mcgf_rs_sim function

Simulate regime-switching Markov chain Gaussian field

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 = list(0),
  mu_p_ls = list(0),
  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.

Returns

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.

Examples

par_s <- list(nugget = 0.5, c = 0.01, gamma = 0.5)
par_t <- list(a = 1, alpha = 0.5)
par_base <- list(par_s = par_s, par_t = par_t)
par_lagr <- list(v1 = 5, v2 = 10)
h1 <- matrix(c(0, 5, -5, 0), nrow = 2)
h2 <- matrix(c(0, 8, -8, 0), nrow = 2)
h <- sqrt(h1^2 + h2^2)
dists <- list(h = h, h1 = h1, h2 = h2)

set.seed(123)
label <- sample(1:2, 1000, replace = TRUE)
X <- mcgf_rs_sim(
    N = 1000,
    label = label,
    base_ls = list("sep"),
    lagrangian_ls = list("none", "lagr_tri"),
    lambda_ls = list(0, 0.5),
    par_base_ls = list(par_base),
    par_lagr_ls = list(NULL, par_lagr),
    dists_ls = list(dists, dists)
)
# plot.ts(X[, -1])

See Also

Other simulations of Markov chain Gaussian fields: mcgf_sim()