Summarize an mfrm_bias object in a user-friendly format

Summarize an mfrm_bias object in a user-friendly format

Usage

# S3 method for class 'mfrm_bias'
summary(object, digits = 3, top_n = 10, p_cut = 0.05, ...)

Arguments

object

Output from estimate_bias().

digits

Number of digits for printed numeric values.

top_n

Number of strongest bias rows to keep.

p_cut

Significance cutoff used for counting flagged rows.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_bias with:

• overview: interaction facets/order, cell counts, and effect-size profile

• chi_sq: fixed-effect chi-square block

• final_iteration: end-of-iteration status row

• top_rows: highest-|t| interaction rows

• notes: short interpretation notes

Details

This method returns a compact interaction-bias summary:

• interaction facets/order and analyzed cell counts

• effect-size profile (|bias| mean/max, significant cell count)

• fixed-effect chi-square block

• iteration-end convergence indicators

• top rows ranked by absolute t

Interpreting output

• overview: interaction order, analyzed cells, and effect-size profile.

• chi_sq: fixed-effect test block.

• final_iteration: end-of-loop status from the bias routine.

• top_rows: strongest bias contrasts by |t|; bounded GPCM summaries also retain the profile-likelihood review columns when present.

Typical workflow

1. Estimate interactions with estimate_bias().

2. Check summary(bias) for screen-positive and unstable cells.

3. Use bias_interaction_report() or plot_bias_interaction() for details.

See also

estimate_bias(), bias_interaction_report()

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_bias")
toy <- toy[toy$Person %in% unique(toy$Person)[1:8], ]
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 = 1)
summary(bias)
}