get_actions: Get action results from a solution set

In multiscape: Multi-Action, Multi-Objective Spatial Planning

Get action results from a solution set

Description

Extract the action-allocation summary table from a solutionset-class object returned by solve.

The returned table summarizes solution values at the planning unit–action level and typically includes a selected indicator showing whether each feasible (pu, action) pair is selected in a solution.

Usage

get_actions(x, solution = NULL, ...)

Arguments

x	A solutionset-class object returned by solve.
solution	Optional positive integer giving the solution id to extract. If NULL, all runs are returned when available.
...	Deprecated arguments kept for backwards compatibility. Currently supports run and solution_id, which are redirected to solution.

Details

This function reads the action summary stored in x$summary$actions. It does not reconstruct the table from the raw decision vector; it simply returns the stored summary after optional run filtering.

Let x_{ia} denote the decision variable associated with selecting action a in planning unit i. In standard multiscape workflows, the selected column is the user-facing representation of that decision, typically coded as 0 or 1.

If solution is provided, only rows belonging to that solution are returned. This requires the summary table to contain a solution_id column.

To return only selected action allocations, filter the returned table using selected == 1.

Value

A data.frame containing the stored action-allocation summary. Typical columns include planning-unit ids, action ids, optional labels, and a selected indicator.

See Also

get_planning_units, get_features, get_targets

Examples

# Load a complete simulated planning problem.
example_data <- load_sim_multiaction()

problem <- create_problem(
  pu = example_data$planning_units,
  features = example_data$features,
  dist_features = example_data$dist_features,
  cost = "cost"
) |>
  add_actions(
    example_data$actions,
    cost = example_data$action_costs
  ) |>
  add_effects(
    example_data$effect_assumptions
  ) |>
  add_constraint_targets_relative(0.05) |>
  add_objective_min_cost(alias = "cost", include_pu_cost = FALSE) |>
  add_objective_max_effect(alias = "benefit") |>
  set_method_weighted_sum(
    aliases = c("cost", "benefit"),
    runs = set_runs_grid(n = 3),
    normalize_weights = TRUE
  )

if (requireNamespace("rcbc", quietly = TRUE)) {
  problem <- set_solver_cbc(
    problem,
    verbose = FALSE
  )

  solutions <- solve(problem)

  # All feasible planning-unit/action assignments
  get_actions(solutions)

  # Only selected action assignments
  selected_actions <- get_actions(solutions)
  selected_actions <- selected_actions[
    selected_actions$selected == 1L,
    ,
    drop = FALSE
  ]
  selected_actions

  # Action allocations for one solution
  solution_ids <- get_runs(solutions)$solution_id

  get_actions(
    solutions,
    solution = solution_ids[1]
  )
}

multiscape documentation built on Oct. 12, 2026, 5:08 p.m.