di_ppg_iterate: Iteratively calculate disproportionate impact via the...

In DisImpact: Calculates Disproportionate Impact When Binary Success Data are Disaggregated by Subgroups

Iteratively calculate disproportionate impact via the percentage point gap (PPG) method for many variables.

Description

Iteratively calculate disproportionate impact via the percentage point gap (PPG) method for many disaggregation variables.

Usage

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
)

Arguments

data	A data frame for which to iterate DI calculation for a set of variables.
success_vars	A character vector of success variable names to iterate across.
group_vars	A character vector of group (disaggregation) variable names to iterate across.
cohort_vars	A character vector of cohort variable names to iterate across.
reference_groups	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'.
repeat_by_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.
weight_var	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.
min_moe	The minimum margin of error to be used in the PPG calculation, passed to 'di_ppg'.
use_prop_in_moe	Whether the estimated proportions should be used in the margin of error calculation by the PPG, passed to 'di_ppg'.
prop_sub_0	Passed to 'di_ppg'.
prop_sub_1	Passed to 'di_ppg'.

Details

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'.

Value

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').

Examples

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')

DisImpact documentation built on Oct. 11, 2022, 1:06 a.m.