find_AUC_q | R Documentation |
Based on https://win-vector.com/2020/09/13/why-working-with-auc-is-more-powerful-than-one-might-think/
find_AUC_q(
modelPredictions,
yValues,
...,
na.rm = FALSE,
yTarget = TRUE,
n_points = 101
)
modelPredictions |
numeric predictions (not empty), ordered (either increasing or decreasing) |
yValues |
truth values (not empty, same length as model predictions) |
... |
force later arguments to bind by name. |
na.rm |
logical, if TRUE remove NA values. |
yTarget |
value considered to be positive. |
n_points |
number of points to use in estimates. |
q that such that curve 1 - (1 - (1-ideal_roc$Specificity)^q)^(1/q) matches area
d <- data.frame(pred = 1:4, truth = c(TRUE,FALSE,TRUE,TRUE))
q <- find_AUC_q(d$pred, d$truth)
roc <- build_ROC_curve(d$pred, d$truth)
ideal_roc <- data.frame(Specificity = seq(0, 1, length.out = 101))
ideal_roc$Sensitivity <- sensitivity_from_specificity_q(ideal_roc$Specificity, q)
# library(ggplot2)
# ggplot(mapping = aes(x = 1 - Specificity, y = Sensitivity)) +
# geom_line(data = roc, color = "DarkBlue") +
# geom_line(data = ideal_roc, color = "Orange") +
# theme(aspect.ratio=1) +
# ggtitle("example actual and ideal curve")
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