facets_chisq_table: Build facet variability diagnostics with fixed/random...
In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

facets_chisq_table

R Documentation

Build facet variability diagnostics with fixed/random reference tests

Description

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.

Details

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

Value

A named list with:

table: facet-level chi-square diagnostics
summary: one-row summary
thresholds: applied p-value thresholds

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

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

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)
p_chi <- plot(chi, draw = FALSE)
class(p_chi)

mfrmr documentation built on March 31, 2026, 1:06 a.m.