add_feature_weights | R Documentation |

Set weights for conserving features in a project prioritization
`problem()`

.

add_feature_weights(x, weights) ## S4 method for signature 'ProjectProblem,numeric' add_feature_weights(x, weights) ## S4 method for signature 'ProjectProblem,character' add_feature_weights(x, weights)

`x` |
ProjectProblem object. |

`weights` |
Object that specifies the weights for each feature. See the Details section for more information. |

Weights are used to specify the relative importance for
maintaining the persistence of specific features. For budget constrained
problems (e.g. `add_max_richness_objective()`

), these
weights
could be used to specify which features are more important than other
features according to evolutionary or cultural metrics. Specifically,
features with a higher weight value are considered more important. It is
generally best to ensure that the feature weights range between 0.0001 and
10,000 to reduce the time required to solve problems using exact
algorithm solvers. As a consequence, you might have to rescale the feature
weights if the largest or smallest values occur outside this range
(excluding zeros). If you want to ensure that certain features conserved
in the solutions, it is strongly recommended to lock in the actions for
these features instead of setting really high weights for these features.
Please note that a warning will be thrown if you attempt to solve
problems with weights when an objective has been specified that does
not use weights. Currently, all objectives—except for the minimum
set objective (i.e. `add_min_set_objective()`

)—can use weights.

The weights for a problem can be specified in several different ways:

`numeric`

`vector`

of weight values for 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 weights.

weights.

# load data data(sim_projects, sim_features, sim_actions) # print feature data print(sim_features) # build problem with maximum richness objective, $300 budget, and no weights p1 <- problem(sim_projects, sim_actions, sim_features, "name", "success", "name", "cost", "name") %>% add_max_richness_objective(budget = 200) %>% add_binary_decisions() # print problem print(p1) # build another problem, and specify feature weights using the values in the # "weight" column of the sim_features table by specifying the column # name "weight" p2 <- p1 %>% add_feature_weights("weight") # print problem print(p2) # build another problem, and specify feature weights using the # values in the "weight column of the sim_features table, but # actually input the values rather than specifying the column name # "weights" column of the sim_features table p3 <- p1 %>% add_feature_weights(sim_features$weight) # print problem print(p3) ## Not run: # solve the 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)

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