create.ROCR.plots.v2: Create ROCR plots
In itslwg/SupaLarna: Package for the SuperLearner vs clincians study

Plots reciever operating charecteristiqs curves or precision/recall curves.

create.ROCR.plots.v2(study_sample, outcome_name, device = "eps",
  split_var = "train", train_test = TRUE, ROC_or_precrec = "ROC",
  models = c("pred_con_train", "pred_cat_train", "pred_con_test",
  "pred_cat_test", "tc"),
  pretty_names = c("SuperLearner continuous prediction",
  "SuperLearner priority levels", "SuperLearner continuous prediction",
  "SuperLearner priority levels", "Clinicians priority levels"),
  subscript = FALSE, models_to_invert = NULL)

`study_sample`	Study sample list. No default.
`device`	Character vector with the name of the image device to use. Passed to rocr.plot (in turn, passed to save.plot). Defaults to "eps".
`split_var`	The variable used to split plots. As string. Defaults to "train".
`train_test`	Logical. Is the dataset splitted in train and test set? Defaults to TRUE.
`ROC_or_precrec`	String. To perform ROC or precision/recall analysis. Accepted values are "ROC" or "prec_rec". No default.
`models`	Model names as character vector. Defaults to c("pred_con_train", "pred_cat_train", "pred_con_test", "pred_cat_test", "tc")
`pretty_names`	Names to be used in plots. As character vector. Defaults to c("SuperLearner continuous prediction", "SuperLearner priority levels", "SuperLearner continuous prediction", "SuperLearner priority levels", "Clinicians priority levels")
`subscript`	Logical. If TRUE, underscores in pretty names in converted to expression. Passed to rocr.plots. Defaults to FALSE.
`models_to_invert`	Character vector. Names of models to invert. Defaults to NULL.