Run the JAGS model
runSTADEMmodel(
file_name = NULL,
mcmc_chainLength = 100,
mcmc_burn = 10,
mcmc_thin = 2,
mcmc_chains = 1,
jags_data = NULL,
weekly_params = FALSE,
seed = NULL,
verbose = FALSE,
parallel = TRUE,
DIC = FALSE,
win_model = c("neg_bin", "neg_bin2", "pois", "quasi_pois", "log_space"),
trap_est = TRUE,
use_jagsUI = FALSE
)name (with file path) to save the model as
number of burn-in samples in each MCMC chain
thinning interval for MCMC samples to save
number of MCMC chains
list of data being passed to JAGS model, created by prepJAGS
should weekly estimates be saved? Default is FALSE
starting seed value for RNG, to make results reproducible
passed to the jags function
passed to the jags function
passed to the jags function
what type of distribution should be used when modeling the window counts. neg_bin is a standard negative binomial distribution. neg_bin2 is a more flexible version of a negative binomial, allowing the mean-variance relationship to take different forms. pois is a Poisson distribution. quasi_pois is the quasi-Poisson distribution. log_space assumes that the window counts have normal errors in log-space.
should an estimate of escapement based on the adult fish trap rate be used as a second observation of true total escapement, together with the window counts? Default is TRUE. If FALSE, win_model is automatically set to pois.
should the `jagsUI` package be used to fit the model? Default is `FALSE`, meaning the `rjags` package is used instead.
number of total samples in each MCMC chain
mcmc.list
#runSTADEMmodel()