di_iterate_dt {DisImpact} | R Documentation |

## Iteratively calculate disproportionate impact using multiple method for many variables, using data.table and collapse.

### Description

Iteratively calculate disproportionate impact via the percentage point gap (PPG), proportionality index, and 80% index methods for many success variables, disaggregation variables, and scenarios, using data.table and collapse.

### Usage

```
di_iterate_dt(
dt,
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()/2
)
```

### Arguments

`dt` |
A data frame of class data.table. If the object is not a data table, one could surround the object with as.data.table. |

`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: Ed Goal: Degree/Transfer, Shot-term Career, Non-credit First time college student: Yes, No Full-time status: Yes, No
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; see di_ppg. |

`use_prop_in_moe` |
( |

`prop_sub_0` |
Default is 0.50; see di_ppg. |

`prop_sub_1` |
Default is 0.50; see di_ppg. |

`di_prop_index_cutoff` |
Threshold used for determining disproportionate impact using the proportionality index; see di_prop_index; defaults to 0.80. |

`di_80_index_cutoff` |
Threshold used for determining disproportionate impact using the 80% index; see di_80_index; defaults to 0.80. |

`di_80_index_reference_groups` |
Either |

`check_valid_reference` |
( |

`parallel` |
If |

`parallel_n_cores` |
The number of CPU cores to use if |

### Details

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`

, using data.table and collapse.

### Value

A summarized data set of class data.table, with variables as described in di_iterate.

*DisImpact*version 0.0.21 Index]