Plot a saved QC pipeline

Display the checks already returned by run_qc_pipeline(). Use the colored verdicts for triage, then read the returned detail for the reason and next review.

Usage

# S3 method for class 'mfrm_qc_pipeline'
plot(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 base-graphics arguments for the selected view.

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 the saved Detail text beside each verdict. Read this explanation and the returned table to understand why a check was flagged or unavailable.

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.

Examples

# Long-running workflow; run explicitly when needed.
# example("plot.mfrm_qc_pipeline", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
ratings <- 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(ratings, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 300)
qc <- run_qc_pipeline(fit)
verdicts <- plot(qc, draw = FALSE)
head(verdicts)
} # }