# AUC

0th

Percentile

##### Area Under the ROC Curve.

Given a vector of false-positive rates and a vector of true-positive rates, calculate the area under the Receiver Operator Characteristic (ROC) curve.

##### Usage
AUC(FPR, TPR)
##### Arguments
FPR

Vector of False-Positive Rates.

TPR

Vector of True-Positive Rates.

##### Value

A value representing the area under the ROC curve.

##### Note

Script originally retrieved and modified from https://blog.revolutionanalytics.com/2016/11/calculating-auc.html.

• AUC
##### Examples
# NOT RUN {
# Generate some fictional data. Say, 100 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
testdata <- rbinom(100, 100, rBeta.4P(100, .25, .75, 5, 3))
hist(testdata, xlim = c(0, 100))

# Suppose the cutoff value for attaining a pass is 50 items correct, and
# that the reliability of this test was estimated to 0.7. To calculate the
# necessary (x, y) coordinates to compute the area under the curve statistic
# one can use the LL.ROC() function with the argument
# raw.out = TRUE.
coords <- LL.ROC(x = testdata, reliability = .7, truecut = 50, min = 0,
max = 100, raw.out = TRUE)

# To calculate and retrieve the Area Under the Curve (AUC) with the AUC()
# function, feed it the raw coordinates calculated above.
AUC(coords[, "FPR"], coords[, "TPR"])
# }

Documentation reproduced from package betafunctions, version 1.4.0, License: CC0

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