nb_plot: Produce a net benefit plot for a set of predicted...

In gweissman/gmish: Miscellaneous Functions for Predictive Modeling

Produce a net benefit plot for a set of predicted probabilities for one or more binary classifiers.

Description

Produce a net benefit plot for a set of predicted probabilities for one or more binary classifiers.

Usage

nb_plot(
  form,
  data,
  treat_all = TRUE,
  treat_none = TRUE,
  omniscient = TRUE,
  weight = 1L,
  max_neg = 0.1
)

Arguments

form	A formula where the left-hand side is the variable representing the observed outcome, 0 or 1, and the right-hand side represents the column names of the different model probabilities.
data	A data frame that contains at least two columns, one of which is the observed outcome and the others that are predicted probabilities.
treat_all	Whether or not to include a line indicating the net benefit of a model that treats everyone Default = TRUE
treat_none	Whether or not to include a line indicating the net benefit of a model that treats no one. Default = TRUE
omniscient	Whether or not to include a line indicating the net benefit of a model that guesses the actual observed outcome for each prediction. Default = TRUE
weight	Relative weighted importance of true positives to false positives. When weight = 1, the original net benefit calculation is used. Default = 1
max_neg	The lower y-range below y = 0 that is plotted as a proportion of the highest possible net benefit. Default = 0.1

Examples

m1 <- glm(mpg > 20 ~ cyl + disp + hp, family = 'binomial', data = mtcars)
results <- data.frame(outcome = mtcars$mpg > 20, lr_1 = predict(m1, type = 'response'))
nb_plot(outcome ~ lr_1, data = results)

gweissman/gmish documentation built on Feb. 21, 2025, 1:20 a.m.