Skip to contents

Add NpAUC lower bound to a ROC plot

npauc_lower_bounds.Rd

Calculate and plot lower bound defined by NpAUC specificity index.

  • add_npauc_normalized_lower_bound() allows to plot normalized lower bound, which is used to formally calculate NpAUC.

  • add_npauc_lower_bound() is a lower level function providing a way to plot lower bound previous to normalization.

Usage

add_npauc_lower_bound(
  data,
  response = NULL,
  predictor = NULL,
  threshold,
  .condition = NULL,
  .label = NULL
)

add_npauc_normalized_lower_bound(
  data,
  response = NULL,
  predictor = NULL,
  threshold,
  .condition = NULL,
  .label = NULL
)

Arguments

data

A data.frame or extension (e.g. a tibble) containing values for predictors and response variables.

response

A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard).

If the variable presents more than two possible outcomes, classes or categories:

  • The outcome of interest (the one to be predicted) will remain distinct.

  • All other categories will be combined into a single category.

New combined category represents the "absence" of the condition to predict. See .condition for more information.

predictor

A data variable which must be numeric, representing values of a classifier or predictor for each observation.

threshold

A number between 0 and 1, inclusive. This number represents the lower value of TPR for the region where to calculate and plot lower bound.

Because of definition of np_auc(), region upper bound will be established as 1.

.condition

A value from response that represents class, category or condition of interest which wants to be predicted.

If NULL, condition of interest will be selected automatically depending on response type.

Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted.

See vignette("selecting-condition") for further information on how automatic selection is performed and details on selecting the condition of interest.

.label

A string representing the name used in labels.

If NULL, variable name from predictor will be used as label.

Examples

plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
  add_npauc_lower_bound(
    iris,
    response = Species,
    predictor = Sepal.Width,
    threshold = 0.9
  )