ubms (version 1.2.7)

stan_multinomPois: Fit the Multinomial-Poisson Mixture Model

Description

This function fits the multinomial-Poisson mixture model, useful for data collected via survey methods such as removal or double observer sampling.

Usage

stan_multinomPois(
  formula,
  data,
  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

ubmsFitMultinomPois object describing the model fit.

Arguments

formula

Double right-hand side formula describing covariates of detection and abundance in that order

data

A unmarkedFrameMPois object

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

See Also

multinomPois, unmarkedFrameMPois

Examples

# \donttest{
data(ovendata)
ovenFrame <- unmarkedFrameMPois(ovendata.list$data,
                                siteCovs=ovendata.list$covariates,
                                type="removal")

oven_fit <- stan_multinomPois(~1~scale(ufc), ovenFrame, chains=3, iter=300)
# }