oppr (version 0.0.2)

add_manual_targets: Add manual targets

Description

Set targets for a project prioritization problem by manually specifying all the required information for each target. This function is useful because it can be used to customize all aspects of a target. For most cases, targets can be specified using the add_absolute_targets and add_relative_targets functions. However, this function can be used to mix absolute and relative targets for different features.

Usage

# S4 method for ProjectProblem,data.frame
add_manual_targets(x, targets)
# S4 method for ProjectProblem,tbl_df
add_manual_targets(x, targets)

Arguments

x

ProjectProblem-class object.

targets

data.frame or tibble object. See the Details section for more information.

Value

ProjectProblem-class object with the targets added to it.

Details

Targets are used to specify the minimum probability of persistence for each feature in solutions. For minimum set objectives (i.e. add_min_set_objective, these targets specify the minimum probability of persistence required for each species in the solution. And for budget constrained objectives that use targets (i.e. add_max_targets_met_objective), these targets specify the minimum threshold probability of persistence that needs to be achieved to count the benefits for conserving these species. Please note that attempting to solve problems with objectives that require targets without specifying targets will throw an error.

The targets argument should contain the following columns:

"feature"

character name of features in argument to x.

"type"

character describing the type of target. Acceptable values include "absolute" and "relative". These values correspond to add_absolute_targets, and add_relative_targets respectively.

"sense"

character sense of the target. The only acceptable value currently supported is: ">=". This field (column) is optional and if it is missing then target senses will default to ">=" values.

"target"

numeric target threshold.

See Also

targets.

Examples

# NOT RUN {
# load data
data(sim_projects, sim_features, sim_actions)


# create data frame with targets
targets <- data.frame(feature = sim_features$name,
                      type = "absolute",
                      target = 0.1)

# print targets
print(targets)

# build problem with minimum set objective and targets that require each
# feature to have a 30% chance of persisting into the future
p <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_manual_targets(targets) %>%
      add_binary_decisions()

# print problem
print(p)

# }
# NOT RUN {
# solve problem
s <- solve(p)

# print solution
print(s)
# }