Run an OpenFAIR model with parameters provided for TEF, TC, DIFF, and LM sampling. If there are multiple controls provided for the scenario, the arithmetic mean (average) is taken across samples for all controls to get the effective control strength for each threat event.

openfair_tef_tc_diff_lm(tef, tc, diff, lm, n = 10^4, verbose = FALSE)

Arguments

tef

Parameters for TEF simulation

tc

Parameters for TC simulation

diff

Parameters for DIFF simulation

lm

Parameters for LM simulation

n

Number of iterations to run.

verbose

Whether to print progress indicators.

Value

Dataframe of scenario name, threat_event count, loss_event count, mean TC and DIFF exceedance, and ALE samples.

See also

Other OpenFAIR helpers: compare_tef_vuln, get_mean_control_strength, sample_diff, sample_lef, sample_lm, sample_tc, sample_vuln, select_loss_opportunities

Examples

data(mc_quantitative_scenarios) params <- mc_quantitative_scenarios[[1, "scenario"]]$parameters openfair_tef_tc_diff_lm(params$tef, params$tc, params$diff, params$lm, 10)
#> # A tibble: 10 x 11 #> iteration threat_events loss_events vuln mean_tc_exceeda… mean_diff_excee… #> <int> <int> <int> <dbl> <dbl> <dbl> #> 1 1 30 5 0.167 0.0463 0.0690 #> 2 2 30 11 0.367 0.0323 0.0723 #> 3 3 23 7 0.304 0.0249 0.0842 #> 4 4 19 11 0.579 0.0330 0.0604 #> 5 5 24 4 0.167 0.0281 0.0759 #> 6 6 36 14 0.389 0.0255 0.0633 #> 7 7 32 7 0.219 0.0405 0.0580 #> 8 8 31 8 0.258 0.0210 0.0711 #> 9 9 41 11 0.268 0.0299 0.0660 #> 10 10 20 4 0.2 0.0459 0.0855 #> # … with 5 more variables: ale <dbl>, sle_mean <dbl>, sle_median <dbl>, #> # sle_max <dbl>, sle_min <dbl>