stan_occuRN: Fit the Occupancy Model of Royle and Nichols (2003)

Description

Fit the occupancy model of Royle and Nichols (2003), which relates probability of detection of the species to the number of individuals available for detection at each site.

Usage

stan_occuRN(
  formula,
  data,
  K = 20,
  prior_intercept_state = normal(0, 5),
  prior_coef_state = normal(0, 2.5),
  prior_intercept_det = logistic(0, 1),
  prior_coef_det = logistic(0, 1),
  prior_sigma = gamma(1, 1),
  log_lik = TRUE,
  ...
)

Value

ubmsFitOccuRN object describing the model fit.

Arguments

formula: Double right-hand side formula describing covariates of detection and abundance in that order
data: A unmarkedFrameOccu object
K: Integer upper index of integration for N-mixture. This should be set high enough so that it does not affect the parameter estimates. Note that computation time will increase with K.
prior_intercept_state: Prior distribution for the intercept of the state (abundance) model; see ?priors for options
prior_coef_state: Prior distribution for the regression coefficients of the state model
prior_intercept_det: Prior distribution for the intercept of the detection probability model
prior_coef_det: Prior distribution for the regression coefficients of the detection model
prior_sigma: Prior distribution on random effect standard deviations
log_lik: If TRUE, Stan will save pointwise log-likelihood values in the output. This can greatly increase the size of the model. If FALSE, the values are calculated post-hoc from the posteriors
...: Arguments passed to the stan call, such as number of chains chains or iterations iter

References

Royle JA, Nichols JD. 2003. Estimating abundance from repeated presence-absence data or point counts. Ecology 84: 777-790.

Examples

Run this code

# \donttest{
data(birds)
woodthrushUMF <- unmarkedFrameOccu(woodthrush.bin)
#Add a site covariate
siteCovs(woodthrushUMF) <- data.frame(cov1=rnorm(numSites(woodthrushUMF)))

(fm_wood <- stan_occuRN(~1~cov1, woodthrushUMF, chains=3, iter=300))
# }