Given a dataframe of raw results from run_simulations, summarize
the individual results at a per-iteration level.
summarize_iterations(simulation_result, ..., .key = "iteration")Results object for a single scenario.
Additional simulation result objects to summarize.
Iteration ID field
Dataframe.
Summary stats created include: * Mean/Min/Max/Median are calculated for loss events * Median/Max/VaR are calculated for annual loss expected (ALE) * Mean/Median/Max/Min are calculated for single loss expected (SLE) * Mean percentage of threat capability exceeding difficulty on successful threat events * Mean percentage of difficulty exceeding threat capability on defended events * Vulnerability percentage * Z-score of ALE (outliers flagged as 2 >= z-score)
data(mc_simulation_results)
summarize_iterations(mc_simulation_results$results)
#> # A tibble: 1,000 × 11
#> iteration largest_single_scenario_loss min_loss max_loss outliers ale_sum
#> <int> <dbl> <dbl> <dbl> <lgl> <dbl>
#> 1 1 2987483. 0 13247898. TRUE 13247898.
#> 2 2 3159502. 0 13571376. TRUE 13571376.
#> 3 3 2937112. 0 14084157. TRUE 14084157.
#> 4 4 2142845. 0 9469525. TRUE 9469525.
#> 5 5 2610111. 0 10066804. TRUE 10066804.
#> 6 6 4000900. 0 16928496. TRUE 16928496.
#> 7 7 3812536. 0 18283797. TRUE 18283797.
#> 8 8 3705607. 0 16948823. TRUE 16948823.
#> 9 9 3888987. 0 20748512. TRUE 20748512.
#> 10 10 2440504. 0 10966272. TRUE 10966272.
#> # … with 990 more rows, and 5 more variables: mean_tc_exceedance <dbl>,
#> # mean_diff_exceedance <dbl>, loss_events <int>, threat_events <int>,
#> # avoided_events <int>