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
Other overviews:
constraints,
decisions,
importance,
objectives,
penalties,
portfolios,
solvers,
summaries
Examples
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_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
targs <- data.frame(
feature = names(sim_features),
target = 0.1,
type = "relative"
)
p4 <- p %>% add_manual_targets(targs)
# solve problem
s <- c(solve(p1), solve(p2), solve(p3), solve(p4))
names(s) <- c(
"relative targets", "absolute targets", "loglinear targets",
"manual targets"
)
# plot solution
plot(s, axes = FALSE)
# }