View source: R/performance_roc.R
performance_roc  R Documentation 
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/ycoordinate pairs for the ROC
curve (Sensitivity
and Specificity
), and a column with the
model name.
There is also a plot()
method implemented in the seepackage.
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 `see` package installed, you can also plot comparison of # ROC curves for different models if (require("see")) plot(performance_roc(m1, m2, m3))
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