estimate_all_bias: Estimate bias across multiple facet pairs

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

Estimate bias across multiple facet pairs

Description

Estimate bias across multiple facet pairs

Usage

estimate_all_bias(
  fit,
  diagnostics = NULL,
  pairs = NULL,
  include_person = FALSE,
  drop_empty = TRUE,
  keep_errors = TRUE,
  max_abs = 10,
  omit_extreme = TRUE,
  max_iter = 4,
  tol = 0.001
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm(). When NULL, diagnostics are computed with residual_pca = "none".
pairs	Optional list of facet specifications. Each element should be a character vector of length 2 or more, for example list(c("Rater", "Criterion"), c("Task", "Criterion")). When NULL, all 2-way combinations of modeled facets are used.
include_person	If TRUE and pairs = NULL, include "Person" in the automatically generated pair set.
drop_empty	If TRUE, omit empty bias tables from by_pair while still recording them in the summary table.
keep_errors	If TRUE, retain per-pair error rows in the returned errors table instead of failing the whole batch.
max_abs	Passed to estimate_bias().
omit_extreme	Passed to estimate_bias().
max_iter	Passed to estimate_bias().
tol	Passed to estimate_bias().

Details

This function orchestrates repeated calls to estimate_bias() across multiple facet pairs and returns a consolidated bundle.

Bias/interaction in MFRM refers to a systematic departure from the additive model for a specific combination of facet elements (e.g., a particular rater is unexpectedly harsh on a particular criterion). See estimate_bias() for the mathematical formulation.

When pairs = NULL, the function builds all 2-way combinations of modelled facets automatically. For a model with facets Rater, Criterion, and Task, this yields Rater\timesCriterion, Rater\timesTask, and Criterion\timesTask.

The summary table aggregates results across pairs:

• Rows: number of interaction cells estimated

• Significant: count of cells with |t| \ge 2

• MeanAbsBias: average absolute bias magnitude (logits)

Per-pair failures (e.g., insufficient data for a sparse pair) are captured in errors rather than stopping the entire batch.

Value

A named list with class mfrm_bias_collection.

Output

The returned object is a bundle-like list with class mfrm_bias_collection and components such as:

• summary: one row per requested interaction

• by_pair: named list of successful estimate_bias() outputs

• errors: per-pair error log

• settings: resolved execution settings

• primary: first successful bias bundle, useful for downstream helpers

Typical workflow

1. Fit with fit_mfrm() and diagnose with diagnose_mfrm().

2. Run estimate_all_bias() to compute app-style multi-pair interactions.

3. Pass the resulting by_pair list into reporting_checklist() or facet_quality_dashboard().

See Also

estimate_bias(), reporting_checklist(), facet_quality_dashboard()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 25)
diag <- diagnose_mfrm(fit, residual_pca = "none")
bias_all <- estimate_all_bias(fit, diagnostics = diag)
bias_all$summary[, c("Interaction", "Rows", "Significant")]

mfrmr documentation built on March 31, 2026, 1:06 a.m.