Examine statistical parity of a model

Usage

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

data: Data frame containing the outcome, predicted outcome, and sensitive attribute
outcome: Name of the outcome variable, it must be binary
group: Name of the sensitive attribute
probs: Name of the predicted outcome variable
cutoff: Threshold for the predicted outcome, default is 0.5
bootstraps: Number of bootstrap samples, default is 2500
alpha: The 1 - significance level for the confidence interval, default is 0.05
digits: Number of digits to round the results to, default is 2
message: Whether to print the results, default is TRUE
confint: Whether to compute 95% confidence interval, default is TRUE

A list containing the following elements:

PPR_Group1: Positive Prediction Rate for the first group
PPR_Group2: Positive Prediction Rate for the second group
PPR_Diff: Difference in Positive Prediction Rate If confidence intervals are computed (confint = TRUE):
PPR_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Positive Prediction Rate