plot_rater_agreement_heatmap: Pairwise rater-agreement heatmap

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Pairwise rater-agreement heatmap

Description

Summarizes inter-rater agreement as a symmetric rater x rater heatmap. Cells are coloured by the chosen agreement metric: exact agreement proportion by default, or the Pearson-style Corr column from interrater_agreement_table() when metric = "correlation". The plot is a compact alternative to plot_interrater_agreement()'s bar chart when the rater count exceeds ~6 pairs.

Usage

plot_rater_agreement_heatmap(
  fit,
  diagnostics = NULL,
  rater_facet = "Rater",
  metric = c("exact", "correlation"),
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE
)

Arguments

fit	An mfrm_fit.
diagnostics	Optional diagnose_mfrm() output; piped through to interrater_agreement_table() when supplied.
rater_facet	Name of the rater facet (default "Rater").
metric	Column to colour by: "exact" (default) or "correlation". Quadratic-weighted kappa is not currently computed by interrater_agreement_table() and is therefore not offered here.
preset	Visual preset.
draw	If TRUE, draw with base graphics.

Value

An mfrm_plot_data object whose data slot bundles the rater x rater matrix and the raw pairwise rows.

See Also

interrater_agreement_table() for the underlying numeric table; plot_guttman_scalogram() for a complementary person-by-element view of residual structure; diagnose_mfrm() for the diagnostics bundle the heatmap reads from.

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
p <- plot_rater_agreement_heatmap(fit, draw = FALSE)
dim(p$data$matrix)
# Look for (default `metric = "exact"`):
# - Off-diagonal cells close to the corresponding entry of
#   `summary(diag)$interrater$ExactAgreement` indicate consistent
#   pair behaviour; cells well below the average mark a pair
#   that disagrees more than the rest.
# - With `metric = "correlation"` the colour scale switches to
#   `[-1, 1]`; positive cells = pairs agree on relative ordering,
#   negative cells = pairs systematically rank persons in opposite
#   directions and are the highest-priority review cases.

mfrmr documentation built on June 13, 2026, 1:07 a.m.