This function fits the single season N-mixture model of Royle et al. (2004).
Double right-hand side formula describing covariates of detection and abundance in that order
A unmarkedFramePCount 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.
Character specifying mixture: "P" is only option currently.
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
ubmsFitPcount object describing the model fit.
Royle JA. 2004. N-mixture models for estimating populaiton size from spatially replicated counts. Biometrics 60: 105-108.
pcount, unmarkedFramePCount
# \donttest{
data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs=mallard.site)
(fm_mallard <- stan_pcount(~1~elev+forest, mallardUMF, K=30,
chains=3, iter=300))
#>
#> SAMPLING FOR MODEL 'pcount' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.004122 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 41.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 6.361 seconds (Warm-up)
#> Chain 1: 6.324 seconds (Sampling)
#> Chain 1: 12.685 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'pcount' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.004665 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 46.65 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 6.545 seconds (Warm-up)
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#> Chain 2: 12.966 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'pcount' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.00332 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 33.2 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 6.567 seconds (Warm-up)
#> Chain 3: 6.392 seconds (Sampling)
#> Chain 3: 12.959 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 too high. See help('pareto-k-diagnostic') for details.
#>
#> Call:
#> stan_pcount(formula = ~1 ~ elev + forest, data = mallardUMF,
#> K = 30, chains = 3, iter = 300)
#>
#> Abundance (log-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> (Intercept) -1.943 0.224 -2.44 -1.547 201 1.002
#> elev -1.346 0.206 -1.77 -0.941 220 0.996
#> forest -0.734 0.157 -1.04 -0.454 223 0.996
#>
#> Detection (logit-scale):
#> Estimate SD 2.5% 97.5% n_eff Rhat
#> 0.486 0.188 0.0932 0.827 338 0.997
#>
#> LOOIC: 536.301
#> Runtime: 38.610 sec
# }