count_quasi_exts {metafolio} | R Documentation |
meta_sim
output objects and count quasi
extinctionsTake meta_sim
output objects and count quasi
extinctions
count_quasi_exts(dat, quasi_thresh, ignore_pops_thresh = 5, duration = 1)
dat |
Input data. Should be a list of lists. The first level corresponds to the conservation plan and the second level corresponds to the replicate. |
quasi_thresh |
The quasi extinction threshold |
ignore_pops_thresh |
Threshold below which to ignore populations (e.g. if you started some populations with very low abundance and you don't want to count those populations. |
duration |
Number of years that the abundance must
be below the |
A list of matrices. The list elements correspond to the
conservation plans. The columns of the matrix correspond to
the subpopulations that were above the
ignore_pops_thresh
level. The rows of the matrix
correspond to the replicates.
## 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) count_quasi_exts(x_arma_sp$plans_port, quasi_thresh = 200) ## End(Not run)