plot_tradeoff: Plot trade-offs from a solution set

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

Plot trade-offs from a solution set

Description

Plot pairwise trade-offs among objective values stored in a solutionset-class object.

This function is intended for workflows in which the solution set contains one row per run and two or more objective-value columns of the form value_*.

If exactly two objectives are selected, the function returns a single scatterplot. If three or more objectives are selected, all pairwise combinations are plotted using facets.

Usage

plot_tradeoff(
  x,
  objectives = NULL,
  color_by = NULL,
  all_pairs = NULL,
  connect = FALSE,
  label_runs = FALSE,
  point_size = 3,
  line_alpha = 0.5,
  text_size = 3,
  ...
)

Arguments

x	A solutionset-class object.
objectives	Optional character vector of objective aliases to display. These must match the suffixes of the value_* columns in x$solution$runs. If NULL, all available objective columns are used.
color_by	Optional character scalar used to colour points. This may be either one of the selected objective aliases or one of the run-level columns "run_id", "status", "runtime", or "gap".
all_pairs	Logical. If TRUE, allow plotting all pairwise combinations even when more than four objectives are selected. If NULL, it is treated as FALSE.
connect	Logical. If TRUE, connect points by run order within each panel.
label_runs	Logical. If TRUE, add run labels to points.
point_size	Numeric point size.
line_alpha	Numeric alpha value for connecting lines.
text_size	Numeric size for run labels.
...	Reserved for future extensions.

Details

This function reads the run-level table stored in x$solution$runs. It expects objective values to be stored in columns whose names begin with "value_".

If the available objective columns are, for example, value_cost, value_benefit, and value_frag, then the corresponding objective aliases are "cost", "benefit", and "frag".

Let f_k(r) denote the value of objective k in run r. This function visualizes pairwise projections of the run table of the form:

\left(f_k(r), f_\ell(r)\right)

for selected pairs of objectives k,\ell.

If exactly two objectives are selected, a single panel is produced.

If three or more objectives are selected, all pairwise combinations are generated:

\{(k,\ell): k < \ell,\; k,\ell \in \mathcal{O}\},

where \mathcal{O} is the selected set of objective aliases.

By default, plotting more than four objectives is not allowed unless all_pairs = TRUE, because the number of panels grows quadratically in the number of objectives.

Colouring

If color_by is supplied, points are coloured by either:

• one of the selected objective aliases, in which case the corresponding value_* column is used;

• or one of the run-level columns run_id, status, runtime, or gap.

Connecting runs

If connect = TRUE, runs are connected in their current table order within each panel. This can be useful when runs correspond to an ordered scan of weights, \epsilon-levels, or frontier points, but it should be used with care when run order has no substantive meaning.

Run labels

If label_runs = TRUE, each point is labelled by its run_id. If the ggrepel package is available, repelled labels are used.

Value

Invisibly returns a ggplot object.

See Also

solve, solutionset-class

Examples

if (
  requireNamespace("ggplot2", quietly = TRUE) &&
  requireNamespace("rcbc", quietly = TRUE)
) {
  # 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
    ) |>
    set_solver_cbc(verbose = FALSE)

  solutions <- solve(problem)

  plot_tradeoff(
    solutions,
    objectives = c("cost", "benefit")
  )
}

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