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

Run facets_chisq_table(fit, ...).
Inspect summary(chi) then facet rows in chi$table.
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.

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("facets_chisq_table", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# 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$fit
chi <- facets_chisq_table(fit)
summary(chi)
p_chi <- plot(chi, draw = FALSE)
p_chi$data$plot
} # }