This function fits the hierarchical distance sampling model of Royle et al. (2004) to line or point transect data recorded in discerete distance intervals.
stan_distsamp(
formula,
data,
keyfun = c("halfnorm", "exp", "hazard"),
output = c("density", "abund"),
unitsOut = c("ha", "kmsq"),
prior_intercept_state = normal(0, 5),
prior_coef_state = normal(0, 2.5),
prior_intercept_det = normal(0, 5),
prior_coef_det = normal(0, 2.5),
prior_intercept_scale = normal(0, 2.5),
prior_sigma = gamma(1, 1),
...
)Double right-hand side formula describing covariates of detection and occupancy in that order
A unmarkedFrameDS object
One of the following detection functions:
"halfnorm" for half-normal, "exp" for negative exponential,
or "hazard" for hazard-rate (see warning below)
Model either density "density" or abundance "abund"
Units of density. Either "ha" or "kmsq" for
hectares and square kilometers, respectively
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 for the intercept of the scale parameter (i.e., log(scale)) for Hazard-rate models
Prior distribution on random effect standard deviations
Arguments passed to the stan call, such as
number of chains chains or iterations iter
ubmsFitDistsamp object describing the model fit.
Values of `dist.breaks` in the `unmarkedFrameDS` should be as small as possible (<10) to facilitate convergence. Consider converting `unitsIn` from meters to kilometers, for example. See example below.
Use of the hazard-rate key function ("hazard")
typically requires a large sample size in order to get good parameter
estimates. If you have a relatively small number of points/transects (<100),
you should be cautious with the resulting models. Check your results against
estimates from unmarked, which doesn't require as much data to get
good estimates of the hazard-rate shape and scale parameters.
Royle, J. A., Dawson, D. K., & Bates, S. (2004). Modeling abundance effects in distance sampling. Ecology 85: 1591-1597.
# \donttest{
data(issj)
#Note use of km instead of m for distance breaks
jayUMF <- unmarkedFrameDS(y=as.matrix(issj[,1:3]),
siteCovs=issj[,c("elevation","forest")],
dist.breaks=c(0,0.1,0.2,0.3),
unitsIn="km", survey="point")
fm_jay <- stan_distsamp(~1~scale(elevation), jayUMF, chains=3, iter=300)
#>
#> SAMPLING FOR MODEL 'distsamp' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000861 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 8.61 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 2.193 seconds (Warm-up)
#> Chain 1: 1.418 seconds (Sampling)
#> Chain 1: 3.611 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'distsamp' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.001686 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 16.86 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 1.513 seconds (Warm-up)
#> Chain 2: 1.048 seconds (Sampling)
#> Chain 2: 2.561 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'distsamp' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.001058 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 10.58 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
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#> Chain 3:
#> Chain 3: Elapsed Time: 1.549 seconds (Warm-up)
#> Chain 3: 1.367 seconds (Sampling)
#> Chain 3: 2.916 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
# }