getPotentialBenefit {prioriactions}

Extract potential benefit of features

Description

Provides the maximum values of benefits to achieve for each feature given a set of data inputs.

Usage

getPotentialBenefit(x)

Arguments

x

data-class object.

Details

For a given feature s, let I_s be the set of planning units associated with s, let r_{is} is the amount of feature s in planning unit i, let K_{s} be the set of threats associated with s, and let K_{i} be the set of threats associated with i. The local benefit associated with s in a unit i is given by:

b_{is} = p_{is} r_{is} \\ b_{is} = \frac{ \sum_{k \in K_i \cap K_s}{x_{ik}}}{|K_i \cap K_s|} r_{is}

Where x_{ik} is a decision variable such that x_{ik} = 1 if an action againts threat k is applied in unit i, and x_{ik} = 0, otherwise. This expression for the probability of persistence of the feature (p_{is}) is defined only for the cases where we work with values of binary intensities (presence or absence of threats). See the sensitivities vignette to know the work with continuous intensities.

While the total benefit is calculated as the sum of the local benefits per feature:

b_{s} = \sum_{i \in I_{s}}\frac{ \sum_{k \in K_i \cap K_s}{x_{ik}}}{|K_i \cap K_s|} r_{is}

Since the potential benefit is being calculated, all variables x_{ik} are assumed to be equal to 1; that is, all possible actions are carried out, and only those that have a lock-out status are kept out of the planning (see inputData() function for more information).

Value

data.frame.

Examples

# set seed for reproducibility
set.seed(14)

## Load data
data(sim_pu_data, sim_features_data, sim_dist_features_data,
sim_threats_data, sim_dist_threats_data, sim_sensitivity_data,
sim_boundary_data)

## Create data instance
problem_data <- inputData(
  pu = sim_pu_data, features = sim_features_data, dist_features = sim_dist_features_data,
  threats = sim_threats_data, dist_threats = sim_dist_threats_data,
  sensitivity = sim_sensitivity_data, boundary = sim_boundary_data
)

## Get maximum benefits to obtain
getPotentialBenefit(problem_data)