Build a category structure report (preferred alias)

Build a category structure report (preferred alias)

Usage

category_structure_report(
  fit,
  diagnostics = NULL,
  theta_range = c(-6, 6),
  theta_points = 241,
  drop_unused = FALSE,
  include_fixed = FALSE,
  fixed_max_rows = 200
)

Arguments

fit

Output from fit_mfrm().

diagnostics

Optional output from diagnose_mfrm().

theta_range

Theta/logit range used to derive transition points.

theta_points

Number of grid points used for transition-point search.

drop_unused

If TRUE, remove zero-count categories from outputs.

include_fixed

If TRUE, include a legacy-compatible fixed-width text block.

fixed_max_rows

Maximum rows per fixed-width section.

Value

A named list with category-structure components. Class: mfrm_category_structure.

Details

Preferred high-level API for category-structure diagnostics. This wraps the legacy-compatible bar/transition export and returns a stable bundle interface for reporting and plotting.

Interpreting output

Key components include:

• category usage/fit table (count, expected, infit/outfit, ZSTD)

• threshold ordering and adjacent threshold gaps

• category transition-point table on the requested theta grid

Practical read order:

1. summary(out) for compact warnings and threshold ordering.

2. out$category_table for sparse/misfitting categories.

3. out$median_thresholds for adjacent-threshold caveats when zero-count categories are retained.

4. plot(out) for quick visual check.

Typical workflow

1. fit_mfrm() -> model.

2. diagnose_mfrm() -> residual/fit diagnostics (optional argument here).

3. category_structure_report() -> category health snapshot.

4. summary() and plot() for draft-oriented review of category structure.

See also

rating_scale_table(), category_curves_report(), plot.mfrm_fit(), mfrmr_reports_and_tables, mfrmr_visual_diagnostics

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
out <- category_structure_report(fit)
summary(out)
head(out$category_table[, c("Category", "Count", "Infit", "Outfit")])
p_cs <- plot(out, draw = FALSE)
p_cs$data$plot
}