Plot ROC and Related Curves for Phenotyping Models

Plot ROC-like curves to illustrate phenotyping accuracy.

phecap_plot_roc_curves(
  x, axis_x = "1 - spec", axis_y = "sen",
  what = c("training", "random-splits", "validation"),
  ggplot = TRUE, ...)

Arguments

x	either a single object of class PhecapModel or PhecapValidation (returned from `phecap_train_phenotyping_model` or `phecap_validate_phenotyping_model`), or a named list of such objects
axis_x	an expression that leads to the `x` coordinate. Recognized quantities include: `cut` (probability cutoff), `pct` (percent of predicted cases), `acc` (accuracy), `tpr` (true positive rate), `fpr` (false positive rate), `tnr` (true negative rate), `ppv` (positive predictive value), `fdr` (false discovery rate), `npv` (negative predictive value), `sen` (sensitivity), `spec` (specificity), `prec` (precision), `rec` (recall), `f1` (F1 score).
axis_y	an expression that leads to the `y` coordinate. Recognized quantities are the same as those in `axis_x`.
what	The curves to be included in the figure.
ggplot	if TRUE and ggplot2 is installed, ggplot will be used for the figure. Otherwise, the base R graphics functions will be used.
...	arguments to be ignored.