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.
Double right-hand side formula describing covariates of detection and abundance in that order
A unmarkedFrameOccu object
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 distribution for the intercept of the
state (abundance) 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
ubmsFitOccuRN object describing the model fit.
Royle JA, Nichols JD. 2003. Estimating abundance from repeated presence-absence data or point counts. Ecology 84: 777-790.
# \donttest{
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))
#>
#> SAMPLING FOR MODEL 'occuRN' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.002867 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 28.67 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#> Chain 1: Elapsed Time: 1.898 seconds (Warm-up)
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#> Chain 1: 3.578 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'occuRN' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.001515 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 15.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 1.774 seconds (Warm-up)
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#> Chain 2:
#>
#> SAMPLING FOR MODEL 'occuRN' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.001521 seconds
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#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 1.743 seconds (Warm-up)
#> Chain 3: 1.479 seconds (Sampling)
#> Chain 3: 3.222 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_occuRN(formula = ~1 ~ cov1, data = woodthrushUMF, chains = 3,
#> iter = 300)
#>
#> Abundance (log-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> (Intercept) 0.790 0.150 0.507 1.127 204 1.01
#> cov1 0.248 0.114 0.040 0.454 326 1.00
#>
#> Detection (logit-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> -1.2 0.165 -1.54 -0.908 305 1
#>
#> LOOIC: 633.392
#> Runtime: 10.342 sec
# }