oppr (version 0.0.4)

project_cost_effectiveness: Project cost effectiveness

Description

Calculate the individual cost-effectiveness of each conservation project in a project prioritization problem (Joseph, Maloney & Possingham 2009).

Usage

project_cost_effectiveness(x)

Arguments

x

project prioritization problem.

Value

A tibble table containing the following columns:

"project"

character name of each project

"cost"

numeric cost of each project.

"benefit"

numeric benefit for each project. For a given project, this is calculated as the difference between (i) the objective value for a solution containing all of the management actions associated with the project and all zero cost actions, and (ii) the objective value for a solution containing the baseline project.

"ce"

numeric cost-effectiveness of each project. For a given project, this is calculated as the difference between the the benefit for the project and the benefit for the baseline project, divided by the cost of the project. Note that the baseline project will have a NaN value because it has a zero cost.

"rank"

numeric rank for each project according to is cost-effectiveness value. The project with a rank of one is the most cost-effective project. Ties are accommodated using averages.

Details

Note that project cost-effectiveness cannot be calculated for problems with minimum set objectives because the objective function for these problems is to minimize cost and not maximize some measure of biodiversity persistence.

References

Joseph LN, Maloney RF & Possingham HP (2009) Optimal allocation of resources among threatened species: A project prioritization protocol. Conservation Biology, 23, 328--338.

See Also

solution_statistics, replacement_costs.

Examples

# NOT RUN {
# load data
data(sim_projects, sim_features, sim_actions)

# print project data
print(sim_projects)

# print action data
print(sim_features)

# print feature data
print(sim_actions)

# build problem
p <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 400) %>%
     add_feature_weights("weight") %>%
     add_binary_decisions()

# print problem
print(p)

# calculate cost-effectiveness of each project
pce <- project_cost_effectiveness(p)

# print project costs, benefits, and cost-effectiveness values
print(pce)

# plot histogram of cost-effectiveness values
hist(pce$ce, xlab = "Cost effectiveness", main = "")
# }