Iteratively calculate disproportionate impact via the percentage point gap (PPG) method for many disaggregation variables.
di_ppg_iterate(
data,
success_vars,
group_vars,
cohort_vars,
reference_groups,
repeat_by_vars = NULL,
weight_var = NULL,
min_moe = 0.03,
use_prop_in_moe = FALSE,
prop_sub_0 = 0.5,
prop_sub_1 = 0.5
)
A data frame with all relevant returned fields from `di_ppg` plus `success_variable` (elements of `success_vars`), `disaggregation` (elements of `group_vars`), and `reference_group` (elements of `reference_groups`).
A data frame for which to iterate DI calculation for a set of variables.
A character vector of success variable names to iterate across.
A character vector of group (disaggregation) variable names to iterate across.
A character vector of cohort variable names to iterate across.
Either 'overall', 'hpg', or a character vector of the same length as `group_vars` that indicates the reference group value for each group variable in `group_vars`.
A character vector of variables to repeat DI calculations for across all combination of these variables, including '- All' as a group for each variable. The reference rate used for DI comparison differs for every combination of the variables listed here.
A character scalar specifying the weight variable if the input data set is summarized (ie, the the success variables specified in `success_vars` contain count of successes). Weight here corresponds to the denominator when calculating the success rate. Defaults to `NULL` for an input data set where each row describes each individual.
The minimum margin of error to be used in the PPG calculation, passed to `di_ppg`.
Whether the estimated proportions should be used in the margin of error calculation by the PPG, passed to `di_ppg`.
Passed to `di_ppg`.
Passed to `di_ppg`.
Iteratively calculate disproportionate impact via the percentage point gap (PPG) method for all combinations of `success_vars`, `group_vars`, and `cohort_vars`, for each combination of subgroups specified by `repeat_by_vars`.
library(dplyr)
data(student_equity)
# Multiple group variables
di_ppg_iterate(data=student_equity, success_vars=c('Transfer')
, group_vars=c('Ethnicity', 'Gender'), cohort_vars=c('Cohort')
, reference_groups='overall')
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