Simulation of a fractional SPDE using the covariance-based rational SPDE approximation

The function samples a Gaussian random field based using the covariance-based rational SPDE approximation.

Usage

# S3 method for class 'CBrSPDEobj2d'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  user_nu = NULL,
  user_hx = NULL,
  user_hy = NULL,
  user_hxy = NULL,
  user_sigma = NULL,
  user_m = NULL,
  ...
)

Arguments

object: The covariance-based rational SPDE approximation, computed using matern2d.operators()
nsim: The number of simulations.
seed: An object specifying if and how the random number generator should be initialized (‘seeded’).
user_nu: If non-null, update the shape parameter of the covariance function.
user_hx: If non-null, update the hx parameter.
user_hy: If non-null, update the hy parameter.
user_hxy: If non-null, update the hxy parameter.
user_sigma: If non-null, update the standard deviation of the covariance function.
user_m: If non-null, update the order of the rational approximation, which needs to be a positive integer.
...: Currently not used.

Value

A matrix with the n samples as columns.

Examples

library(fmesher)
n_loc <- 2000
loc_2d_mesh <- matrix(runif(n_loc * 2), n_loc, 2)
mesh_2d <- fm_mesh_2d(loc = loc_2d_mesh, cutoff = 0.03, max.edge = c(0.1, 0.5))
op <- matern2d.operators(mesh = mesh_2d, sigma = 1, nu = 1, hx = 0.1, hy = 0.1, hxy = 0)
u <- simulate(op)