runSTADEMmodel: Run JAGS model

In KevinSee/damEscapement: Estimate escapement past a dam with a window and trap facility

Run JAGS model

Description

Run the JAGS model

Usage

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
)

Arguments

file_name	name (with file path) to save the model as
mcmc_burn	number of burn-in samples in each MCMC chain
mcmc_thin	thinning interval for MCMC samples to save
mcmc_chains	number of MCMC chains
jags_data	list of data being passed to JAGS model, created by prepJAGS
weekly_params	should weekly estimates be saved? Default is FALSE
seed	starting seed value for RNG, to make results reproducible
verbose	passed to the jags function
parallel	passed to the jags function
DIC	passed to the jags function
win_model	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.
trap_est	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.
mcmc_chainlength	number of total samples in each MCMC chain

Author(s)

Kevin See

Examples

#runSTADEMmodel()

KevinSee/damEscapement documentation built on March 7, 2024, 5:58 p.m.