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Plot saved ordinary-model diagnostics

Source: R/api-plotting-dispatch.R
plot.mfrm_diagnostics.Rd

Inspect an existing diagnostic result to locate cells or elements needing follow-up. The selected display retains unavailable results and screening limits.

Usage

# S3 method for class 'mfrm_diagnostics'
plot(
  x,
  type = c("marginal_fit", "marginal_pairwise", "bubble", "residual_pca"),
  view = NULL,
  ...
)

Arguments

x

An mfrm_diagnostics result from diagnose_mfrm().

type

"marginal_fit", "marginal_pairwise", "bubble", or "residual_pca".

view

For marginal fit, "std_residual" (default) or "prop_diff"; for bubbles, "measure" (default) or "infit_outfit"; for PCA, "scree", "parallel_scree", "parallel_excess", or "loadings". Omit it for "marginal_pairwise" and use metric instead.

...

All views accept draw = FALSE, top_n, and preset. Marginal views accept facet; pairwise plots additionally accept metric = "exact" or "adjacent". Bubbles accept fit_stat ("Infit" or "Outfit"), bubble_size ("SE", "N", or "equal"), facets, include_person = FALSE, and the heuristic fit_range = c(0.5, 1.5). PCA accepts mode, facet, and component, as described in plot.mfrm_residual_pca(). Controls for other views are rejected.

Value

An mfrm_plot_data object. Use plot_data() to inspect its tables and notes. The plot does not add unavailable diagnostics or qualify intervals.

Choosing a view

Marginal fit and pairwise plots require strict marginal diagnostics from diagnose_mfrm() with diagnostic_mode = "both" or "marginal_fit" for supported MML RSM/PCM models. Positive residuals indicate more observed category usage or agreement than expected; negative values indicate less. Returned full_table and retention distinguish omitted and unavailable rows. A flag is a descriptive review rule, not a calibrated test.

Bubble plots place measures against mean-square fit, or Infit against Outfit. Bubble size can represent SE or observation count; neither a review band nor bubble size alone justifies deleting an element.

The PCA convenience route can derive PCA from the diagnostic result. For repeated PCA displays, first save analyze_residual_pca() and use its plot() method. Parallel-reference generation remains an analysis step.

See also

diagnose_mfrm(), plot.mfrm_residual_pca(), mfrmr_plot_migration