dot-mcgf_sim function

Simulate Markov chain Gaussian field

.mcgf_sim(
  N,
  base,
  lagrangian,
  par_base,
  par_lagr,
  lambda,
  dists,
  sd,
  lag,
  scale_time = 1,
  horizon = 1,
  init = 0,
  mu_c,
  mu_p,
  return_all = FALSE
)

Arguments

• N: Sample size.
• base: Base model, sep or fs for now.
• lagrangian: Lagrangian model, "none" or lagr_tri for now.
• par_base: Parameters for the base model (symmetric).
• par_lagr: Parameters for the Lagrangian model.
• lambda: Weight of the Lagrangian term, $\lambda\in[0, 1]$.
• dists: Distance matrices or arrays.
• sd: Standard deviation for each location.
• lag: Time lag.
• scale_time: Scale of time unit, default is 1. lag is divided by scale_time.
• horizon: Forecast horizon, default is 1.
• init: Initial samples, default is 0.
• mu_c, mu_p: Means of current and past.
• return_all: Logical; if TRUE the joint covariance matrix, arrays of distances and time lag are returned.

Returns

Simulated Markov chain Gaussian field with user-specified covariance structure. The simulation is done by kriging. The output data is in space-wide format. dists must contain h for symmetric models, and h1

and h2 for general stationary models. horizon controls forecasting horizon. sd, mu_c, mu_p, and init must be vectors of appropriate sizes.