Examine predictive equality of a model
eval_pred_equality.Rd
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