roc {caret} | R Documentation |
Computes sensitivity and specificity for a variety of cutoffs
roc(data, class, dataGrid = TRUE, gridLength = 100, positive = levels(class)[1])
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 |
A matrix of results with columns "cutoff", "sensitivity" and "specificity"
The first row in the output has a cutoff of NA
, zero sensitivity and specificity of one.
Max Kuhn
sensitivity
, specificity
, aucRoc
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)