spauc_lower_bounds | R Documentation |
Calculate and plot lower bound defined by SpAUC specificity index.
add_spauc_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
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:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold , upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If 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 |
.label |
A string representing the name used in labels. If |
SpAUC presents some limitations regarding its lower bound. Lower bound defined by this index cannot be applied to sections where ROC curve is defined under chance line.
add_spauc_lower_bound()
doesn't make any check to ensure the index can be
safely applied. Consequently, it allows to enforce the representation even
though SpAUC cound't be calculated in the region.
A ggplot layer instance object.
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_spauc_lower_bound(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0,
upper_threshold = 0.1
)
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