Build facet variability diagnostics with fixed/random reference tests

Build facet variability diagnostics with fixed/random reference tests

Usage

facets_chisq_table(
  fit,
  diagnostics = NULL,
  fixed_p_max = 0.05,
  random_p_max = 0.05,
  top_n = NULL
)

Arguments

fit

Output from fit_mfrm().

diagnostics

Optional output from diagnose_mfrm(). Must match this fitted analysis; see mfrmr_compatibility_layer for migration of saved diagnostics.

fixed_p_max

Warning cutoff for fixed-effect chi-square p-values.

random_p_max

Warning cutoff for random-effect chi-square p-values.

top_n

Optional maximum number of facet rows to keep.

Value

A named list with:

• table: facet-level chi-square diagnostics

• summary: one-row summary

• thresholds: applied p-value thresholds

Details

This helper summarizes facet-level variability with fixed and random chi-square indices for spread and heterogeneity checks.

Interpreting output

• table: facet-level fixed/random chi-square and p-value flags.

• summary: full-family available/unavailable counts and screen-positive counts, before top_n limits displayed rows.

• thresholds: p-value criteria used for flagging.

Use this table together with inter-rater and displacement diagnostics to distinguish global facet effects from local anomalies.

Typical workflow

1. Run facets_chisq_table(fit, ...).

2. Inspect summary(chi) then facet rows in chi$table.

3. Visualize with plot(x, type = "facets_chisq").

Output columns

The table data.frame contains:

Facet

Facet name.

Levels

Number of estimated levels in this facet.

MeanMeasure, SD

Mean and standard deviation of level measures.

FixedChiSq, FixedDF, FixedProb

Fixed-effect chi-square test (null hypothesis: all levels equal). Significant result means the facet elements differ more than measurement error alone.

RandomChiSq, RandomDF, RandomProb, RandomVar

FACETS-form random spread index and reference tail area. It does not establish normality or random sampling: with equal SEs and positive adjusted variance the statistic is identically eligible-level count minus one, regardless of distributional shape. Full covariance is not propagated.

FixedFlag, RandomFlag

Logical flags for significance.

See also

diagnose_mfrm(), interrater_agreement_table(), plot(x, type = "facets_chisq")

Examples

# Saved synthetic JML/RSM analysis of example_core.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "core-jml.rds", package = "mfrmr"))
fit <- example_fit$fit
chi <- facets_chisq_table(fit)
summary(chi)
#> mfrmr Facet Variability Summary 
#>   Class: mfrm_facets_chisq
#>   Components: 3
#> 
#> Facet variability summary
#>  Facets FixedAvailable FixedUnavailable RandomAvailable RandomUnavailable
#>       3              3                0               3                 0
#>  FixedSignificant RandomSignificant MeanRandomVar MaxFixedChiSq MaxRandomChiSq
#>                 3                 0         0.417       384.048         45.462
#> 
#> Facet rows: table
#>      Facet Levels EligibleLevels ExcludedLevels MeanMeasure    SD FixedChiSq
#>     Person     48             48              0       0.001 1.099    384.048
#>      Rater      4              4              0       0.000 0.313     30.899
#>  Criterion      4              4              0       0.000 0.288     25.914
#>  FixedDF RandomVar FixedProb RandomChiSq RandomDF RandomProb InferenceTier
#>       47     1.089         0      45.462       46      0.495     screening
#>        3     0.089         0       2.999        2      0.223     screening
#>        3     0.073         0       2.997        2      0.223     screening
#>  SupportsFormalInference
#>                    FALSE
#>                    FALSE
#>                    FALSE
#>                                               StatisticBasis FixedFlag
#>  Diagonal-SE heterogeneity screen; covariance not propagated      TRUE
#>  Diagonal-SE heterogeneity screen; covariance not propagated      TRUE
#>  Diagonal-SE heterogeneity screen; covariance not propagated      TRUE
#>  RandomFlag
#>       FALSE
#>       FALSE
#>       FALSE
#> 
#> Settings
#>       Setting Value
#>   fixed_p_max  0.05
#>  random_p_max  0.05
#> 
#> Notes
#>  - Facet variability summary with fixed/random reference tests.
p_chi <- plot(chi, draw = FALSE)
p_chi$data$plot
#> [1] "fixed"