Examine conditional statistical parity of a model
eval_cond_stats_parity.Rd
Examine conditional statistical parity of a model
Usage
eval_cond_stats_parity(
data,
outcome,
group,
group2,
condition,
probs,
cutoff = 0.5,
bootstraps = 2500,
alpha = 0.05,
message = TRUE,
digits = 2
)
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
- group2
Name of the group to condition on
- condition
If the conditional group is categorical, the condition supplied must be a character of the levels to condition on. If the conditional group is continuous, the conditions supplied must be a character containing the sign of the condition and the value to threshold the continuous variable (e.g. "<50", ">50", "<=50", ">=50").
- 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
- message
Whether to print the results, default is TRUE
- digits
Number of digits to round the results to, default is 2
Value
A list containing the following elements:
Conditions: The conditions used to calculate the conditional PPR
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