Bayesian Transition Analysis with NIMBLE

Usage

bay.ta.nimble(
  algorithm,
  method,
  parameters,
  eta = 1,
  gomp_b = NA,
  error_sd = NA,
  minimum_age = 15,
  maximum_age = 100,
  burnInSteps = 2000,
  nChains = 3,
  thinSteps = 1,
  numSteps = 10000,
  seed = FALSE
)

Arguments

algorithm: character string. Either norm for 'simple' ordered regression or mnorm for multinormal ordered regression. Default: norm.
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.
eta: numeric. Parameter for the LKJ distribution, must be > 0. Only used for multinormal ordered regression for the correlation matrix. 1 implies equal correlations, lower values assume stronger correlations. Default: 1.
gomp_b: numeric. Optional prior for parameter Gompertz beta. Default: NA.
error_sd: numeric. Optional error parameter for age estimates. Default: NA.
minimum_age: numeric. Minimum age for Gompertz distribution. Default: 15.
maximum_age: numeric. Maximum age for Gompertz distribution. Default: 100.
burnInSteps: integer. Number of steps for burn-in. Default: 3000.
nChains: integer. Number of chains. Default: 3.
thinSteps: integer. Thinning, i. e. which ith step should be saved. Default: 1 (no thinning).
numSteps: number of steps
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 default settings
  sorsum_res <- bay.ta(method = sorsum)
}