stan_pcount {ubms}

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,
  ...
)

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

Value

ubmsFitPcount object describing the model fit.

References

Royle JA. 2004. N-mixture models for estimating populaiton size from spatially replicated counts. Biometrics 60: 105-108.

See Also

pcount, unmarkedFramePCount

Examples

data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs=mallard.site)

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