di_iterate {DisImpact} | R Documentation |
Iteratively calculate disproportionate impact via the percentage point gap (PPG), proportionality index, and 80% index methods for many success variables, disaggregation variables, and scenarios.
di_iterate(
data,
success_vars,
group_vars,
cohort_vars = NULL,
scenario_repeat_by_vars = NULL,
exclude_scenario_df = NULL,
weight_var = NULL,
include_non_disagg_results = TRUE,
ppg_reference_groups = "overall",
min_moe = 0.03,
use_prop_in_moe = FALSE,
prop_sub_0 = 0.5,
prop_sub_1 = 0.5,
di_prop_index_cutoff = 0.8,
di_80_index_cutoff = 0.8,
di_80_index_reference_groups = "hpg",
check_valid_reference = TRUE,
parallel = FALSE,
parallel_n_cores = parallel::detectCores(),
parallel_split_to_disk = FALSE
)
data |
A data frame for which to iterate DI calculations 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 |
(Optional) A character vector of the same length as |
scenario_repeat_by_vars |
(Optional) A character vector of variables to repeat DI calculations for across all combination of these variables. For example, the following variables could be specified:
Each combination of these variables (eg, full time, first time college students with an ed goal of degree/transfer as one combination) would constitute an iteration / sample for which to calculate disproportionate impact for outcomes listed in |
exclude_scenario_df |
(Optional) A data frame with variables that match |
weight_var |
(Optional) A character variable specifying the weight variable if the input data set is summarized (i.e., the the success variables specified in |
include_non_disagg_results |
A logical variable specifying whether or not the non-disaggregated results should be returned; defaults to |
ppg_reference_groups |
Either |
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; defaults to 0.50. |
prop_sub_1 |
passed to di_ppg; defaults to 0.50. |
di_prop_index_cutoff |
Threshold used for determining disproportionate impact using the proportionality index; passed to di_prop_index; defaults to 0.80. |
di_80_index_cutoff |
Threshold used for determining disproportionate impact using the 80% index; passed to di_80_index; defaults to 0.80. |
di_80_index_reference_groups |
Either |
check_valid_reference |
Check whether |
parallel |
If |
parallel_n_cores |
The number of CPU cores to use if |
parallel_split_to_disk |
If |
Iteratively calculate disproportionate impact via the percentage point gap (PPG), proportionality index, and 80% index methods for all combinations of success_vars
, group_vars
, and cohort_vars
, for each combination of subgroups specified by scenario_repeat_by_vars
.
A summarized data set (data frame) consisting of:
success_variable
(elements of success_vars
),
disaggregation
(elements of group_vars
),
cohort
(values corresponding to the variables specified in cohort_vars
,
di_indicator_ppg
(1 if there is disproportionate impact per the percentage point gap method, 0 otherwise),
di_indicator_prop_index
(1 if there is disproportionate impact per the proportionality index, 0 otherwise),
di_indicator_80_index
(1 if there is disproportionate impact per the 80% index, 0 otherwise), and
other relevant fields returned from di_ppg, di_prop_index, and di_80_index.
library(dplyr)
data(student_equity)
# Multiple group variables
di_iterate(data=student_equity, success_vars=c('Transfer')
, group_vars=c('Ethnicity', 'Gender'), cohort_vars=c('Cohort')
, ppg_reference_groups='overall')