Last updated on 2021-10-06 06:49:42 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 7.0.1 | 460.55 | 281.52 | 742.07 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 7.0.1 | 375.25 | 208.64 | 583.89 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 7.0.1 | 956.07 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 7.0.1 | 1060.06 | ERROR | |||
| r-devel-windows-x86_64 | 7.0.1 | 661.00 | 262.00 | 923.00 | NOTE | |
| r-devel-windows-x86_64-gcc10-UCRT | 7.0.1 | NOTE | ||||
| r-patched-linux-x86_64 | 7.0.1 | 449.99 | 268.17 | 718.16 | NOTE | |
| r-patched-solaris-x86 | 7.0.1 | 906.20 | ERROR | |||
| r-release-linux-x86_64 | 7.0.1 | 441.27 | 268.02 | 709.29 | NOTE | |
| r-release-macos-arm64 | 7.0.1 | NOTE | ||||
| r-release-macos-x86_64 | 7.0.1 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 7.0.1 | 1234.00 | 357.00 | 1591.00 | NOTE | |
| r-oldrel-macos-x86_64 | 7.0.1 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 7.0.1 | 939.00 | 491.00 | 1430.00 | NOTE |
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking: 'gurobi', 'rcbc'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'RandomFields', 'gurobi', 'rcbc', 'cplexAPI'
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 7.0.1
Check: installed package size
Result: NOTE
installed size is 17.2Mb
sub-directories of 1Mb or more:
doc 1.9Mb
libs 13.1Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-x86_64, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 7.0.1
Check: dependencies in R code
Result: NOTE
Namespace in Imports field not imported from: ‘exactextractr’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-x86_64
Version: 7.0.1
Check: examples
Result: ERROR
Running examples in ‘prioritizr-Ex.R’ failed
The error most likely occurred in:
> ### Name: add_linear_penalties
> ### Title: Add linear penalties
> ### Aliases: add_linear_penalties
> ### add_linear_penalties,ConservationProblem,ANY,Matrix-method
> ### add_linear_penalties,ConservationProblem,ANY,matrix-method
> ### add_linear_penalties,ConservationProblem,ANY,dgCMatrix-method
> ### add_linear_penalties,ConservationProblem,ANY,character-method
> ### add_linear_penalties,ConservationProblem,ANY,numeric-method
> ### add_linear_penalties,ConservationProblem,ANY,Raster-method
>
> ### ** Examples
>
> # set seed for reproducibility
> set.seed(600)
>
> # load data
> data(sim_pu_polygons, sim_pu_zones_stack, sim_features, sim_features_zones)
>
> # add a column to contain the penalty data for each planning unit
> # e.g. these values could indicate the level of habitat
> sim_pu_polygons$penalty_data <- runif(nrow(sim_pu_polygons))
>
> # plot the penalty data to visualise its spatial distribution
> spplot(sim_pu_polygons, zcol = "penalty_data", main = "penalty data",
+ axes = FALSE, box = FALSE)
>
> # create minimal problem with minimum set objective,
> # this does not use the penalty data
> p1 <- problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
+ add_min_set_objective() %>%
+ add_relative_targets(0.1) %>%
+ add_binary_decisions() %>%
+ add_default_solver(verbose = FALSE)
>
> # print problem
> print(p1)
Conservation Problem
planning units: SpatialPolygonsDataFrame (90 units)
cost: min: 190.13276, max: 215.86384
features: layer.1, layer.2, layer.3, ... (5 features)
objective: Minimum set objective
targets: Relative targets [targets (min: 0.1, max: 0.1)]
decisions: Binary decision
constraints: <none>
penalties: <none>
portfolio: default
solver: Rsymphony [first_feasible (0), gap (0.1), time_limit (2147483647), verbose (0)]
>
> # create an updated version of the previous problem,
> # with the penalties added to it
> p2 <- p1 %>% add_linear_penalties(100, data = "penalty_data")
>
> # print problem
> print(p2)
Conservation Problem
planning units: SpatialPolygonsDataFrame (90 units)
cost: min: 190.13276, max: 215.86384
features: layer.1, layer.2, layer.3, ... (5 features)
objective: Minimum set objective
targets: Relative targets [targets (min: 0.1, max: 0.1)]
decisions: Binary decision
constraints: <none>
penalties: <Linear penalties [penalty (100)]>
portfolio: default
solver: Rsymphony [first_feasible (0), gap (0.1), time_limit (2147483647), verbose (0)]
>
> ## Not run:
> ##D # solve the two problems
> ##D s1 <- solve(p1)
> ##D s2 <- solve(p2)
> ##D
> ##D # plot the solutions and compare them,
> ##D # since we supplied a very high penalty value (i.e. 100), relative
> ##D # to the range of values in the penalty data and the objective function,
> ##D # the solution in s2 is very sensitive to values in the penalty data
> ##D spplot(s1, zcol = "solution_1", main = "solution without penalties",
> ##D axes = FALSE, box = FALSE)
> ##D spplot(s2, zcol = "solution_1", main = "solution with penalties",
> ##D axes = FALSE, box = FALSE)
> ##D
> ##D # for real conservation planning exercises,
> ##D # it would be worth exploring a range of penalty values (e.g. ranging
> ##D # from 1 to 100 increments of 5) to explore the trade-offs
> ## End(Not run)
>
> # now, let's examine a conservation planning exercise involving multiple
> # management zones
>
> # create targets for each feature within each zone,
> # these targets indicate that each zone needs to represent 10% of the
> # spatial distribution of each feature
