oppr (version 1.0.0)

solution_statistics: Solution statistics

Description

Calculate statistics describing a solution to a project prioritization problem.

Usage

solution_statistics(x, solution)

Arguments

x

project prioritization problem.

solution

data.frame or tibble table containing the solutions. Here, rows correspond to different solutions and columns correspond to different actions. Each column in the argument to solution should be named according to a different action in x. Cell values indicate if an action is funded in a given solution or not, and should be either zero or one. Arguments to solution can contain additional columns, and they will be ignored.

Value

A tibble table containing the following columns:

"cost"

numeric cost of each solution.

"obj"

numeric objective value for each solution. This is calculated using the objective function defined for the argument to x.

x$project_names()

numeric column for each project indicating if it was completely funded (with a value of 1) or not (with a value of 0).

x$feature_names()

numeric column for each feature indicating the probability that it will persist into the future given each solution.

See Also

objectives, replacement_costs, project_cost_effectiveness.

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)

# create a table with some solutions
solutions <- data.frame(F1_action =       c(0, 1, 1),
                        F2_action =       c(0, 1, 0),
                        F3_action =       c(0, 1, 1),
                        F4_action =       c(0, 1, 0),
                        F5_action =       c(0, 1, 1),
                        baseline_action = c(1, 1, 1))

# print the solutions
# the first solution only has the baseline action funded
# the second solution has every action funded
# the third solution has only some actions funded
print(solutions)

# calculate statistics
solution_statistics(p, solutions)
# }