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

.mcgf_sim

R Documentation

Simulate Markov chain Gaussian field

Description

Simulate Markov chain Gaussian field

Usage

.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.

Value

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.