bias_interaction_report: Build a bias-interaction plot-data bundle (FACETS Table 13:...

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

Build a bias-interaction plot-data bundle (FACETS Table 13: ranked bias list)

Description

Bundles the ranked flagged-cells view of a bias-interaction run for downstream printing and plotting. The three sibling reports in this family are intentionally distinct:

• bias_interaction_report() (this one) = FACETS Table 13: a ranked list of interaction cells with t, ⁠bias size⁠, and screening tail area – use when reviewing which ⁠(facet_a, facet_b)⁠ cells deserve follow-up.

• bias_iteration_report() = iteration history / convergence trace for the bias recalibration (FACETS Table 9 territory) – use when diagnosing whether the bias run itself stabilised.

• bias_pairwise_report() = pairwise contrast table for a target facet (FACETS Table 14 territory) – use when comparing levels within a facet while controlling for the other.

Usage

bias_interaction_report(
  x,
  diagnostics = NULL,
  facet_a = NULL,
  facet_b = NULL,
  interaction_facets = NULL,
  max_abs = 10,
  omit_extreme = TRUE,
  max_iter = 4,
  tol = 0.001,
  top_n = 50,
  abs_t_warn = 2,
  abs_bias_warn = 0.5,
  p_max = 0.05,
  sort_by = c("abs_t", "abs_bias", "prob")
)

Arguments

x	Output from estimate_bias() or fit_mfrm().
diagnostics	Optional output from diagnose_mfrm() (used when x is fit).
facet_a	First facet name (required when x is fit and interaction_facets is not supplied).
facet_b	Second facet name (required when x is fit and interaction_facets is not supplied).
interaction_facets	Character vector of two or more facets.
max_abs	Bound for absolute bias size when estimating from fit.
omit_extreme	Omit extreme-only elements when estimating from fit.
max_iter	Iteration cap for bias estimation when x is fit.
tol	Convergence tolerance for bias estimation when x is fit.
top_n	Maximum number of ranked rows to keep.
abs_t_warn	Warning cutoff for absolute t statistics.
abs_bias_warn	Warning cutoff for absolute bias size.
p_max	Warning cutoff for p-values.
sort_by	Ranking key: "abs_t", "abs_bias", or "prob".

Details

Preferred bundle API for interaction-bias diagnostics. The function can:

• use a precomputed bias object from estimate_bias(), or

• estimate internally from mfrm_fit + facet specification.

Value

A named list with bias-interaction plotting/report components. Class: mfrm_bias_interaction.

Interpreting output

Focus on ranked rows where multiple screening criteria converge:

• large absolute t statistic

• large absolute bias size

• small screening tail area

The bundle is optimized for downstream summary() and plot_bias_interaction() views.

Typical workflow

1. Run estimate_bias() (or provide mfrm_fit here).

2. Build bias_interaction_report(...).

3. Review summary(out) and visualize with plot_bias_interaction().

See Also

estimate_bias(), build_fixed_reports(), plot_bias_interaction()

Examples

toy <- load_mfrmr_data("example_bias")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
bias <- estimate_bias(fit, diag, facet_a = "Rater", facet_b = "Criterion", max_iter = 2)
out <- bias_interaction_report(bias, top_n = 10)
summary(out)
p_bi <- plot(out, draw = FALSE)
p_bi$data$plot

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