mfrmr_visual_diagnostics: mfrmr Visual Diagnostics Map

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

mfrmr Visual Diagnostics Map

Description

Quick guide to choosing the right base-R diagnostic plot in mfrmr. Use this page when you know the analysis question but do not yet know which plotting helper or plot() method to call.

Start with the question

• "Do persons and facet levels overlap on the same logit scale?" Use plot(fit, type = "wright") or plot_wright_unified().

• "Where do score categories transition across theta?" Use plot(fit, type = "pathway") and plot(fit, type = "ccc").

• "Is the design linked well enough across subsets or administrations?" Use plot(subset_connectivity_report(...), type = "design_matrix") and plot_anchor_drift().

• "Which responses or levels look locally problematic?" Use plot_unexpected() and plot_displacement().

• "Do raters agree and do facets separate meaningfully?" Use plot_interrater_agreement() and plot_facets_chisq().

• "Is there notable residual structure after the main Rasch dimension?" Use plot_residual_pca().

• "Which interaction cells or facet levels drive bias screening results?" Use plot_bias_interaction().

• "I need one compact triage screen first." Use plot_qc_dashboard().

Recommended visual route

1. Start with plot_qc_dashboard() for one-page triage.

2. Move to plot_unexpected(), plot_displacement(), and plot_interrater_agreement() for flagged local issues.

3. Use plot(fit, type = "wright"), plot(fit, type = "pathway"), and plot_residual_pca() for structural interpretation.

4. Use plot(..., draw = FALSE) when you want reusable plotting payloads instead of immediate graphics.

5. Use preset = "publication" when you want the package's cleaner manuscript-oriented styling.

Which plot answers which question

plot(fit, type = "wright")

Shared logit map of persons, facet levels, and step thresholds. Best for targeting and spread.

plot(fit, type = "pathway")

Expected score by theta, with dominant-category strips. Best for scale progression.

plot(fit, type = "ccc")

Category probability curves. Best for checking whether categories peak in sequence.

plot_unexpected()

Observation-level surprises. Best for case review and local misfit triage.

plot_displacement()

Level-wise anchor movement. Best for anchor robustness and residual calibration tension.

plot_interrater_agreement()

Exact agreement, expected agreement, pairwise correlation, and agreement gaps. Best for rater consistency.

plot_facets_chisq()

Facet variability and chi-square summaries. Best for checking whether a facet contributes meaningful spread.

plot_residual_pca()

Residual structure after the Rasch dimension is removed. Best for exploratory residual-structure review, not as a standalone unidimensionality test.

plot_bias_interaction()

Interaction-bias screening views for cells and facet profiles. Best for systematic departure from the additive main-effects model.

plot_anchor_drift()

Anchor drift and screened linking-chain visuals. Best for multi-form or multi-wave linking review after checking retained common-element support.

Practical interpretation rules

• Wright map: look for gaps between person density and facet/step locations; large gaps indicate weaker targeting.

• Pathway / CCC: look for monotone progression and clear category dominance bands; flat or overlapping curves suggest weak category separation.

• Unexpected / displacement: use as screening tools, not final evidence by themselves.

• Inter-rater agreement and facet variability address different questions: agreement concerns scoring consistency, whereas variability concerns whether facet elements are statistically distinguishable.

• Residual PCA and bias plots should be interpreted as follow-up layers after the main fit screen, not as first-pass diagnostics.

Typical workflow

• Quick screening: fit_mfrm() -> diagnose_mfrm() -> plot_qc_dashboard().

• Scale and targeting review: plot(fit, type = "wright") -> plot(fit, type = "pathway") -> plot(fit, type = "ccc").

• Linking review: subset_connectivity_report() -> plot(..., type = "design_matrix") -> plot_anchor_drift().

• Interaction review: estimate_bias() -> plot_bias_interaction() -> reporting_checklist().

Companion vignette

For a longer, plot-first walkthrough, run vignette("mfrmr-visual-diagnostics", package = "mfrmr").

See Also

mfrmr_workflow_methods, mfrmr_reports_and_tables, mfrmr_reporting_and_apa, mfrmr_linking_and_dff, plot.mfrm_fit(), plot_qc_dashboard(), plot_unexpected(), plot_displacement(), plot_interrater_agreement(), plot_facets_chisq(), plot_residual_pca(), plot_bias_interaction(), plot_anchor_drift()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(
  toy,
  person = "Person",
  facets = c("Rater", "Criterion"),
  score = "Score",
  method = "JML",
  maxit = 25
)
diag <- diagnose_mfrm(fit, residual_pca = "none")

qc <- plot_qc_dashboard(fit, diagnostics = diag, draw = FALSE, preset = "publication")
names(qc$data)

wright <- plot(fit, type = "wright", draw = FALSE, preset = "publication")
wright$data$preset

pca <- analyze_residual_pca(diag, mode = "overall")
scree <- plot_residual_pca(pca, plot_type = "scree", draw = FALSE, preset = "publication")
scree$data$preset

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