mcgf (version 1.1.1)

.mcgf_sim: 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
)

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.

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.