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Summarize an mfrm_bias object in a user-friendly format

Source: R/api-methods.R
summary.mfrm_bias.Rd

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

Tail-area cutoff used for counting screen-positive 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, screen-positive 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

# \donttest{
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)
#> Many-Facet Measurement Bias Summary
#>   Interaction facets: Rater x Criterion | Cells: 16
#>   Order: 2 | Mode: pairwise
#>   Mean |Bias|: 0.304 | Max |Bias|: 0.909 | Screen-positive (p <= 0.050): 0
#>   Bonferroni screen-positive: 0 | Holm screen-positive: 0 (cut = 0.050, m = 16)
#> 
#> Fixed-effect chi-square
#>  FixedChiSq FixedDF FixedProb InferenceTier SupportsFormalInference
#>       3.521      15     0.999     screening                   FALSE
#>  FormalInferenceEligible PrimaryReportingEligible   ReportingUse
#>                    FALSE                    FALSE screening_only
#>                                 TestBasis InteractionFacets InteractionOrder
#>  conditional plug-in heterogeneity screen Rater x Criterion                2
#>  InteractionMode
#>         pairwise
#> 
#> Final iteration status
#>  Iteration MaxScoreResidual MaxScoreResidualPct MaxScoreResidualCategories
#>          1                0                   0                         NA
#>  MaxLogitChange BiasCells
#>          -0.909        16
#> 
#> Top |t| bias rows
#>                Pair Rater    Criterion Bias Size  S.E.      t Prob.
#>      R04 | Accuracy   R04     Accuracy    -0.909 0.929 -0.979 0.431
#>       R04 | Content   R04      Content     0.759 0.982  0.773 0.520
#>  R04 | Organization   R04 Organization     0.759 0.982  0.773 0.520
#>  R01 | Organization   R01 Organization    -0.384 0.624 -0.615 0.572
#>       R02 | Content   R02      Content    -0.473 0.783 -0.604 0.588
#>      R04 | Language   R04     Language    -0.470 0.929 -0.506 0.663
#>      R02 | Accuracy   R02     Accuracy     0.248 0.735  0.337 0.758
#>      R01 | Language   R01     Language     0.203 0.624  0.325 0.761
#>      R01 | Accuracy   R01     Accuracy     0.163 0.643  0.253 0.813
#>      R03 | Accuracy   R03     Accuracy     0.134 0.823  0.163 0.881
#>  Obs-Exp Average  AbsT
#>                0 0.979
#>                0 0.773
#>                0 0.773
#>                0 0.615
#>                0 0.604
#>                0 0.506
#>                0 0.337
#>                0 0.325
#>                0 0.253
#>                0 0.163
#> 
#> Notes
#>  - Bias iteration may not have fully stabilized (BiasCells > 0 at final step).
# }