Return one solution per instance for different targets values. This
function assumes that the *minimizeCosts* model is being used. As well as the
`prioriactions()`

function, it inherits all arguments from `inputData()`

,
`problem()`

and `solve()`

.

`evalTarget(values = c(), ...)`

- values
`numeric`

. Proportion of maximum value of benefits to verify (both recovery and conservation benefits). This information can be obtained with`getPotentialBenefit()`

function. More than one value is needed.- ...
arguments inherited from

`inputData()`

,`problem()`

, and`solve()`

functions.

An object of class portfolio.

`evalTarget()`

creates and solves multiple instances, of the corresponding
multi-actions planning problem, for different proportions of maximum benefit values
as target values. It is assumed that the same proportion is applied for the maximum
benefit in recovery and conservation. Alternatively, this
could be obtained by executing function `prioriactions()`

or by steps the `inputData()`

,
`problem()`

and `solve()`

functions; using, in each run, different targets values.
However, the `evalTarget()`

function has two advantages with
respect to this manual approach: : 1)
it is more efficient to create the models (this is because the model is created
just once and, at each iteration, only the target values are updated); and 2) the
output is a portfolio object, which allows
obtaining information about the group of solutions (including all *get* functions).

```
# \donttest{
# set seed for reproducibility
set.seed(14)
## Create model and solve
port <- evalTarget(pu = sim_pu_data, features = sim_features_data,
dist_features = sim_dist_features_data,
threats = sim_threats_data,
dist_threats = sim_dist_threats_data,
sensitivity = sim_sensitivity_data,
boundary = sim_boundary_data,
values = c(0.1, 0.3, 0.5),
time_limit = 50,
output_file = FALSE,
cores = 2)
#> *********************************
#> Iteration 1 of 3: Prop0.1
#> *********************************
#> Warning: The blm argument was set to 0, so the boundary data has no effect
#> Warning: Some blm_actions argument were set to 0, so the boundary data has no effect for these cases
#> Rcplex: num variables=396 num constraints=284
#> *********************************
#> Iteration 2 of 3: Prop0.3
#> *********************************
#> Warning: The blm argument was set to 0, so the boundary data has no effect
#> Warning: Some blm_actions argument were set to 0, so the boundary data has no effect for these cases
#> Rcplex: num variables=396 num constraints=284
#> *********************************
#> Iteration 3 of 3: Prop0.5
#> *********************************
#> Warning: The blm argument was set to 0, so the boundary data has no effect
#> Warning: Some blm_actions argument were set to 0, so the boundary data has no effect for these cases
#> Rcplex: num variables=396 num constraints=284
getCost(port)
#> solution_name monitoring threat_1 threat_2
#> 1 Prop0.1 28 2 25
#> 2 Prop0.3 99 27 70
#> 3 Prop0.5 180 72 109
# }
```