add_relative_targets: Add relative targets

Description Usage Arguments Details See Also Examples

Description

Set targets for a project prioritization problem() as a proportion (between 0 and 1) of the maximum probability of persistence associated with the best project for feature. For instance, if the best project for a feature has an 80% probability of persisting, setting a 50% (i.e. 0.5) relative target will correspond to a 40% threshold probability of persisting.

Usage

1
2
3
4
5
6
7
add_relative_targets(x, targets)

## S4 method for signature 'ProjectProblem,numeric'
add_relative_targets(x, targets)

## S4 method for signature 'ProjectProblem,character'
add_relative_targets(x, targets)

Arguments

x

ProjectProblem object.

targets

Object that specifies the targets for each feature. See the Details section for more information.

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 for a problem can be specified in several different ways:

numeric

vector of target values for each feature. The order of the target values should correspond to the order of the features in the data used to create the argument to x. Additionally, for convenience, this type of argument can be a single value to assign the same target to each feature.

character

specifying the name of column in the feature data (i.e. the argument to features in the problem() function) that contains the persistence targets.

See Also

targets.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
# load data
data(sim_projects, sim_features, sim_actions)

# build problem with minimum set objective and targets that require each
# feature to have a level of persistence that is greater than or equal to
# 70% of the best project for conserving it
p1 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_relative_targets(0.7) %>%
      add_binary_decisions()

# print problem
print(p1)

# build problem with minimum set objective and specify targets that require
# different levels of persistence for each feature
p2 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_relative_targets(c(0.2, 0.3, 0.4, 0.5, 0.6)) %>%
      add_binary_decisions()

# print problem
print(p2)

# add a column name to the feature data with targets
sim_features$target <- c(0.2, 0.3, 0.4, 0.5, 0.6)

# build problem with minimum set objective and specify targets using
# column name in the feature data
p3 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
      add_min_set_objective() %>%
      add_relative_targets("target") %>%
      add_binary_decisions()

## Not run: 
# print problem
print(p3)

# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)

# print solutions
print(s1)
print(s2)
print(s3)

# plot solutions
plot(p1, s1)
plot(p2, s2)
plot(p3, s3)

## End(Not run)

oppr documentation built on May 12, 2021, 1:07 a.m.