bias_count_table: Build a bias-cell count report

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

Build a bias-cell count report

Description

Build a bias-cell count report

Usage

bias_count_table(
  bias_results,
  min_count_warn = 10,
  branch = c("original", "facets"),
  fit = NULL
)

Arguments

bias_results	Output from estimate_bias().
min_count_warn	Minimum count threshold for flagging sparse bias cells.
branch	Output branch: "facets" keeps legacy manual-aligned naming, "original" returns compact QC-oriented names.
fit	Optional fit_mfrm() result used to attach run context metadata.

Details

This helper summarizes how many observations contribute to each bias-cell estimate and flags sparse cells.

Branch behavior:

• "facets": keeps legacy manual-aligned column labels (Sq, ⁠Observd Count⁠, ⁠Obs-Exp Average⁠, ⁠Model S.E.⁠) for side-by-side comparison with external workflows.

• "original": keeps compact field names (Count, BiasSize, SE) for custom QC workflows and scripting.

Value

A named list with:

• table: cell-level counts with low-count flags

• by_facet: named list of counts aggregated by each interaction facet

• by_facet_a, by_facet_b: first two facet summaries (legacy compatibility)

• summary: one-row summary

• thresholds: applied thresholds

• branch, style: output branch metadata

• fit_overview: optional one-row fit metadata when fit is supplied

Interpreting output

• table: cell-level contribution counts and low-count flags.

• by_facet: sparse-cell structure by each interaction facet.

• summary: overall low-count prevalence.

• fit_overview: optional run context (when fit is supplied).

Low-count cells should be interpreted cautiously because bias-size estimates can become unstable with sparse support.

Typical workflow

1. Estimate bias with estimate_bias().

2. Build bias_count_table(...) in desired branch.

3. Review low-count flags before interpreting bias magnitudes.

Further guidance

For a plot-selection guide and a longer walkthrough, see mfrmr_visual_diagnostics and vignette("mfrmr-visual-diagnostics", package = "mfrmr").

Output columns

The table data.frame contains, in the legacy-compatible branch:

FacetA, FacetB

Interaction facet level identifiers; placeholder names for the two interaction facets.

Sq

Sequential row number.

Observd Count

Number of observations for this cell.

Obs-Exp Average

Observed minus expected average for this cell.

Model S.E.

Standard error of the bias estimate.

Infit, Outfit

Fit statistics for this cell.

LowCountFlag

Logical; TRUE when count < min_count_warn.

The summary data.frame contains:

InteractionFacets

Names of the interaction facets.

Cells, TotalCount

Number of cells and total observations.

LowCountCells, LowCountPercent

Number and share of low-count cells.

See Also

estimate_bias(), unexpected_after_bias_table(), build_fixed_reports(), mfrmr_visual_diagnostics

Examples

toy <- load_mfrmr_data("example_bias")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
diag <- diagnose_mfrm(fit, residual_pca = "none")
bias <- estimate_bias(fit, diag, facet_a = "Rater", facet_b = "Criterion", max_iter = 2)
t11 <- bias_count_table(bias)
t11_facets <- bias_count_table(bias, branch = "facets", fit = fit)
summary(t11)
p <- plot(t11, draw = FALSE)
p2 <- plot(t11, type = "lowcount_by_facet", draw = FALSE)
if (interactive()) {
  plot(
    t11,
    type = "cell_counts",
    draw = TRUE,
    main = "Bias Cell Counts (Customized)",
    palette = c(count = "#2b8cbe", low = "#cb181d"),
    label_angle = 45
  )
}

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