Fit the MacKenzie et al. (2002) Occupancy Model

This function fits the single season occupancy model of MacKenzie et al. (2002).

stan_occu(
  formula,
  data,
  prior_intercept_state = logistic(0, 1),
  prior_coef_state = logistic(0, 1),
  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 occupancy in that order

data

A unmarkedFrameOccu object

prior_intercept_state

Prior distribution for the intercept of the state (occupancy probability) 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

ubmsFitOccu object describing the model fit.

References

MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248-2255.

See also

occu, unmarkedFrameOccu

Examples

# \donttest{
data(frogs)
pferUMF <- unmarkedFrameOccu(pfer.bin)

#Add some covariates
siteCovs(pferUMF) <- data.frame(cov1=rnorm(numSites(pferUMF)))

#Fit model
(fm <- stan_occu(~1~cov1, pferUMF, chains=3, iter=300))
#> 
#> SAMPLING FOR MODEL 'occu' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000198 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.98 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.53 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'occu' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000163 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.63 seconds.
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#> Chain 2:                0.595 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'occu' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000163 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.63 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.388 seconds (Warm-up)
#> Chain 3:                0.295 seconds (Sampling)
#> Chain 3:                0.683 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
#> 
#> Call:
#> stan_occu(formula = ~1 ~ cov1, data = pferUMF, chains = 3, iter = 300)
#> 
#> Occupancy (logit-scale):
#>             Estimate   SD   2.5% 97.5% n_eff Rhat
#> (Intercept)    2.951 1.30  0.994  6.04   137 1.01
#> cov1           0.768 0.89 -1.241  2.50   270 1.00
#> 
#> Detection (logit-scale):
#>  Estimate    SD  2.5% 97.5% n_eff Rhat
#>     -1.78 0.197 -2.14 -1.39   192 1.01
#> 
#> LOOIC: 260.485
#> Runtime: 1.808 sec
# }