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. |

## See also