run_jags_cjs: Run Bayesian CJS Model

In mackerman44/telemetyr: Tools for processing, analysis, and visualization of telemetry data

Run Bayesian CJS Model

Description

Generate MCMC samples from the posteriors of a Bayesian Cormack Jolly-Seber model.

Usage

run_jags_cjs(
  file_path = NULL,
  jags_data = NULL,
  n_chains = 4,
  n_adapt = 1000,
  n_burnin = 2500,
  n_iter = 2500,
  n_thin = 5,
  params_to_save = c("phi", "p", "survship"),
  rng_seed = 4
)

Arguments

file_path	name (with file path) to save the model as. Probably a .txt file
jags_data	list of data to be fed into the JAGS CJS model. Generated from prep_jags_cjs.
n_chains	the number of parallel chains for the model
n_adapt	the number of iterations for adaptation. If n_adapt = 0 then no adaptation takes place.
n_burnin	the number of iterations of the Markov chain to run during the burn-in phase
n_iter	the number of iterations to monitor
n_thin	the thinning interval for monitors
params_to_save	a character vector giving the names of variables to be monitored
rng_seed	random number generator seed, to make results reproducible
...	other parameters to be fed to rjags::jags.model, update.jags or rjags::coda.samples

Value

mcmc.list

Author(s)

Kevin See and Mike Ackerman

mackerman44/telemetyr documentation built on Feb. 15, 2025, 1:08 a.m.