build_mfrm_network_review: Build an MFRM network review

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

Build an MFRM network review

Description

Build an MFRM network review

Usage

build_mfrm_network_review(
  fit,
  diagnostics = NULL,
  sparse_design = NULL,
  peer_review_design = NULL,
  top_n_subsets = NULL,
  min_observations = 0,
  top_n = 10,
  include_graph = FALSE
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
sparse_design	Optional sparse-design metadata. Supply either the generated data frame that carries the mfrm_sparse_design attribute, the attribute itself, or a data frame with sparse design columns such as SparseDesignActive, DesignDensity, MinCommonPersonsPerRaterPair, and ZeroCommonRaterPairs.
peer_review_design	Optional peer-review design metadata. Supply either the generated data frame that carries the mfrm_peer_review_design attribute, the attribute itself, or its overview data frame.
top_n_subsets	Optional maximum number of connected-subset rows to retain before constructing the graph; passed to mfrm_network_analysis().
min_observations	Minimum observations required to keep a subset row; passed to mfrm_network_analysis().
top_n	Number of central/cut/bridge rows to retain in the review.
include_graph	Logical; if TRUE, keep the underlying igraph object in the nested source_network bundle.

Details

build_mfrm_network_review() is a synthesis layer over mfrm_network_analysis(). It keeps the measurement model and graph view in separate lanes: MFRM estimates remain the measurement results, while the network review summarizes co-observation connectedness and linking vulnerability in the observed design. This is especially useful for sparse or incomplete rater-mediated designs, where common-person links, connected subsets, articulation points, and bridge edges can explain why an otherwise estimable model depends on fragile design links.

The review status is deliberately conservative and descriptive. It is not a literature-derived adequacy cut point for fit, separation, recovery, or rater quality. Use it to decide which design links, anchors, or additional observations need inspection before making common-scale claims.

Value

A bundle of class mfrm_network_review containing:

• overview: connectedness and front-door review status

• network_summary: graph-level metrics from mfrm_network_analysis()

• facet_summary: facet-level vulnerability summaries

• top_central_nodes, top_cut_nodes, top_bridge_edges: follow-up rows

• sparse_review: optional sparse-design linking review

• peer_review: optional peer-review assignment and linkage diagnostics

• reporting_map: boundary between MFRM, design network, sparse design, peer-review design, and rater-effect network routes

References

• Wind, S. A., & Jones, E. (2018). The stabilizing influences of linking set size and model-data fit in sparse rater-mediated assessment networks. Educational and Psychological Measurement. doi:10.1177/0013164417703733.

• Wind, S. A., Jones, E., & Grajeda, S. (2023). Does sparseness matter? Examining the use of generalizability theory and many-facet Rasch measurement in sparse rating designs. Applied Psychological Measurement, 47(5-6), 351-364. doi:10.1177/01466216231182148.

• DeMars, C. E., Shapovalov, Y. A., & Hathcoat, J. D. (2023). Many-Facet Rasch Designs: How Should Raters be Assigned to Examinees? NCME presentation.

See Also

mfrm_network_analysis(), subset_connectivity_report(), build_summary_table_bundle(), rater_network_analysis(), rater_halo_network_analysis()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
if (requireNamespace("igraph", quietly = TRUE)) {
  review <- build_mfrm_network_review(fit)
  summary(review)
  build_summary_table_bundle(review)
}

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