Bayesian Transition Analysis with JAGS

Usage

bay.ta.jags(
  method,
  parameters,
  gomp_b = NA,
  minimum_age = 15,
  maximum_age = 100,
  error_sd = NA,
  adaptSteps = 2000,
  burnInSteps = 3000,
  runjagsMethod = "rjags",
  nChains = 3,
  thinSteps = 1,
  numSavedSteps = 10000,
  silent.jags = F,
  silent.runjags = F,
  seed = seed
)

Arguments

method: matrix of integers, converted to matrix if not already matrix. Ordinal trait(s) for age estimation.
parameters: vector of character strings. Parameters to monitor.
gomp_b: numeric. Optional prior for parameter Gompertz beta. Default: NA.
minimum_age: numeric. Minimum age for Gompertz distribution. Default: 15.
maximum_age: numeric. Maximum age for Gompertz distribution. Default: 100.
error_sd: numeric. Optional error parameter for age estimates. Default: NA.
adaptSteps: integer. Number of adaptation steps, ignored when framework is set to NIMBLE. Default: 2000.
burnInSteps: integer. Number of steps for burn-in. Default: 3000.
runjagsMethod: string. Mode to run `runjags`, options: "rjags", "rjparallel", "parallel". Default: "rjags".
nChains: integer. Number of chains. Default: 3.
thinSteps: integer. Thinning, i. e. which ith step should be saved. Default: 1 (no thinning).
numSavedSteps: integer. Number of saved steps. Default: 10000. The total number of steps equals thinSteps × numSavedSteps.
silent.jags: TRUE/FALSE Silent mode to run JAGS. Default: FALSE. Ignored when framework is set to NIMBLE.
silent.runjags: TRUE/FALSE Silent mode to run runjags. Default: FALSE. Ignored when framework is set to NIMBLE.
seed: integer. Random number for reproducibility. In parallel processing, each cluster automatically gets different seeds. If no seed is specified, the value is set to today's date as integer.

Value

A list of MCMC chains of class coda::mcmc.list.

Examples

if (FALSE) { # interactive()

  # select Sorsum data with auricular surface after Lovejoy et al. 1985 and
  # convert to matrix
  sorsum <- as.matrix(sorsum_as[,2])

  # example with framework JAGS
  sorsum_res <- bay.ta(framework = "JAGS", method = sorsum)
}