Examine treatment equality of a model

Usage

eval_treatment_equality(
  data,
  outcome,
  group,
  probs,
  cutoff = 0.5,
  alpha = 0.05,
  bootstraps = 2500,
  digits = 2,
  message = TRUE
)

data: Data frame containing the outcome, predicted outcome, and sensitive attribute
outcome: Name of the outcome variable
group: Name of the sensitive attribute
probs: Predicted probabilities
cutoff: Cutoff value for the predicted probabilities
alpha: The 1 - significance level for the confidence interval, default is 0.05
bootstraps: Number of bootstraps to use for confidence intervals
digits: Number of digits to round the results to, default is 2
message: Whether to print the results, default is TRUE
confint: Logical indicating whether to calculate confidence intervals

A list containing the following elements:

False Negative / False Positive ratio for Group 1
False Negative / False Positive ratio for Group 2
Difference in False Negative / False Positive ratio If confidence intervals are computed (confint = TRUE):
A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in False Negative / False Positive ratio