Simulate community relative-abundance (count / continuous) data

Per-species GLMM with Gaussian community hyperpriors on the coefficients (the ms_count() / spAbundance msAbund model): g(mu_{s,i}) = X_i beta_s, beta_s ~ N(beta_comm_mean, diag(beta_comm_sd^2)), y_{s,i} drawn Poisson / negative-binomial (log link) or Gaussian (identity). No detection.

Usage

simulate_ms_count(
  N = 120,
  n_species = 10,
  beta_comm_mean = c(1, 0.5),
  beta_comm_sd = c(0.4, 0.3),
  response = c("poisson", "negbin", "gaussian", "binomial"),
  size = 2,
  size.log.sd = 0.3,
  sd = 1,
  trials = 10,
  seed = NULL
)

Arguments

N: Number of sites.
n_species: Number of species.
beta_comm_mean, beta_comm_sd: Community mean and SD of the per-species coefficients (intercept first). Length sets the number of covariates.
response: One of "poisson", "negbin", "gaussian", "binomial".
size: Negative-binomial community size (mean of the per-species log_r); size.log.sd is its across-species SD.
size.log.sd: Across-species SD of log(size) (negbin only).
sd: Gaussian residual SD.
trials: Binomial trial count (response = "binomial"): a scalar (shared across sites and species) or a length-N per-site vector. Default 10.
seed: Optional RNG seed.

Value

A list with y (an N x n_species matrix), data, and truth.

Examples

sim <- simulate_ms_count(N = 50, n_species = 4, seed = 1)
head(sim$y)