visualize\(evaluate\)discrimination$roc

ROC curves for discrimination results

Description

ROC curves for discrimination results

Usage

visualize.evaluate.roc(
  results,
  models = NULL,
  outcomes = NULL,
  show.auc = TRUE,
  facet.by = c("none", "outcome"),
  colors = NULL,
  ...
)

Arguments

`results`	A `PolyGeniusEvaluation` object.
`models`	Optional model filter.
`outcomes`	Optional outcome filter.
`show.auc`	Whether to append AUC values, and confidence intervals when available, to legend labels.
`facet.by`	Faceting mode (`“none”` or `“outcome”`).
`colors`	Optional named color vector.
`…`	Reserved for future extensions.

Value

A ggplot object.

See Also

Other visualize: visualize.evaluate.calibration.curve(), visualize.evaluate.calibration.slope.intercept(), visualize.evaluate.compare.delta.forest(), visualize.evaluate.compare.delta.heatmap(), visualize.evaluate.confusion(), visualize.evaluate.discrimination.curve(), visualize.evaluate.distribution(), visualize.evaluate.forest(), visualize.evaluate.incremental.delta(), visualize.evaluate.leaderboard(), visualize.evaluate.metric.flags(), visualize.evaluate.metric.heatmap(), visualize.evaluate.pr(), visualize.evaluate.predicted.vs.observed(), visualize.evaluate.residuals(), visualize.evaluate.risk.strata.lift(), visualize.evaluate.risk.strata.profile(), visualize.evaluate.similarity.heatmap()