This function fits the single season occupancy model of MacKenzie et al. (2002).
stan_occu( formula, data, prior_intercept_state = logistic(0, 1), prior_coef_state = logistic(0, 1), prior_intercept_det = logistic(0, 1), prior_coef_det = logistic(0, 1), prior_sigma = gamma(1, 1), ... )
| formula | Double right-hand side formula describing covariates of detection and occupancy in that order |
|---|---|
| data | A |
| prior_intercept_state | Prior distribution for the intercept of the
state (occupancy probability) model; see |
| prior_coef_state | Prior distribution for the regression coefficients of the state model |
| prior_intercept_det | Prior distribution for the intercept of the detection probability model |
| prior_coef_det | Prior distribution for the regression coefficients of the detection model |
| prior_sigma | Prior distribution on random effect standard deviations |
| ... | Arguments passed to the |
ubmsFitOccu object describing the model fit.
MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248-2255.
occu, unmarkedFrameOccu
# \donttest{ data(frogs) pferUMF <- unmarkedFrameOccu(pfer.bin) #Add some covariates siteCovs(pferUMF) <- data.frame(cov1=rnorm(numSites(pferUMF))) #Fit model (fm <- stan_occu(~1~cov1, pferUMF, chains=3, iter=300))#> #> SAMPLING FOR MODEL 'occu' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 0.000117 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.17 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 1: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 1: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 1: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 1: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 1: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 1: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 1: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 1: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 1: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 1: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 1: Iteration: 300 / 300 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.203014 seconds (Warm-up) #> Chain 1: 0.110751 seconds (Sampling) #> Chain 1: 0.313765 seconds (Total) #> Chain 1: #> #> SAMPLING FOR MODEL 'occu' NOW (CHAIN 2). #> Chain 2: #> Chain 2: Gradient evaluation took 0.00011 seconds #> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.1 seconds. #> Chain 2: Adjust your expectations accordingly! #> Chain 2: #> Chain 2: #> Chain 2: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 2: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 2: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 2: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 2: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 2: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 2: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 2: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 2: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 2: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 2: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 2: Iteration: 300 / 300 [100%] (Sampling) #> Chain 2: #> Chain 2: Elapsed Time: 0.194265 seconds (Warm-up) #> Chain 2: 0.104856 seconds (Sampling) #> Chain 2: 0.299121 seconds (Total) #> Chain 2: #> #> SAMPLING FOR MODEL 'occu' NOW (CHAIN 3). #> Chain 3: #> Chain 3: Gradient evaluation took 0.000116 seconds #> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.16 seconds. #> Chain 3: Adjust your expectations accordingly! #> Chain 3: #> Chain 3: #> Chain 3: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 3: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 3: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 3: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 3: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 3: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 3: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 3: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 3: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 3: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 3: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 3: Iteration: 300 / 300 [100%] (Sampling) #> Chain 3: #> Chain 3: Elapsed Time: 0.237109 seconds (Warm-up) #> Chain 3: 0.126114 seconds (Sampling) #> Chain 3: 0.363223 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 #> http://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 #> http://mc-stan.org/misc/warnings.html#tail-ess#> #> Call: #> stan_occu(formula = ~1 ~ cov1, data = pferUMF, chains = 3, iter = 300) #> #> Occupancy (logit-scale): #> Estimate SD 2.5% 97.5% n_eff Rhat #> (Intercept) 3.053 1.345 1.04 5.96 179 1.01 #> cov1 -0.018 0.759 -1.55 1.36 206 1.00 #> #> Detection (logit-scale): #> Estimate SD 2.5% 97.5% n_eff Rhat #> -1.81 0.188 -2.16 -1.43 234 1 #> #> LOOIC: 261.783 #> Runtime: 0.976 sec# }