Royle-Nichols occupancy family

Occupancy with abundance-induced detection heterogeneity (Royle & Nichols 2003; unmarked occuRN). Latent abundance N_i ~ Poisson(lambda_i) drives the per-visit detection probability 1 - (1 - r_ij)^{N_i}, where r_ij is the per-individual detection probability. The state formula models log lambda (abundance); detection models logit r. Detection is site-level by default; passing visits makes it visit-varying (logit r_ij gains the visit-level covariates), exactly as for the occupancy / N-mixture front doors. The latent N marginalises in closed form (a Poisson sum to K_max), so the fit maximises the exact marginal with an observed-information vcov.

Usage

royle_nichols(K_max = NULL)

Arguments

K_max

Upper summation bound for the latent abundance (default: a data-driven Poisson-tail guess).

Value

A tobs_family object for tobs().

Examples

# \donttest{
sim <- simulate_royle_nichols(N = 150, J = 5, seed = 1)
fit <- tobs(~ x, data = sim$data, family = royle_nichols(),
            detection = ~ 1, y = sim$y, control = list(verbose = FALSE))
coef(fit)

# Visit-varying detection via `visits`:
sv <- simulate_royle_nichols(N = 150, J = 5, beta_r_visit = 0.8, seed = 1)
fv <- tobs(~ x, data = sv$data, family = royle_nichols(),
           detection = ~ w, y = sv$y, visits = sv$visits,
           control = list(verbose = FALSE))
coef(fv)
# }