Simulate binned distance-sampling abundance data — simulate

Latent abundance N_i ~ Poisson(lambda_i) (or NegBin(lambda_i, size)) with log lambda_i = X_lambda beta_lambda, observed through a half-normal or hazard-rate detection function with log sigma_i = X_sigma beta_sigma. Each individual lands in a distance bin (or goes undetected) by the multinomial cell probabilities. Returns an N x n_bins integer matrix of per-bin counts suitable for tobs() with distance().

Usage

simulate_distance(
  N = 200,
  cutpoints = seq(0, 1, length.out = 6),
  key = c("halfnorm", "hazard"),
  transect = c("line", "point"),
  n_abund_covs = 1,
  n_sigma_covs = 1,
  beta_lambda = NULL,
  beta_sigma = NULL,
  shape = 3,
  mixture = c("poisson", "negbin"),
  size = 5,
  seed = NULL
)

Arguments

N: Number of sites (default 200).
cutpoints: Distance-bin edges (length n_bins + 1). Default seq(0, 1, length.out = 6) (five bins out to 1).
key: "halfnorm" (default) or "hazard".
transect: "line" (default) or "point".
n_abund_covs, n_sigma_covs: Number of abundance / detection covariates.
beta_lambda: Abundance coefficients (log scale). Default c(log(40), runif(n_abund_covs, -0.4, 0.4)).
beta_sigma: Detection-scale coefficients (log scale). Default c(log(0.4), runif(n_sigma_covs, -0.3, 0.3)).
shape: Hazard-rate shape b (key = "hazard" only, default 3).
mixture: "poisson" (default) or "negbin".
size: Negative-binomial size r (mixture = "negbin" only, default 5).
seed: Optional random seed.

Value

A list with y (N x n_bins count matrix), data (covariates), cutpoints, and truth (coefficients, per-site lambda / sigma, latent N, key/transect/mixture/shape/size).

Examples

sim <- simulate_distance(N = 50, seed = 1)
head(sim$y)