Build a weighting-policy review between Rasch-family and bounded GPCM fits

Build a weighting-policy review between Rasch-family and bounded GPCM fits

Usage

build_weighting_review(
  rasch_fit,
  gpcm_fit,
  theta_range = c(-6, 6),
  theta_points = 101L,
  top_n = 10L
)

Arguments

rasch_fit

Output from fit_mfrm() using model = "RSM" or "PCM".

gpcm_fit

Output from fit_mfrm() using bounded model = "GPCM".

theta_range

Numeric vector of length 2 passed to compute_information() for the information-redistribution comparison.

theta_points

Integer number of theta grid points passed to compute_information().

top_n

Maximum number of rows to keep in compact summary outputs.

Value

An object of class mfrm_weighting_review.

Details

build_weighting_review() is an operational model-choice review helper. It is designed for the common question:

• what changes when a Rasch-family equal-weighting model is replaced with a bounded GPCM that allows discrimination-based reweighting?

The helper does not estimate a new model. Instead, it synthesizes four package-native evidence sources:

• compare_mfrm() for same-data model comparison

• the non-person facet measures from each fit

• the bounded GPCM slope table

• compute_information() for design-weighted information redistribution

The result is intended for substantive review, not for automatic model selection. In particular, a better-fitting GPCM should not by itself be interpreted as a reason to discard an equal-weighting Rasch-family route.

Recommended input route

1. Fit an equal-weighting reference model with model = "RSM" or "PCM".

2. Fit a bounded GPCM on the same prepared response data.

3. Run build_weighting_review(rasch_fit, gpcm_fit).

4. Read summary(review) before deciding whether the discrimination-based reweighting is substantively acceptable.

What the returned tables mean

• model_comparison: same-data model-comparison bundle from compare_mfrm().

• facet_shift: how non-person facet estimates move under bounded GPCM.

• slope_profile: which slope_facet levels are upweighted or downweighted.

• information_redistribution: within-facet information-share changes between the Rasch-family fit and bounded GPCM.

• top_reweighted_levels: compact triage table for the strongest slope-facet-level redistribution signals.

GPCM boundary

This helper is available only for the current bounded GPCM branch. It requires the package's existing slope_facet == step_facet contract and should be read as an operational weighting-policy review, not as a formal validity adjudication.

See also

compare_mfrm(), compute_information(), gpcm_capability_matrix()

Examples

if (FALSE) { # \dontrun{
toy <- load_mfrmr_data("example_core")
rasch_fit <- fit_mfrm(
  toy,
  "Person",
  c("Rater", "Criterion"),
  "Score",
  method = "MML",
  model = "RSM",
  quad_points = 9
)
gpcm_fit <- fit_mfrm(
  toy,
  "Person",
  c("Rater", "Criterion"),
  "Score",
  method = "MML",
  model = "GPCM",
  step_facet = "Criterion",
  slope_facet = "Criterion",
  quad_points = 9
)
review <- build_weighting_review(rasch_fit, gpcm_fit, theta_points = 41)
summary(review)
review$top_reweighted_levels
} # }