performance_roc: Simple ROC curve

In performance: Assessment of Regression Models Performance

Simple ROC curve

Description

This function calculates a simple ROC curves of x/y coordinates based on response and predictions of a binomial model.

Usage

performance_roc(x, ..., predictions, new_data)

Arguments

x	A numeric vector, representing the outcome (0/1), or a model with binomial outcome.
...	One or more models with binomial outcome. In this case, new_data is ignored.
predictions	If x is numeric, a numeric vector of same length as x, representing the actual predicted values.
new_data	If x is a model, a data frame that is passed to predict() as newdata-argument. If NULL, the ROC for the full model is calculated.

Value

A data frame with three columns, the x/y-coordinate pairs for the ROC curve (Sensitivity and Specificity), and a column with the model name.

Note

There is also a plot()-method implemented in the see-package.

Examples

library(bayestestR)
data(iris)

set.seed(123)
iris$y <- rbinom(nrow(iris), size = 1, .3)
folds <- sample(nrow(iris), size = nrow(iris) / 8, replace = FALSE)
test_data <- iris[folds, ]
train_data <- iris[-folds, ]

model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
as.data.frame(performance_roc(model, new_data = test_data))

roc <- performance_roc(model, new_data = test_data)
area_under_curve(roc$Specificity, roc$Sensitivity)

if (interactive()) {
  m1 <- glm(y ~ Sepal.Length + Sepal.Width, data = iris, family = "binomial")
  m2 <- glm(y ~ Sepal.Length + Petal.Width, data = iris, family = "binomial")
  m3 <- glm(y ~ Sepal.Length + Species, data = iris, family = "binomial")
  performance_roc(m1, m2, m3)

  # if you have `see` package installed, you can also plot comparison of
  # ROC curves for different models
  if (require("see")) plot(performance_roc(m1, m2, m3))
}

performance documentation built on June 22, 2024, 10:55 a.m.