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_designattribute, the attribute itself, or a data frame with sparse design columns such asSparseDesignActive,DesignDensity,MinCommonPersonsPerRaterPair, andZeroCommonRaterPairs.- peer_review_design
Optional peer-review design metadata. Supply either the generated data frame that carries the
mfrm_peer_review_designattribute, the attribute itself, or itsoverviewdata 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 underlyingigraphobject in the nestedsource_networkbundle.
Value
A bundle of class mfrm_network_review containing:
overview: connectedness and front-door review statusnetwork_summary: graph-level metrics frommfrm_network_analysis()facet_summary: facet-level vulnerability summariestop_central_nodes,top_cut_nodes,top_bridge_edges: follow-up rowssparse_review: optional sparse-design linking reviewpeer_review: optional peer-review assignment and linkage diagnosticsreporting_map: boundary between MFRM, design network, sparse design, peer-review design, and rater-effect network routes
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.
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.
Examples
# \donttest{
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
method = "JML", maxit = 30
)
#> Warning: Optimization convergence review did not produce an inference-ready numerical solution (code = 1, status = iteration_limit). Optimizer reached the iteration limit before the terminal gradient became small enough for review-only acceptance. Inspect the model specification, data support, and starting values. Do not interpret estimates until the review is resolved.
if (requireNamespace("igraph", quietly = TRUE)) {
review <- build_mfrm_network_review(fit)
summary(review)
build_summary_table_bundle(review)
}
#> mfrmr Summary Table Bundle
#>
#> Overview
#> Title SourceClass SummaryClass
#> Network Review Tables mfrm_network_review summary.mfrm_network_review
#> TablesAvailable TablesReturned AppendixPreset
#> 12 10 none
#>
#> Table index
#> Table Rows Cols Role
#> overview 1 11 network_review_overview
#> network_summary 1 13 network_design_summary
#> facet_summary 3 8 network_facet_vulnerability
#> top_central_nodes 10 11 network_central_nodes
#> top_cut_nodes 0 11 network_articulation_nodes
#> top_bridge_edges 0 12 network_bridge_edges
#> reporting_map 5 4 reporting_map
#> caveats 1 3 analysis_caveats
#> notes 4 1 interpretation_notes
#> settings 5 2 review_settings
#> Description
#> Front-door connectedness and design-network review status.
#> Graph-level connectedness, density, articulation-point, and bridge-edge metrics.
#> Facet-level aggregation of node centrality and design-link vulnerability.
#> Highest-betweenness design-network nodes for follow-up inspection.
#> Articulation-point rows whose removal would fragment the design graph.
#> Bridge-edge rows indicating one-link dependencies between graph regions.
#> Map separating MFRM measurement, design-network review, sparse-design review, peer-review design, and rater-effect network routes.
#> Interpretation caveats for network-design diagnostics.
#> Compact interpretation notes for network-design review.
#> Settings and provenance recorded by build_mfrm_network_review().
#>
#> Plot index
#> Table PlotReady NumericColumns DefaultPlotTypes
#> overview TRUE 7 numeric_profile, first_numeric
#> network_summary TRUE 12 numeric_profile, first_numeric
#> facet_summary TRUE 7 numeric_profile, first_numeric
#> top_central_nodes TRUE 7 numeric_profile, first_numeric
#> top_cut_nodes FALSE 7
#> top_bridge_edges FALSE 5
#> reporting_map FALSE 0
#> caveats FALSE 0
#> notes FALSE 0
#> settings FALSE 0
#>
#> Notes
#> - MFRM estimates remain the measurement-model results; network rows summarize observed design links.
#> - Articulation points and bridge edges identify levels or links whose removal would fragment the co-observation graph.
#> - Sparse-design review rows, when supplied, report planned-missingness and rater-link diagnostics rather than recovery or fit gates.
#> - Peer-review design rows, when supplied, report assignment structure and reviewer linkage rather than reviewer quality or fairness.
#> - 2 empty table(s) were omitted from `tables`; use `include_empty = TRUE` to retain them.
# }
