aggregate_risk {ecorisk} | R Documentation |
Compute High-Complexity Multi-Risk Scores and (Eco)system Risk
Description
The function aggregate_risk
uses the output of the risk function or
the vulnerability function. The risk or vulnerability scores are aggregated
in three ways:
as multi-pressure risk per state indicator, representing the overall effect on one indicator
as multi- state indicator risk per pressure, representing the overall effect each pressure has on all state indicators
as ecosystem risk, the combined multi-pressure risks of all indicators.
Usage
aggregate_risk(risk_results, method = "mean")
Arguments
risk_results |
Risk score for each state indicator ~ pressure ~
type combination, derived from the function |
method |
Character indicating the method for aggregating the pressures and
state indicators to multiple risk scores and the ecosystem risk score.
The use can choose between (arithmetic) |
Details
The returned lists are required in the plotting functions plot_radar
and plot_heatmap
.
The aggregated scores are calculated for each type and pathway combination
individually and across all types and pathways. If only one type and/or one
pathway has been evaluated beforehand, the results will be the same for the
different combinations.
Value
The function returns a list containing three sublists, first the multi-state indicator risk list, containing the risks and uncertainties aggregated per pressure, type and pathway. Second the multi-pressure risk list, where risks and uncertainties are aggregated per state indicator and type and pathway. The third list contains the ecosystem risks, which aggregates the multi-pressure risk and uncertainty scores per type and pathway.
See Also
vulnerability
, risk
,
plot_radar
, plot_heatmap
Examples
### Demo with example output from the risk() function based on expert scores
# (where direct and direct/indirect effects were evaluated)
# Calculate mean risks scores per indicator/pressure/ecosystem:
mean_risk <- aggregate_risk(
risk_results = ex_output_risk_expert,
method = "mean" # default
)
mean_risk
# Calculate median risks scores:
aggregate_risk(
risk_results = ex_output_risk_expert,
method = "median"
)
# Calculate maximum risks scores:
aggregate_risk(
risk_results = ex_output_risk_expert,
method = "maximum"
)
### Demo with example output from the risk() function based on modelled
# scores (where only direct/indirect effects were evaluated)
# Calculate mean risks scores:
aggregate_risk(risk_results = ex_output_risk_model)
### Demo with combined expert-based and model-based pathways
combined_risk <- rbind(ex_output_risk_expert, ex_output_risk_model)
aggr_risk <- aggregate_risk(risk_results = combined_risk)
aggr_risk
aggr_risk$multi_indicator_risk |>
dplyr::filter(type == "combined", pathway == "combined")
aggr_risk$multi_pressure_risk |>
dplyr::filter(type == "combined", pathway == "combined")
aggr_risk$ecosystem_risk |>
dplyr::filter(type == "combined", pathway == "combined")
### Demo with vulnerability scores using example output data from
# vulnerability() based on modelled scores
aggregate_risk(risk_results = ex_output_vulnerability_model)