Simulate conservation project data to develop simulated prioritizations.
ppp_simulate_data(number_species, cost_mean = 100, cost_sd = 5, success_min_probability = 0.7, success_max_probability = 0.99, funded_min_persistence_probability = 0.5, funded_max_persistence_probability = 0.9, not_funded_min_persistence_probability = 0.01, not_funded_max_persistence_probability = 0.4, locked_in_proportion = 0, locked_out_proportion = 0)
| number_species |
|
|---|---|
| cost_mean |
|
| cost_sd |
|
| success_min_probability |
|
| success_max_probability |
|
| funded_min_persistence_probability |
|
| funded_max_persistence_probability |
|
| not_funded_min_persistence_probability |
|
| not_funded_max_persistence_probability |
|
| locked_in_proportion |
|
| locked_out_proportion |
|
A list object containing the elements:
"project_data"A tibble containing
the data for the conservation projects. It contains the following
columns:
"name"character name for each project.
"success"numeric probability of each project
succeeding if it is funded.
"S1" ... "SN"numeric columns for each
species, ranging from "S1" to "SN" where N
is the number of species, indicating the enhanced probability that
each species will persist if it funded.
"S1_action" ... "SN_action"logical
columns for each action, ranging from "S1_action" to
"SN_action" where N is
the number of actions (equal to the number of species in this
simulated data), indicating if an action is associated with a
project (TRUE) or not (FALSE).
"action_data"A tibble containing
the data for the conservation actions. It contains the following
columns:
"name"character name for each action.
"cost"numeric cost for each action.
"locked_in"logical indicating if certain
actions should be locked into the solution.
"locked_out"logical indicating if certain
actions should be locked out of the solution.
"species_data"A tibble containing
the data for the species. It contains the following columns:
"name"character name for each species.
"weight"numeric weight for each species.
For each species, this is calculated as the amount of time that
elapsed between the present and the species' last common ancestor.
In other words, the weights are calculated as the unique amount
of evolutionary history that each species has experienced.
phylo phylogenetic tree for the species.
The simulated data set will contain one conservation project for each species and a "baseline" (do nothing) project to reflect species' persistence when none of their conservation projects are not funded. Each conservation project is associated with a single action, and no conservation projects share any actions. Specifically, the data are simulated as follows:
A conservation project is created for each species, and each project is associated with its own single action.
Cost data for each action are simulated using a normal
distribution and the cost_mean and cost_sd arguments.
A set proportion of the actions are randomly set to be locked
in and out of the solutions using the locked_in_proportion and
locked_out_proportion arguments.
The probability of each project succeeding if its action is funded
is simulated by drawing probabilities from a uniform distribution with
the upper and lower bounds set as the success_min_probability
and success_max_probability arguments.
The probability of each species persisting if its project is funded
and is successful is simulated by drawing probabilities from a uniform
distribution with the upper and lower bounds set as the
funded_min_persistence_probability and
funded_max_persistence_probability arguments.
An additional project is created which represents the "baseline"
(do nothing) scenario. The probability of each species persisting
when managed under this project is simulated by drawing probabilities
from a uniform distribution with the upper and lower bounds
set as the not_funded_min_persistence_probability
and not_funded_max_persistence_probability arguments.
A phylogenetic tree is simulated for the species using
rcoal.
Species data are created from the phylogenetic tree. The weights are calculated as the amount of evolutionary history that has elapsed between each species and its last common ancestor.
# create a simulated data set s <- ppp_simulate_data(number_species = 5, cost_mean = 100, cost_sd = 5, success_min_probability = 0.7, success_max_probability = 0.99, funded_min_persistence_probability = 0.5, funded_max_persistence_probability = 0.9, not_funded_min_persistence_probability = 0.01, not_funded_max_persistence_probability = 0.4, locked_in_proportion = 0.01, locked_out_proportion = 0.01) # print data set print(s)#> $project_data #> # A tibble: 6 x 13 #> name success S1 S2 S3 S4 S5 S1_action S2_action S3_action #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> #> 1 S1_p~ 0.756 0.789 0 0 0 0 TRUE FALSE FALSE #> 2 S2_p~ 0.889 0 0.511 0 0 0 FALSE TRUE FALSE #> 3 S3_p~ 0.723 0 0 0.808 0 0 FALSE FALSE TRUE #> 4 S4_p~ 0.936 0 0 0 0.838 0 FALSE FALSE FALSE #> 5 S5_p~ 0.980 0 0 0 0 0.655 FALSE FALSE FALSE #> 6 base~ 1 0.373 0.0232 0.162 0.101 0.0776 FALSE FALSE FALSE #> # ... with 3 more variables: S4_action <lgl>, S5_action <lgl>, #> # baseline_action <lgl> #> #> $action_data #> # A tibble: 6 x 4 #> name cost locked_in locked_out #> <chr> <dbl> <lgl> <lgl> #> 1 S1_action 101. TRUE FALSE #> 2 S2_action 104. FALSE FALSE #> 3 S3_action 106. FALSE TRUE #> 4 S4_action 94.4 FALSE FALSE #> 5 S5_action 91.2 FALSE FALSE #> 6 baseline_action 0 FALSE FALSE #> #> $species_data #> # A tibble: 5 x 2 #> name weight #> <chr> <dbl> #> 1 S2 1.24 #> 2 S1 0.0686 #> 3 S3 0.0686 #> 4 S4 0.177 #> 5 S5 0.177 #> #> $tree #> #> Phylogenetic tree with 5 tips and 4 internal nodes. #> #> Tip labels: #> [1] "S2" "S1" "S3" "S4" "S5" #> #> Rooted; includes branch lengths. #>