cutpointr (version 0.7.3)

roc: Calculate a ROC curve

Description

Given a data.frame with a numeric predictor variable and a binary outcome variable this function returns a data.frame that includes all elements of the confusion matrix (true positives, false positives, true negatives, and false negatives) for every unique value of the predictor variable. Additionally, the true positive rate (tpr), false positive rate (fpr), true negative rate (tnr) and false negative rate (fnr) are returned.

Usage

roc(data, x, class, pos_class, neg_class, direction = ">=", silent = FALSE)

Arguments

data

A data.frame or matrix. Will be converted to a data.frame.

x

(character) The numeric independent (predictor) variable.

class

(character) A binary vector of outcome values.

pos_class

The value of 'class' that represents the positive cases.

neg_class

The value of 'class' that represents the negative cases.

direction

(character) One of ">=" or "<=". Specifies if the positive class is associated with higher values of x (default).

silent

If FALSE and the ROC curve contains no positives or negatives, a warning is generated.

Value

A data frame with the columns x.sorted, tp, fp, tn, fn, tpr, tnr, fpr, and fnr.

Details

To enable classifying all observations as belonging to only one class the predictor values will be augmented by Inf or -Inf.

See Also

Other main cutpointr functions: cutpointr_, cutpointr, multi_cutpointr, predict.cutpointr

Examples

# NOT RUN {
## First two classes of the iris data
dat <- iris[1:100, ]
roc(data = dat, x = "Petal.Width", class = "Species",
pos_class = "versicolor", neg_class = "setosa", direction = ">=")
# }