plot_person_fit: Plot per-person fit

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

Plot per-person fit

Description

Per-person diagnostic bubble plot inspired by FACETS Table 6 / KIDMAP summaries. Each bubble represents one person at the intersection of Infit (x) and Outfit (y), sized by total observations and coloured by the standard 0.5/1.5 fit envelope: green when both Infit and Outfit fall in ⁠[lower, upper]⁠, amber when one statistic is outside, red when both are outside. Set fit_index = "loglik" for a ranked view of the report-ready lz_star / lz index instead.

Usage

plot_person_fit(
  fit,
  diagnostics = NULL,
  lower = 0.5,
  upper = 1.5,
  top_n_label = 12L,
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE,
  fit_index = c("meansquare", "loglik")
)

Arguments

fit	An mfrm_fit from fit_mfrm().
diagnostics	Optional diagnose_mfrm() output. When omitted, diagnose_mfrm(fit, residual_pca = "none") is run internally.
lower	Lower fit threshold (default 0.5, Linacre 2002).
upper	Upper fit threshold (default 1.5).
top_n_label	Maximum number of persons whose label is drawn. The default mean-square view uses largest ⁠|Infit - 1| + |Outfit - 1|⁠; fit_index = "loglik" uses largest absolute report index. Default 12.
preset	Visual preset, including "monochrome".
draw	If TRUE, draw with base graphics.
fit_index	Plot focus. "meansquare" keeps the Infit/Outfit bubble plot. "loglik" draws the report index selected by compute_person_fit_indices() (lz_star when available, otherwise lz with a caveat).

Value

An mfrm_plot_data object whose reusable plot data include data with one row per person, plot_long for custom R graphics, person_fit_indices from compute_person_fit_indices(), and compact flag/status summaries.

Interpreting output

The default 0.5-1.5 envelope follows Linacre (2002) Rasch Measurement Transactions. Persons in the green centre are fit-acceptable; amber and red corners are candidates for misfit review (overfit / underfit) using unexpected_response_table() for follow-up.

See Also

diagnose_mfrm(), unexpected_response_table(), build_misfit_casebook().

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
p <- plot_person_fit(fit, draw = FALSE)
head(p$data$data)

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