This function uses Gurobi to find prioritizations using the input parameter
and data stored in a RapUnsolved() object, and returns a
RapSolved() object with outputs in it.
# S4 method for RapUnsolOrSol,missing
solve(a, b, ..., verbose = FALSE)
# S4 method for RapUnsolOrSol,GurobiOpts
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,matrix
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,numeric
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,logical
solve(a, b, verbose = FALSE)RapUnsolved() or RapSolved() object.
missing to generate solutions using Gurobi. Prioritizations
can be specified using logical, numeric, or
base::matrix() objects. This may be useful for evaluating the
performance of solutions obtained using other software.
not used.
logical should messages be printed during creation of
the initial model matrix?.
RapSolved() object
This function is used to solve a RapUnsolved() object that
has all of its inputs generated. The rap function (without lower case 'r')
provides a more general interface for generating inputs and outputs.
# \dontrun{
# load RapUnsolved object
data(sim_ru)
# solve it using Gurobi
sim_rs <- solve(sim_ru)
# evaluate manually specified solution using planning unit indices
sim_rs2 <- solve(sim_ru, seq_len(10))
#> Warning: some species have space.held values less than 0, and thus are poorly represented
# evaluate manually specifed solution using binary selections
sim_rs3 <- solve(sim_ru, c(rep(TRUE, 10), rep(FALSE, 90)))
#> Warning: some species have space.held values less than 0, and thus are poorly represented
# evaluate multiple manually specified solutions
sim_rs4 <- solve(sim_ru, matrix(sample(c(0, 1), size = 500, replace = TRUE),
ncol = 100, nrow = 5))
# }