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

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,
  ...
)

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

Value

ubmsFitMultinomPois object describing the model fit.

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)
#> 
#> SAMPLING FOR MODEL 'multinomPois' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000183 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.83 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 1: Iteration:  30 / 300 [ 10%]  (Warmup)
#> Chain 1: Iteration:  60 / 300 [ 20%]  (Warmup)
#> Chain 1: Iteration:  90 / 300 [ 30%]  (Warmup)
#> Chain 1: Iteration: 120 / 300 [ 40%]  (Warmup)
#> Chain 1: Iteration: 150 / 300 [ 50%]  (Warmup)
#> Chain 1: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 1: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 1: Iteration: 210 / 300 [ 70%]  (Sampling)
#> Chain 1: Iteration: 240 / 300 [ 80%]  (Sampling)
#> Chain 1: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 1: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.127 seconds (Warm-up)
#> Chain 1:                0.109 seconds (Sampling)
#> Chain 1:                0.236 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'multinomPois' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000126 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.26 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 2: Iteration:  30 / 300 [ 10%]  (Warmup)
#> Chain 2: Iteration:  60 / 300 [ 20%]  (Warmup)
#> Chain 2: Iteration:  90 / 300 [ 30%]  (Warmup)
#> Chain 2: Iteration: 120 / 300 [ 40%]  (Warmup)
#> Chain 2: Iteration: 150 / 300 [ 50%]  (Warmup)
#> Chain 2: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 2: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 2: Iteration: 210 / 300 [ 70%]  (Sampling)
#> Chain 2: Iteration: 240 / 300 [ 80%]  (Sampling)
#> Chain 2: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 2: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.137 seconds (Warm-up)
#> Chain 2:                0.116 seconds (Sampling)
#> Chain 2:                0.253 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'multinomPois' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000134 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.34 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 3: Iteration:  30 / 300 [ 10%]  (Warmup)
#> Chain 3: Iteration:  60 / 300 [ 20%]  (Warmup)
#> Chain 3: Iteration:  90 / 300 [ 30%]  (Warmup)
#> Chain 3: Iteration: 120 / 300 [ 40%]  (Warmup)
#> Chain 3: Iteration: 150 / 300 [ 50%]  (Warmup)
#> Chain 3: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 3: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 3: Iteration: 210 / 300 [ 70%]  (Sampling)
#> Chain 3: Iteration: 240 / 300 [ 80%]  (Sampling)
#> Chain 3: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 3: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.129 seconds (Warm-up)
#> Chain 3:                0.113 seconds (Sampling)
#> Chain 3:                0.242 seconds (Total)
#> Chain 3: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
# }