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Examine predictive equality of a model

Usage

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

Arguments

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

alpha

The 1 - significance level for the confidence interval, default is 0.05

bootstraps

Number of bootstrap samples, default is 2500

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

Value

A list containing the following elements:

  • FPR_Group1: False Positive Rate for the first group

  • FPR_Group2: False Positive Rate for the second group

  • FPR_Diff: Difference in False Positive Rate If confidence intervals are computed (confint = TRUE):

  • FPR_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in False Positive Rate