rSPDE (version 2.5.1)

rspde.matern.precision: Precision matrix of the covariance-based rational approximation of stationary Gaussian Matern random fields

Description

rspde.matern.precision is used for computing the precision matrix of the covariance-based rational SPDE approximation of a stationary Gaussian random fields on \(R^d\) with a Matern covariance function $$C(h) = \frac{\sigma^2}{2^(\nu-1)\Gamma(\nu)}(\kappa h)^\nu K_\nu(\kappa h)$$

Usage

rspde.matern.precision(
  kappa,
  nu,
  tau = NULL,
  sigma = NULL,
  rspde.order,
  dim,
  fem_mesh_matrices,
  only_fractional = FALSE,
  return_block_list = FALSE,
  type_rational_approx = "brasil"
)

Value

The precision matrix

Arguments

kappa

Range parameter of the covariance function.

nu

Shape parameter of the covariance function.

tau

Scale parameter of the covariance function. If sigma is not provided, tau should be provided.

sigma

Standard deviation of the covariance function. If tau is not provided, sigma should be provided.

rspde.order

The order of the rational approximation

dim

The dimension of the domain

fem_mesh_matrices

A list containing the FEM-related matrices. The list should contain elements c0, g1, g2, g3, etc.

only_fractional

Logical. Should only the fractional-order part of the precision matrix be returned?

return_block_list

Logical. For type = "covariance", should the block parts of the precision matrix be returned separately as a list?

type_rational_approx

Which type of rational approximation should be used? The current types are "brasil", "chebfun" or "chebfunLB".

Examples

set.seed(123)
nobs <- 101
x <- seq(from = 0, to = 1, length.out = nobs)
fem <- rSPDE.fem1d(x)
kappa <- 40
sigma <- 1
d <- 1
nu <- 2.6
tau <- sqrt(gamma(nu) / (kappa^(2 * nu) * (4 * pi)^(d / 2) *
  gamma(nu + d / 2)))
range <- sqrt(8 * nu) / kappa
op_cov <- matern.operators(
  loc_mesh = x, nu = nu, range = range, sigma = sigma,
  d = 1, m = 2, compute_higher_order = TRUE,
  parameterization = "matern"
)
v <- t(rSPDE.A1d(x, 0.5))
c.true <- matern.covariance(abs(x - 0.5), kappa, nu, sigma)
Q <- rspde.matern.precision(
  kappa = kappa, nu = nu, tau = tau, rspde.order = 2, d = 1,
  fem_mesh_matrices = op_cov$fem_mesh_matrices
)
A <- Diagonal(nobs)
Abar <- cbind(A, A, A)
w <- rbind(v, v, v)
c.approx_cov <- (Abar) %*% solve(Q, w)

# plot the result and compare with the true Matern covariance
plot(x, matern.covariance(abs(x - 0.5), kappa, nu, sigma),
  type = "l", ylab = "C(h)",
  xlab = "h", main = "Matern covariance and rational approximations"
)
lines(x, c.approx_cov, col = 2)