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Build facet variability diagnostics with fixed/random reference tests

Source: R/api-tables.R
facets_chisq_table.Rd

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().

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: number of significant facets and overall magnitude indicators.

  • 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_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

Random-effect test (null hypothesis: variation equals that of a random sample from a single population). Significant result suggests systematic heterogeneity beyond sampling variation.

FixedFlag, RandomFlag

Logical flags for significance.

See also

diagnose_mfrm(), interrater_agreement_table(), plot_facets_chisq()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
chi <- facets_chisq_table(fit)
summary(chi)
#> mfrmr Facet Variability Summary 
#>   Class: mfrm_facets_chisq
#>   Components (3): table, summary, thresholds
#> 
#> Facet variability summary
#>  Facets FixedSignificant RandomSignificant MeanRandomVar MaxFixedChiSq
#>       3                3                 0         0.418       384.563
#>  MaxRandomChiSq
#>          45.462
#> 
#> Facet rows: table
#>      Facet Levels MeanMeasure    SD FixedChiSq FixedDF RandomVar FixedProb
#>     Person     48       0.001 1.101    384.563      47     1.091         0
#>      Rater      4       0.000 0.313     30.881       3     0.089         0
#>  Criterion      4       0.000 0.287     25.804       3     0.073         0
#>  RandomChiSq RandomDF RandomProb FixedFlag RandomFlag
#>       45.462       46      0.495      TRUE      FALSE
#>        2.999        2      0.223      TRUE      FALSE
#>        2.997        2      0.223      TRUE      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)
class(p_chi)
#> [1] "mfrm_plot_data" "list"