metafolio (version 0.1.0)

run_cons_plans: Run conservation plans and return the portfolio mean and variance values

Description

This function takes a set of weights representing different conservation plans and gets the mean and variance in portfolio space. This function allows a maximally complicated set of weights to accommodate all possible scenarios. It can accommodate different spatial strategies of conservation, conserving different numbers of populations, and a lack of knowledge. You can do this by how you set your w weight object. See the example.

Usage

run_cons_plans(w, env_type, env_params, show_progress = TRUE, burn = 1:30,
  assess_freq = 5, risk_fn = var, ...)

Arguments

w

A (nested) list of weights. The first list level contains the different plans. The next level contains repetitions for a given plan. E.g. cp[[2]][[1]] contains the first iteration of the second conservation plan. Each end element should be a matrix of weights with one row and the number of columns equal to the number of subpopulations.

env_type

The environmental type to pass to generate_env_ts

env_params

The environmental parameters to pass to generate_env_ts

show_progress

Logical: show an indication of progress?

burn

Cycles to throw out as burn in

assess_freq

How frequently (in years) to re-assess the Ricker a and b values.

risk_fn

A risk function to use. Can be any function that takes a numeric vector and returns a single value. Suggested values include var, or VaR, or CVaR. Defaults to variance.

...

Other values to pass to meta_sim

Value

A list with two high-level elements: the mean variance output (plans_mv) and the raw simulation output (plans_port). Within plans_mv, each element of the list contains a conservation plan. Each row of the data frames represents a trial run. Within plans_port, each first level of the list contains a weight element and each second level of the list contains a replicate.

Examples

# NOT RUN {
set.seed(1)
w_plans <- list()
w_plans[[1]] <- c(5, 1000, 5, 1000, 5, 5, 1000, 5, 1000, 5)
w_plans[[2]] <- c(5, 5, 5, 1000, 1000, 1000, 1000, 5, 5, 5)
w_plans[[3]] <- c(rep(1000, 4), rep(5, 6))
w_plans[[4]] <- rev(w_plans[[3]])
plans_name_sp <- c("Full range of responses", "Most stable only",
"Lower half", "Upper half")
 n_trials <- 50 # number of trials at each n conservation plan
 n_plans <- 4 # number of plans
 num_pops <- c(2, 4, 8, 16) # n pops to conserve
 w <- list()
 for(i in 1:n_plans) { # loop over number conserved
  w[[i]] <- list()
  for(j in 1:n_trials) { # loop over trials
    w[[i]][[j]] <- matrix(rep(625, 16), nrow = 1)
    w[[i]][[j]][-sample(1:16, num_pops[i])] <- 5
  }
 }
arma_env_params <- list(mean_value = 16, ar = 0.1, sigma_env = 2, ma = 0)

x_arma_sp <- run_cons_plans(w, env_type = "arma", env_params = arma_env_params)

plot_cons_plans(x_arma_sp$plans_mv, plans_name = plans_name_sp, cols =
  cols, add_all_efs = FALSE, xlim = c(0.02, 0.15), ylim = c(-0.017,
    0.017), add_legend = FALSE)

# In this version, the pops are wiped out; total abundance changes
n_trials <- 50 # number of trials at each n conservation plan
num_pops <- c(2, 4, 8, 16) # n pops to conserve
n_plans <- length(num_pops) # number of plans
w <- list()
for(i in 1:n_plans) { # loop over number conserved
 w[[i]] <- list()
 for(j in 1:n_trials) { # loop over trials
   w[[i]][[j]] <- matrix(rep(1000, 16), nrow = 1)
   w[[i]][[j]][-sample(1:16, num_pops[i])] <- 5
 }
}
plans_name_n <- paste(num_pops, "populations")
arma_env_params <- list(mean_value = 16, ar = 0.1, sigma_env = 2, ma = 0)

x_arma_n <- run_cons_plans(w, env_type = "arma", env_params =
  arma_env_params, max_a = thermal_integration(16))

plot_cons_plans(x_arma_n$plans_mv, plans_name = plans_name_n, cols =
  cols, add_all_efs = FALSE, xlim = c(0.02, 0.15), ylim = c(-0.017,
    0.017), add_legend = FALSE)
# }