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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.

QC checks performed

The pipeline evaluates up to 10 checks (depending on available diagnostics):

  1. Convergence: did the optimizer converge?

  2. Overall Infit: global information-weighted mean-square

  3. Overall Outfit: global unweighted mean-square

  4. Misfit rate: proportion of elements with \(|\mathrm{ZSTD}| > 2\)

  5. Category usage: minimum observations per score category

  6. Disordered steps: whether threshold estimates are monotonic

  7. Separation (per facet): element discrimination adequacy

  8. Residual PCA eigenvalue: first-component eigenvalue (if computed)

  9. Displacement: maximum absolute displacement across elements

  10. Inter-rater agreement: minimum pairwise exact agreement

Interpreting plots

  • Green (Pass): the check meets the current threshold-profile criteria.

  • Amber (Warn): borderline—monitor but not necessarily disqualifying. Review the detail panel to see how close the value is to the fail threshold.

  • Red (Fail): requires investigation before strong operational or interpretive claims are made from the current run. Common remedies include collapsing categories (for disordered steps), removing outlier raters (for misfit), or increasing sample size (for low separation).

  • The detail view shows numeric values, making it easy to communicate exact results to stakeholders.

See also

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

Examples

toy <- load_mfrmr_data("study1")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 25)
#> Warning: Optimizer did not fully converge (code = 1). Consider increasing maxit (current: 25) or relaxing reltol (current: 1e-06).
qc <- run_qc_pipeline(fit)
plot_qc_pipeline(qc, draw = FALSE)