--- title: Running the analysis output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Running the analysis} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Config All of the functions take a `config` argument, which can either be a path to a `config.yml` file (see `vignette("config_yml")`) or a config list object containing previously imported settings from a `config.yml` file. All of the settings/options are configured with this `config.yml` file. # Setup You'll likely want to load the package and save the path to the `config.yml` file in a variable first: ```r library(pacta.multi.loanbook) config_path <- "config.yml" ``` # Data preparation Your ABCD data will need to be prepared first with the `prepare_abcd()` function. If you want to use a custom sector split, that will also need to be prepared with the `prepare_sector_split()` function: ```r prepare_abcd(config_path) prepare_sector_split(config_path) ``` # Matching process To run the matching process, you will use the `run_matching()` function. ```r run_matching(config_path) ``` After the matching process is complete, you will need to do some manual matching. # Prioritization To prioritize the data in your loanbooks, you will use the `run_match_prioritize()` function. ```r run_match_prioritize(config_path) ``` # Match success and coverage stats After the matching and prioritization process is complete, you may want to review the match success rate and loanbook coverage stats. This can be done with the `run_calculate_match_success_rate()` and `run_calculate_loanbook_coverage()` functions. In case you are not satisfied with your match success rate, you may have to go back to the manual matching process and try to match additional loans. You can then rerun `run_calculate_match_success_rate()` and `run_calculate_loanbook_coverage()` to check if your additional matching has improved coverage. ```r run_calculate_match_success_rate(config_path) run_calculate_loanbook_coverage(config_path) ``` # PACTA analysis To run PACTA on all of your previously matched and prioritized loanbooks, you will use the `run_pacta()` function. ```r run_pacta(config_path) ``` # PACTA metrics and plots Once the PACTA analysis has been run on your loanbooks, you may want to calculate aggregate alignment metrics and export some plots, which can be done with the `run_aggregate_alignment_metric()` and `plot_aggregate_loanbooks()` functions. ```r run_aggregate_alignment_metric(config_path) plot_aggregate_loanbooks(config_path) ```