unexpected_after_bias_table: Build an unexpected-after-adjustment screening report

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

Build an unexpected-after-adjustment screening report

Description

Build an unexpected-after-adjustment screening report

Usage

unexpected_after_bias_table(
  fit,
  bias_results,
  diagnostics = NULL,
  abs_z_min = 2,
  prob_max = 0.3,
  top_n = 100,
  rule = c("either", "both")
)

Arguments

fit	Output from fit_mfrm().
bias_results	Output from estimate_bias().
diagnostics	Optional output from diagnose_mfrm() for baseline comparison.
abs_z_min	Absolute standardized-residual cutoff.
prob_max	Maximum observed-category probability cutoff.
top_n	Maximum number of rows to return.
rule	Flagging rule: "either" or "both".

Details

This helper recomputes expected values and residuals after interaction adjustments from estimate_bias() have been introduced.

summary(t10) is supported through summary(). plot(t10) is dispatched through plot() for class mfrm_unexpected_after_bias (type = "scatter", "severity", "comparison").

Value

A named list with:

• table: unexpected responses after bias adjustment

• summary: one-row summary (includes baseline-vs-after counts)

• thresholds: applied thresholds

• facets: analyzed bias facet pair

Interpreting output

• summary: before/after unexpected counts and reduction metrics.

• table: residual unexpected responses after bias adjustment.

• thresholds: screening settings used in this comparison.

Large reductions indicate bias terms explain part of prior unexpectedness; persistent unexpected rows indicate remaining model-data mismatch.

Typical workflow

1. Run unexpected_response_table() as baseline.

2. Estimate bias via estimate_bias().

3. Run unexpected_after_bias_table(...) and compare reductions.

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 has the same structure as unexpected_response_table() output, with an additional BiasAdjustment column showing the bias correction applied to each observation's expected value.

The summary data.frame contains:

TotalObservations

Total observations analyzed.

BaselineUnexpectedN

Unexpected count before bias adjustment.

AfterBiasUnexpectedN

Unexpected count after adjustment.

ReducedBy, ReducedPercent

Reduction in unexpected count.

See Also

estimate_bias(), unexpected_response_table(), bias_count_table(), 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)
t10 <- unexpected_after_bias_table(fit, bias, diagnostics = diag, top_n = 20)
summary(t10)
p_t10 <- plot(t10, draw = FALSE)
class(p_t10)

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