This function fits the dynamic occupancy model of MacKenzie et al. (2003).
stan_colext(
psiformula = ~1,
gammaformula = ~1,
epsilonformula = ~1,
pformula = ~1,
data,
prior_intercept_psi = logistic(0, 1),
prior_coef_psi = logistic(0, 1),
prior_intercept_gamma = logistic(0, 1),
prior_coef_gamma = logistic(0, 1),
prior_intercept_eps = logistic(0, 1),
prior_coef_eps = logistic(0, 1),
prior_intercept_det = logistic(0, 1),
prior_coef_det = logistic(0, 1),
prior_sigma = gamma(1, 1),
log_lik = TRUE,
...
)Right-hand sided formula for the initial probability of occupancy at each site
Right-hand sided formula for colonization probability
Right-hand sided formula for extinction probability
Right-hand sided formula for detection probability
A unmarkedMultFrame object
Prior distribution for the intercept of the
psi (initial occupancy probability) model; see ?priors for options
Prior distribution for the regression coefficients of the psi model
Prior distribution on intercept for colonization probability
Prior distribution on regression coefficients for colonization probability
Prior distribution on intercept for extinction probability
Prior distribution on regression coefficients for extinction probability
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
ubmsFitColext object describing the model fit.
MacKenzie DI, Nicholas JD, Hines JE, Knutson MG, Franklin AB. 2003. Ecology 84: 2200-2207.
# \donttest{
data(frogs)
umf <- formatMult(masspcru)
umf@y[umf@y > 1] <- 1 #convert counts to presence/absence
umf <- umf[1:100,] #Use only 100 sites
fit_frog <- stan_colext(~1, ~1, ~1, ~1, umf, chains=3, iter=300)
#>
#> SAMPLING FOR MODEL 'colext' 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: 6.194 seconds (Warm-up)
#> Chain 1: 2.115 seconds (Sampling)
#> Chain 1: 8.309 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'colext' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.001486 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 14.86 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 7.181 seconds (Warm-up)
#> Chain 2: 1.978 seconds (Sampling)
#> Chain 2: 9.159 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'colext' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.001066 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 10.66 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
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#> Chain 3:
#> Chain 3: Elapsed Time: 5.219 seconds (Warm-up)
#> Chain 3: 1.761 seconds (Sampling)
#> Chain 3: 6.98 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
# }