stan_occuRN {ubms}

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,
  ...
)

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

Value

ubmsFitOccuRN object describing the model fit.

References

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

See Also

occuRN, unmarkedFrameOccu

Examples

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))