# roc

From caret v3.45
by Max Kuhn

##### 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 sensitivity and specificity of one.

##### See Also

##### 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 3.45, License: GPL-2*

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