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 0.002371 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 23.71 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 2.949 seconds (Warm-up)
#> Chain 1: 2.665 seconds (Sampling)
#> Chain 1: 5.614 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'occuRN' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.00198 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 19.8 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.759 seconds (Warm-up)
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#> Chain 2: 5.269 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'occuRN' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.001804 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 18.04 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 2.36 seconds (Warm-up)
#> Chain 3: 2.375 seconds (Sampling)
#> Chain 3: 4.735 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) 0.8121 0.169 0.508 1.149 190 1.01
#> cov1 -0.0567 0.134 -0.324 0.195 254 1.00
#>
#> Detection (logit-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> -1.23 0.18 -1.59 -0.921 151 1.01
#>
#> LOOIC: 637.952
#> Runtime: 15.618 sec
# }