Add losses

Convenience wrapper around add_effects that keeps only negative effects, represented by rows with loss > 0.

This function is useful when the workflow is focused only on damaging consequences of actions and a loss-only wrapper is more convenient than calling add_effects(..., component = "loss") directly. Internally, it calls add_effects() with component = "loss". The canonical stored result therefore contains only rows whose effect satisfies \(\mathrm{loss}_{iaf} > 0\).

In addition, a mirror table containing only the loss component is stored in x$data$dist_loss.

Usage

add_losses(
  x,
  losses = NULL,
  effect_type = c("delta", "after"),
  effect_aggregation = c("sum", "mean")
)

Arguments

x

A Problem object created with create_problem. It must already contain feasible actions; run add_actions first.

losses

Alias of effects, used for symmetry with add_benefits().

effect_type

Character string indicating how supplied effect values are interpreted. Must be one of:

• "delta": values represent signed net changes,

• "after": values represent after-action amounts and are converted to net changes relative to baseline feature amounts.

effect_aggregation

Character string giving the aggregation used when converting raster values to planning-unit level. Must be one of "sum" or "mean".

Value

An updated Problem object containing:

dist_effects

The canonical filtered effects table, containing only rows with loss > 0.

dist_loss

A convenience table containing only the loss component.

losses_meta

Metadata for the stored loss table.

See also

add_effects, add_benefits, add_objective_min_loss

Examples

pu <- data.frame(id = 1:2, cost = c(1, 2))
features <- data.frame(id = 1, name = "sp1")
dist_features <- data.frame(pu = 1:2, feature = 1, amount = c(5, 10))

p <- create_problem(
  pu = pu,
  features = features,
  dist_features = dist_features
)

p <- add_actions(
  p,
  data.frame(id = "harvest")
)

eff <- data.frame(
  pu = c(1, 2),
  action = c("harvest", "harvest"),
  feature = c(1, 1),
  delta = c(2, -4)
)

p <- add_losses(p, losses = eff)
p$data$dist_loss
#>   pu  action feature loss internal_pu internal_action internal_feature
#> 2  2 harvest       1    4           2               1                1
#>   feature_name action_name
#> 2          sp1     harvest