Targets are used to specify the minimum amount or proportion of a feature's distribution that should (ideally) be covered (represented) by a solution.

Details

Please note that most objectives require targets, and attempting to solve a problem that requires targets will throw an error.

The following functions can be used to specify targets for a conservation planning problem():

add_relative_targets()

Set targets as a proportion (between 0 and 1) of the total amount of each feature in the the study area.

add_absolute_targets()

Set targets that denote the minimum amount of each feature required in the prioritization.

add_loglinear_targets()

Set targets as a proportion (between 0 and 1) that are calculated using log-linear interpolation.

add_manual_targets()

Set targets manually.

See also

Examples

# load data data(sim_pu_raster, sim_features) # create base problem p <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # create problem with added relative targets p1 <- p %>% add_relative_targets(0.1) # create problem with added absolute targets p2 <- p %>% add_absolute_targets(3) # create problem with added loglinear targets p3 <- p %>% add_loglinear_targets(10, 0.9, 100, 0.2) # create problem with manual targets that equate to 10% relative targets p4 <- p %>% add_manual_targets(data.frame(feature = names(sim_features), target = 0.1, type = "relative")) # \dontrun{ # solve problem s <- stack(solve(p1), solve(p2), solve(p3), solve(p4)) # plot solution plot(s, axes = FALSE, box = FALSE, main = c("relative targets", "absolute targets", "loglinear targets", "manual targets"))
# }