Get performance curves — get

This function generates and saves the Receiver Operating Characteristic (ROC) curve and Precision-Recall curve based on the provided metrics. It also includes the AUC values for both curves in the plot legends.

Usage

get_curves(
  data,
  spec = "Specificity",
  sens = "Sensitivity",
  reca = "Recall",
  prec = "Precision",
  color,
  auc_roc,
  auc_prc,
  LODO = FALSE,
  file.name,
  width = 6,
  height = 6
)

Arguments

data: A data frame containing the prediction metrics.
spec: The name of the column containing the specificity values.
sens: The name of the column containing the sensitivity values.
reca: The name of the column containing the recall values.
prec: The name of the column containing the precision values.
color: The name of the column containing the cohort names. Each cohort will have a corresponding color in the plot. Multiple cohorts will result in different curves.
auc_roc: A numeric value representing the AUC for the ROC curve.
auc_prc: A numeric value representing the AUC for the Precision-Recall curve.
LODO: Logical. If TRUE, the function assumes the data contains stacked predictions from multiple cohorts and assigns AUROC/AUPRC per cohort (default = FALSE).
file.name: A character string used as the file name prefix for saving the plots.
width: A numeric value for the width of plot
height: A numeric value for the height of plot

Value

Saves two PDF plots: one for the ROC curve and one for the Precision-Recall curve in the "Results/" directory.