset connectivity report (preferred alias)

Description

Build a subset connectivity report (preferred alias)

Usage

subset_connectivity_report(
  fit,
  diagnostics = NULL,
  top_n_subsets = NULL,
  min_observations = 0
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
top_n_subsets	Optional maximum number of subset rows to keep.
min_observations	Minimum observations required to keep a subset row.

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_subset_connectivity (type = "subset_observations", "facet_levels", or "linking_matrix" / "coverage_matrix" / "design_matrix").

Value

A named list with subset-connectivity components. Class: mfrm_subset_connectivity.

Interpreting output

• summary: number and size of connected subsets.

• subset table: whether data are fragmented into disconnected components.

• facet-level columns: where connectivity bottlenecks occur.

Typical workflow

1. Run subset_connectivity_report(fit).

2. Confirm near-single-subset structure when possible.

3. Use results to justify linking/anchoring strategy.

See Also

diagnose_mfrm(), measurable_summary_table(), data_quality_report(), mfrmr_linking_and_dff, mfrmr_visual_diagnostics

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
out <- subset_connectivity_report(fit)
summary(out)
p_sub <- plot(out, draw = FALSE)
p_design <- plot(out, type = "design_matrix", draw = FALSE)
class(p_sub)
class(p_design)
out$summary[, c("Subset", "Observations", "ObservationPercent")]

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