Build a subset connectivity report (preferred alias)

Usage

subset_connectivity_report(
  fit = NULL,
  diagnostics = NULL,
  top_n_subsets = NULL,
  min_observations = 0,
  data = NULL,
  person = NULL,
  facets = NULL,
  score = NULL,
  basis = c("observed", "planned"),
  weight = NULL
)

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.
data: Optional raw assignment/rating data, supplied instead of fit.
person, facets: Names of the person column and one or more facet columns in data. Include only Rater in facets to inspect person-rater links without a common Criterion node obscuring separate panels.
score: Numeric score column for basis = "observed". Recode study-specific missing markers to NA first; no category recoding or estimation is done.
basis: "observed" includes rows with complete identifiers and nonmissing finite scores; "planned" includes complete assignment rows without scores. Planned networks require score = NULL and weight = NULL.
weight: Optional numeric weight column for observed raw data. Missing and zero-weight rows do not create edges. Edge weights still count rows, not sampling weights. Negative or infinite weights are refused.

Value

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

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" / "network"). The network route returns reusable node and edge tables with draw = FALSE; drawing uses igraph when available.

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

Run subset_connectivity_report(fit).
Inspect graph coverage alongside identification_audit. A common criterion can connect panels whose person/rater locations remain confounded.
Use the model rank and population assumptions to assess linking; a single co-observation component is not a FACETS identification subset.

Before fitting

Supply data, person, facets and score instead of fit. Known levels from incomplete/unscored rows remain as nodes, including isolates. A planned assignment roster can be examined with basis = "planned"; absent assignments cannot be reconstructed from an observed-only file. row_usage records each input row's inclusion and first exclusion reason. With a fit, only its retained rows are available here; review original missing assignments before fitting. No prefit result supplies an identification audit or model-readiness decision. settings$is_disjoint describes the source graph, even when subset filters omit components; compare source/retained subset counts. Filters do not establish linking. See online networks and reporting guide for a panel example.

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("subset_connectivity_report", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# Saved synthetic JML/RSM analysis of example_core.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "core-jml.rds", package = "mfrmr"))
fit <- example_fit$fit
out <- subset_connectivity_report(fit)
summary(out)
p_sub <- plot(out, draw = FALSE)
p_design <- plot(out, type = "design_matrix", draw = FALSE)
p_net <- plot(out, type = "network", draw = FALSE)
p_sub$data$plot
p_design$data$plot
p_net$data$edges
out$summary[, c("Subset", "Observations", "ObservationPercent")]
} # }