This function fits the single season occupancy model of MacKenzie et al. (2002).
Double right-hand side formula describing covariates of detection and occupancy in that order
A unmarkedFrameOccu
object
Prior distribution for the intercept of the
state (occupancy probability) model; see ?priors
for options
Prior distribution for the regression coefficients of the state model
Prior distribution for the intercept of the detection probability model
Prior distribution for the regression coefficients of the detection model
Prior distribution on random effect standard deviations
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
ubmsFitOccu
object describing the model fit.
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.
# \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 0.000125 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.25 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
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#> Chain 1: 0.492 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'occu' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000121 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.21 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.294 seconds (Warm-up)
#> Chain 2: 0.189 seconds (Sampling)
#> Chain 2: 0.483 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'occu' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000235 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 2.35 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.299 seconds (Warm-up)
#> Chain 3: 0.18 seconds (Sampling)
#> Chain 3: 0.479 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
#> Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
#>
#> 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) 3.143 1.409 1.17 6.49 137 1.03
#> cov1 -0.238 0.715 -1.71 1.24 241 1.00
#>
#> Detection (logit-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> -1.82 0.178 -2.15 -1.5 173 1.01
#>
#> LOOIC: 260.941
#> Runtime: 1.454 sec
# }