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Plot QC pipeline results

Source: R/api-plotting-anchor-qc.R
plot_qc_pipeline.Rd

Visualizes the output from run_qc_pipeline() as either a traffic-light bar chart or a detail panel showing values versus thresholds.

Usage

plot_qc_pipeline(x, type = c("traffic_light", "detail"), draw = TRUE, ...)

Arguments

x

Output from run_qc_pipeline().

type

Plot type: "traffic_light" (default) or "detail".

draw

If FALSE, return plot data invisibly without drawing.

...

Additional graphical parameters passed to plotting functions.

Value

Invisible verdicts tibble from the QC pipeline.

Details

Two plot types are provided for visual triage of QC results:

  • "traffic_light" (default): A horizontal bar chart with one row per QC check. Bars are coloured green (Pass), amber (Warn), or red (Fail). Provides an at-a-glance summary of the current QC review state.

  • "detail": A panel showing each check's observed value and its pass/warn/fail thresholds. Useful for understanding how close a borderline result is to the next verdict level.

Interpretation

The rows are the actual checks returned by run_qc_pipeline(): convergence, global fit, selected-facet reliability and separation, element misfit, unexpected responses, category structure, connectivity, inter-rater agreement, and the functioning/bias screen. Element misfit uses the configured mean-square band; it is not a ZSTD test. PCA and displacement are separate diagnostics and are not checks in this pipeline.

Green (Pass), amber (Warn), and red (Fail) describe the selected screening rules. Grey (Skip) marks unrequested or unavailable checks; AffectsOverall in the returned data distinguishes their contribution to the overall result. A missing result is not evidence of acceptable fit. High rater separation is not high agreement, and no colour establishes statistical validity or justifies deleting raters or collapsing categories without further review.

See also

run_qc_pipeline(), plot_qc_dashboard(), build_visual_summaries(), mfrmr_visual_diagnostics

Examples

# \donttest{
toy <- load_mfrmr_data("study1")
# These observed data trigger a category-support warning: the QC plot
# displays that review requirement; convergence does not remove it.
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 300)
#> Warning: Category support is retained but requires review: at least one fitted or local scope contains an empty or singleton category/transition cell. The fit may be inspected, but category-information strength has not been certified; inspect `fit$data_review$category_support` before inference.
qc <- run_qc_pipeline(fit)
plot_qc_pipeline(qc, draw = FALSE)
# }