stan_pcount: Fit the N-mixture model of Royle (2004)

Description

This function fits the single season N-mixture model of Royle et al. (2004).

Usage

stan_pcount(
  formula,
  data,
  K = NULL,
  mixture = "P",
  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

ubmsFitPcount object describing the model fit.

Arguments

formula: Double right-hand side formula describing covariates of detection and abundance in that order
data: A unmarkedFramePCount 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.
mixture: Character specifying mixture: "P" is only option currently.
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. 2004. N-mixture models for estimating populaiton size from spatially replicated counts. Biometrics 60: 105-108.

Examples

Run this code

# \donttest{
data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs=mallard.site)

(fm_mallard <- stan_pcount(~1~elev+forest, mallardUMF, K=30,
                           chains=3, iter=300))
# }