mfrm_network_analysis: Analyze the MFRM design network

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

Analyze the MFRM design network

Description

Analyze the MFRM design network

Usage

mfrm_network_analysis(
  fit,
  diagnostics = NULL,
  top_n_subsets = NULL,
  min_observations = 0,
  include_graph = FALSE
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
top_n_subsets	Optional maximum number of connected-subset rows to retain before constructing the graph.
min_observations	Minimum observations required to keep a subset row.
include_graph	Logical; if TRUE, include the underlying igraph object in the returned bundle. Defaults to FALSE so outputs remain easy to serialize.

Details

mfrm_network_analysis() treats the person/facet-level observation design as an undirected weighted graph. Nodes are person or facet levels; edges connect levels that co-occur in at least one observed rating; edge weights are co-observation counts. The resulting network metrics are design diagnostics, not psychometric measures of person ability or rater quality. plot(net, type = "centrality"), plot(net, type = "facet_summary"), and plot(net, type = "network") provide immediate visual checks; use draw = FALSE to extract reusable plot data.

The most useful review columns are:

• Components: more than one component means the design has disconnected measurement subsets.

• IsArticulationPoint: a node whose removal would increase disconnectedness.

• IsBridge: an edge whose removal would increase disconnectedness.

• Betweenness: a routing-dependence indicator; high values identify levels that carry many shortest paths through the design graph.

In incomplete rater-mediated designs, these graph summaries help identify fragile linking structures before interpreting facet measures or planning additional data collection.

Value

A bundle of class mfrm_network_analysis containing:

• summary: graph-level connectedness and vulnerability metrics

• node_metrics: node-level degree, strength, centrality, and cutpoint flags

• edge_metrics: edge-level weights, betweenness, and bridge flags

• facet_summary: facet-level aggregation of node/bridge indicators

• cut_nodes: articulation-point rows from node_metrics

• bridge_edges: bridge rows from edge_metrics

References

• McEwen, M. R. (2015). Development of a Software Prototype for Generating and Classifying Incomplete Many-Facet-Rasch Model Rating Designs. Brigham Young University.

• Csardi, G., Nepusz, T., Traag, V., Horvat, S., Zanini, F., Noom, D., & Muller, K. (2026). igraph: Network Analysis and Visualization.

See Also

subset_connectivity_report(), diagnose_mfrm(), 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 = 30)
if (requireNamespace("igraph", quietly = TRUE)) {
  net <- mfrm_network_analysis(fit)
  net$summary
  head(net$node_metrics)
  net$cut_nodes
  plot(net, type = "centrality", draw = FALSE)
}

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