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".

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

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").

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

Run unexpected_response_table() as baseline.
Estimate bias via estimate_bias().
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.

Examples

if (FALSE) { # interactive()
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)
t10 <- unexpected_after_bias_table(fit, bias, diagnostics = diag, top_n = 20)
summary(t10)
p_t10 <- plot(t10, draw = FALSE)
p_t10$data$plot
}