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Simple ROC curve

Source: R/performance_roc.R
performance_roc.Rd

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

It returns the area under the curve (AUC) as a percentage, which corresponds to the probability that a randomly chosen observation of "condition 1" is correctly classified by the model as having a higher probability of being "condition 1" than a randomly chosen "condition 2" observation.

Applying as.data.frame() to the output returns a data frame containing the following:

  • Sensitivity (that actually corresponds to 1 - Specificity): It is the False Positive Rate.

  • Sensitivity: It is the True Positive Rate, which is the proportion of correctly classified "condition 1" observations.

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))
#>    Sensitivity Specificity   Model
#> 1    0.0000000  0.00000000 Model 1
#> 2    0.1428571  0.00000000 Model 1
#> 3    0.1428571  0.09090909 Model 1
#> 4    0.1428571  0.18181818 Model 1
#> 5    0.1428571  0.27272727 Model 1
#> 6    0.1428571  0.36363636 Model 1
#> 7    0.2857143  0.36363636 Model 1
#> 8    0.2857143  0.45454545 Model 1
#> 9    0.2857143  0.54545455 Model 1
#> 10   0.2857143  0.63636364 Model 1
#> 11   0.2857143  0.72727273 Model 1
#> 12   0.4285714  0.72727273 Model 1
#> 13   0.5714286  0.72727273 Model 1
#> 14   0.5714286  0.81818182 Model 1
#> 15   0.7142857  0.81818182 Model 1
#> 16   0.8571429  0.81818182 Model 1
#> 17   0.8571429  0.90909091 Model 1
#> 18   1.0000000  0.90909091 Model 1
#> 19   1.0000000  1.00000000 Model 1
#> 20   1.0000000  1.00000000 Model 1

roc <- performance_roc(model, new_data = test_data)
area_under_curve(roc$Specificity, roc$Sensitivity)
#> [1] 0.3766234

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))
}