dot-mcgf_rs_sim: Simulate regime-switching Markov chain Gaussian field
In mcgf: Markov Chain Gaussian Fields Simulation and Parameter Estimation

.mcgf_rs_sim

R Documentation

Simulate regime-switching Markov chain Gaussian field

Description

Simulate regime-switching Markov chain Gaussian field

Usage

.mcgf_rs_sim(
  N,
  label,
  base_ls,
  lagrangian_ls,
  par_base_ls,
  par_lagr_ls,
  lambda_ls,
  dists_ls,
  sd_ls,
  lag_ls,
  scale_time = 1,
  init = 0,
  mu_c_ls,
  mu_p_ls,
  return_all = FALSE
)

Arguments

`N`	Sample size.
`label`	Vector of regime labels of the same length as `N`.
`base_ls`	List of base model, `sep` or `fs` for now.
`lagrangian_ls`	List of Lagrangian model, "none" or `lagr_tri` for now.
`par_base_ls`	List of parameters for the base model.
`par_lagr_ls`	List of parameters for the Lagrangian model.
`lambda_ls`	List of weight of the Lagrangian term, `\lambda\in[0, 1]`.
`dists_ls`	List of distance matrices or arrays.
`sd_ls`	List of standard deviation for each location.
`lag_ls`	List of time lags.
`scale_time`	Scale of time unit, default is 1. Elements in `lag_ls` are divided by `scale_time`.
`init`	Initial samples, default is 0.
`mu_c_ls`, `mu_p_ls`	List of means of current and past.
`return_all`	Logical; if TRUE the joint covariance matrix, arrays of distances and time lag are returned.

Value

Simulated regime-switching Markov chain Gaussian field with user-specified covariance structures. The simulation is done by kriging. The output data is in space-wide format. Each element in dists_ls must contain h for symmetric models, and h1 and h2 for general stationary models. init can be a scalar or a vector of appropriate size. List elements in sd_ls, mu_c_ls, and mu_p_ls must be vectors of appropriate sizes.