identify_outliers: Unnest a summarized results dataframe, adding outlier...

In evaluator: Quantified Risk Assessment Toolkit

Description

Given a summarized results dataframe, unnest the summary results column and use the value at risk (VaR) column to identify all the elements that are outliers (having a VaR >= two standard deviations)

Usage

identify_outliers(results)

Arguments

results

Scenario summary results

Value

The supplied dataframe with the following additional columns:

• ale_var_zscore - Annual loss z-score

• outlier - Logical flag when the z-score is greater than or equal to two

Examples

data(mc_scenario_summary)
identify_outliers(mc_scenario_summary)

Example output

# A tibble: 56 x 20
   scenario_id domain_id control_descrip~ results loss_events_mean
   <chr>       <chr>     <list>           <list>             <dbl>
 1 RS-01       ORG       <list [7]>       <tibbl~            7.48 
 2 RS-02       ORG       <list [7]>       <tibbl~            7.48 
 3 RS-03       ORG       <list [7]>       <tibbl~            1.72 
 4 RS-04       ORG       <list [8]>       <tibbl~            2.73 
 5 RS-05       ORG       <list [5]>       <tibbl~            4.21 
 6 RS-06       POL       <list [4]>       <tibbl~            0    
 7 RS-07       POL       <list [4]>       <tibbl~            0    
 8 RS-08       POL       <list [4]>       <tibbl~            0.042
 9 RS-09       COMP      <list [2]>       <tibbl~            0    
10 RS-10       COMP      <list [2]>       <tibbl~            1.90 
# ... with 46 more rows, and 15 more variables: loss_events_median <dbl>,
#   loss_events_min <dbl>, loss_events_max <dbl>, ale_median <dbl>,
#   ale_max <dbl>, ale_var <dbl>, sle_mean <dbl>, sle_median <dbl>,
#   sle_min <dbl>, sle_max <dbl>, mean_tc_exceedance <dbl>,
#   mean_diff_exceedance <dbl>, mean_vuln <dbl>, ale_var_zscore <dbl>,
#   outlier <lgl>

evaluator documentation built on July 6, 2021, 9:06 a.m.