# roc

0th

Percentile

##### Compute the points for an ROC curve

Computes sensitivity and specificity for a variety of cutoffs

Keywords
manip
##### Usage
roc(data, class, dataGrid = TRUE, gridLength = 100, positive = levels(class)[1])
##### Arguments
data
a numeric variable to cut along
class
a factor with class memberships. There must be only two classes.
dataGrid
should the data define the grid of cut-points? If not a sequence of evenly spaced intervals is used.
gridLength
number of intervals to use if the data do not define the grid.
positive
a character string for the level of the class variable that defines a "positive" event
##### Value

• A matrix of results with columns "cutoff", "sensitivity" and "specificity"

##### Note

The first row in the output has a cutoff of NA, zero specificity and sensitivity of one.

sensitivity, specificity, aucRoc

• roc
##### Examples
set.seed(6)
testData <- data.frame(x = c(rnorm(200), rnorm(200) + 1),
group = factor(rep(letters[1:2], each = 200)))

densityplot(~testData$x, groups = testData$group, auto.key = TRUE)

roc(testData$x, testData$group)
Documentation reproduced from package caret, version 4.47, License: GPL-2

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