calc_curve_shape | R Documentation |
calc_curve_shape()
calculates ROC curve shape over a specified region.
calc_curve_shape(
data = NULL,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
ratio,
.condition = 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. |
ratio |
Ratio or axis 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 |
A string indicating ROC curve shape in the specified region. Result can take any of the following values:
"Concave"
. ROC curve is concave over the entire specified region.
"Partially proper"
. ROC curve loses concavity at some point of the
specified region.
"Hook under chance"
. ROC curve loses concavity at some point of the
region and it lies below chance line.
# Calc ROC curve shape of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
calc_curve_shape(iris, Species, Sepal.Width, 0.9, 1, "tpr")
# Change class to virginica
calc_curve_shape(iris, Species, Sepal.Width, 0.9, 1, "tpr", .condition = "virginica")
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