Simple ROC curve
This function calculates a simple ROC curves of x/y coordinates based on response and predictions of a binomial model.
performance_roc(x, ..., predictions, new_data)
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,
|
predictions |
If |
new_data |
If |
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.
There is also a plot()-method implemented in the see-package.
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)
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 \code{see} package installed, you can also plot comparison of
# ROC curves for different models
# if (require("see")) plot(performance_roc(m1, m2, m3))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.