Examine conditional use accuracy equality of a model
eval_cond_acc_equality.Rd
Examine conditional use accuracy equality of a model
Usage
eval_cond_acc_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:
PPV_Group1: Positive Predictive Value for the first group
PPV_Group2: Positive Predictive Value for the second group
PPV_Diff: Difference in Positive Predictive Value
NPV_Group1: Negative Predictive Value for the first group
NPV_Group2: Negative Predictive Value for the second group
NPV_Diff: Difference in Negative Predictive Value If confidence intervals are computed (
confint = TRUE
):PPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Positive Predictive Value
NPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Negative Predictive Value