marxan_problem: Marxan conservation problem
In prioritizr/prioritizrutils: Utilities for prioritizr

Description Usage Arguments Value Examples

Create a Marxan conservation problem. Although users are encouraged to build and tailor conservation planning problems to suit their own needs, sometimes it is easier to use a more familiar "canned" approach. This function provides a convenient wrapper for generating and solving Marxan-style conservation problems. If users already have their conservation planning data in the Marxan input format, this function can also be used to read Marxan data files and solve the Marxan-style problems using exact algorithm solvers.

marxan_problem(x, ...)

## Default S3 method:
marxan_problem(x, features, targets,
  targets_type = c("relative", "absolute"), locked_in = waiver(),
  locked_out = waiver(), penalty = 0, edge_factor = 0.5, ...)

## S3 method for class 'data.frame'
marxan_problem(x, spec, puvspr, bound = NULL, blm = 0,
  asymmetric_connectivity = FALSE, ...)

## S3 method for class 'character'
marxan_problem(x, ...)

`x`	`character` file path for Marxan input file (typically called `"input.dat"`), a `data.frame` object containing planning unit data following conventions (ie. 'pu.dat'), or a `Spatial-class` or `Raster-class` object containing planning unit data.
`...`	not used
`features`	`Raster-class` object containing data on the distribution of features across the study area.
`targets`	`numeric` amount of each feature to be represented in the solution.
`targets_type`	`character` name indicating if the `targets` are expressed as `"relative"` (eg. `0.2` meaning that 20 % of a feature needs to be conserved), or `"absolute"` (eg. `200` meaning that 200 units of a feature need to be conserved) amounts.
`locked_in`	Object that determines which planning units that should be locked in. See details for more information.
`locked_out`	Object that determines which planning units that should be locked out. See details for more information.
`penalty`	`numeric` penalty for exposed edges. This is equivalent to the boundary length modifier (BLM) parameter in marxan.net.
`edge_factor`	`numeric` proportion to scale edges that do not have any neighboring planning units. For example, an edge factor of `0.5` is commonly used for planning units along the coast line.
`spec`	`data.frame` containing information on the features. The argument to `spec` must follow the conventions used by Marxan. Each row corresponds to a different feature. It must also contain the following columns: `"id"` `integer` unique identifier for each feature These identifiers are used in the argument to `rij_data`. `"name"` `character` name for each feature. `"prop"` `numeric` relative target for each feature (optional). ' `"amount"` `numeric` absolute target for each feature (optional).
`puvspr`	`data.frame` containing information on the amount of each feature in each planning unit. Similar to `spec`, the argument to `puvspr` must follow the conventions used by Marxan. It must contain the following columns: `"pu"` `integer` planning unit identifier. `"species"` `integer` feature identifier. `"amount"` `numeric` amount of the feature in the planning unit.
`bound`	puvspr `data.frame` containing information on the planning units' boundaries or the connectivity between planning units. Similar to `spec`, the argument to `bound` must follow the conventions used by Marxan. It must contain the following columns: `"id1"` `integer` planning unit identifier. `"id2"` `integer` planning unit identifier. `"boundary"` `numeric` length of shared boundary between planning units, or the strength of the connectivity between the two planning units.
`blm`	`numeric` boundary length modifier. This argument only has an effect when argument to `x` is a `data.frame`. Defaults to zero.
`asymmetric_connectivity`	`logical` does the argument to `bound` denote asymmetric connectivity between planning units? This argument only has an effect when argument to `bound` is a `data.frame`. Defaults to `FALSE`.

ConservationProblem-class object.

# create Marxan problem using spatial data
data(sim_pu_raster, sim_features)
p1 <- marxan_problem(sim_pu_raster, features=sim_features, targets=0.2,
                     targets_type="relative", penalty=1,
                     edge_factor=0.5)


# solve problem
s1 <- solve(p1)

# show solution
plot(s1)


# create marxan problem using marxan input files
input_file <- system.file("extdata/input.dat", package="prioritizrutils")
p2 <- marxan_problem(input_file)


# solve problem
s2 <- solve(p2)

# count number of selected planning units in solution
print(sum(s2))