Plot ROC-like curves to illustrate phenotyping accuracy.

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



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


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).


an expression that leads to the y coordinate. Recognized quantities are the same as those in axis_x.


The curves to be included in the figure.


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.

See also

See PheCAP-package for code examples.