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Build an MFRM network review

Source: R/api-reports.R
build_mfrm_network_review.Rd

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.

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

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.

See also

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

Examples

if (FALSE) { # \dontrun{
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)
}
} # }