> targ <- matrix(0.1, ncol = number_of_zones(sim_features_zones),
+ nrow = number_of_features(sim_features_zones))
>
> # create penalty data for allocating each planning unit to each zone,
> # these data will be generated by simulating values
> penalty_stack <- simulate_cost(sim_pu_zones_stack[[1]],
+ n = number_of_zones(sim_features_zones))
Error in simulate_data(x, n = n, model = model, transform = transform, :
the "RandomFields" package needs to be installed to simulate data
Calls: simulate_cost -> simulate_data
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'gurobi', 'rcbc', 'cplexAPI'
Flavors: r-devel-windows-x86_64, r-devel-windows-x86_64-gcc10-UCRT, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'RandomFields', 'gurobi', 'rcbc', 'cplexAPI', 'lpsymphony',
'Rsymphony'
Flavor: r-patched-solaris-x86
Version: 7.0.1
Check: examples
Result: ERROR
Running examples in ‘prioritizr-Ex.R’ failed
The error most likely occurred in:
> ### Name: add_linear_penalties
> ### Title: Add linear penalties
> ### Aliases: add_linear_penalties
> ### add_linear_penalties,ConservationProblem,ANY,Matrix-method
> ### add_linear_penalties,ConservationProblem,ANY,matrix-method
> ### add_linear_penalties,ConservationProblem,ANY,dgCMatrix-method
> ### add_linear_penalties,ConservationProblem,ANY,character-method
> ### add_linear_penalties,ConservationProblem,ANY,numeric-method
> ### add_linear_penalties,ConservationProblem,ANY,Raster-method
>
> ### ** Examples
>
> # set seed for reproducibility
> set.seed(600)
>
> # load data
> data(sim_pu_polygons, sim_pu_zones_stack, sim_features, sim_features_zones)
>
> # add a column to contain the penalty data for each planning unit
> # e.g. these values could indicate the level of habitat
> sim_pu_polygons$penalty_data <- runif(nrow(sim_pu_polygons))
>
> # plot the penalty data to visualise its spatial distribution
> spplot(sim_pu_polygons, zcol = "penalty_data", main = "penalty data",
+ axes = FALSE, box = FALSE)
>
> # create minimal problem with minimum set objective,
> # this does not use the penalty data
> p1 <- problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
+ add_min_set_objective() %>%
+ add_relative_targets(0.1) %>%
+ add_binary_decisions() %>%
+ add_default_solver(verbose = FALSE)
>
> # print problem
> print(p1)
Conservation Problem
planning units: SpatialPolygonsDataFrame (90 units)
cost: min: 190.13276, max: 215.86384
features: layer.1, layer.2, layer.3, ... (5 features)
objective: Minimum set objective
targets: Relative targets [targets (min: 0.1, max: 0.1)]
decisions: Binary decision
constraints: <none>
penalties: <none>
portfolio: default
solver: MissingSolver []
>
> # create an updated version of the previous problem,
> # with the penalties added to it
> p2 <- p1 %>% add_linear_penalties(100, data = "penalty_data")
>
> # print problem
> print(p2)
Conservation Problem
planning units: SpatialPolygonsDataFrame (90 units)
cost: min: 190.13276, max: 215.86384
features: layer.1, layer.2, layer.3, ... (5 features)
objective: Minimum set objective
targets: Relative targets [targets (min: 0.1, max: 0.1)]
decisions: Binary decision
constraints: <none>
penalties: <Linear penalties [penalty (100)]>
portfolio: default
solver: MissingSolver []
>
> ## Not run:
> ##D # solve the two problems
> ##D s1 <- solve(p1)
> ##D s2 <- solve(p2)
> ##D
> ##D # plot the solutions and compare them,
> ##D # since we supplied a very high penalty value (i.e. 100), relative
> ##D # to the range of values in the penalty data and the objective function,
> ##D # the solution in s2 is very sensitive to values in the penalty data
> ##D spplot(s1, zcol = "solution_1", main = "solution without penalties",
> ##D axes = FALSE, box = FALSE)
> ##D spplot(s2, zcol = "solution_1", main = "solution with penalties",
> ##D axes = FALSE, box = FALSE)
> ##D
> ##D # for real conservation planning exercises,
> ##D # it would be worth exploring a range of penalty values (e.g. ranging
> ##D # from 1 to 100 increments of 5) to explore the trade-offs
> ## End(Not run)
>
> # now, let's examine a conservation planning exercise involving multiple
> # management zones
>
> # create targets for each feature within each zone,
> # these targets indicate that each zone needs to represent 10% of the
> # spatial distribution of each feature
> targ <- matrix(0.1, ncol = number_of_zones(sim_features_zones),
+ nrow = number_of_features(sim_features_zones))
>
> # create penalty data for allocating each planning unit to each zone,
> # these data will be generated by simulating values
> penalty_stack <- simulate_cost(sim_pu_zones_stack[[1]],
+ n = number_of_zones(sim_features_zones))
Error in simulate_data(x, n = n, model = model, transform = transform, :
the "RandomFields" package needs to be installed to simulate data
Calls: simulate_cost -> simulate_data
Execution halted
Flavor: r-patched-solaris-x86
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'gurobi', 'rcbc', 'cplexAPI', 'lpsymphony', 'Rsymphony'
Flavors: r-release-macos-arm64, r-oldrel-macos-x86_64
Version: 7.0.1
Check: package dependencies
Result: NOTE
Packages suggested but not available for checking:
'gurobi', 'rcbc', 'cplexAPI', 'lpsymphony'
Flavor: r-release-macos-x86_64