rSPDE (version 2.5.1)

rSPDE.construct.matern.loglike: Constructor of Matern loglikelihood functions.

Description

This function returns a log-likelihood function for a Gaussian process with a Matern covariance function, that is observed under Gaussian measurement noise: \(Y_i = u(s_i) + \epsilon_i\), where \(\epsilon_i\) are iid mean-zero Gaussian variables. The latent model is approximated using the a rational approximation of the fractional SPDE model corresponding to the Gaussian process.

Usage

rSPDE.construct.matern.loglike(
  object,
  Y,
  A,
  sigma.e = NULL,
  mu = 0,
  nu = NULL,
  tau = NULL,
  kappa = NULL,
  sigma = NULL,
  range = NULL,
  parameterization = c("spde", "matern"),
  m = NULL,
  log_scale = TRUE,
  return_negative_likelihood = TRUE
)

Value

The log-likelihood function. The parameters of the returned function are given in the order sigma, kappa, nu, sigma.e, whenever they are available.

Arguments

object

The rational SPDE approximation, computed using matern.operators()

Y

The observations, either a vector or a matrix where the columns correspond to independent replicates of observations.

A

An observation matrix that links the measurement location to the finite element basis.

sigma.e

IF non-null, the standard deviation of the measurement noise will be kept fixed in the returned likelihood.

mu

Expectation vector of the latent field (default = 0).

nu

If non-null, the shape parameter will be kept fixed in the returned likelihood.

tau

If non-null, the tau parameter will be kept fixed in the returned likelihood. (Replaces sigma)

kappa

If non-null, the range parameter will be kept fixed in the returned likelihood.

sigma

If non-null, the standard deviation will be kept fixed in the returned likelihood.

range

If non-null, the range parameter will be kept fixed in the returned likelihood. (Replaces kappa)

parameterization

If spde, then one will use the parameters tau and kappa. If matern, then one will use the parameters sigma and range.

m

If non-null, update the order of the rational approximation, which needs to be a positive integer.

log_scale

Should the parameters be evaluated in log-scale?

return_negative_likelihood

Return minus the likelihood to turn the maximization into a minimization?

See Also

matern.operators(), predict.CBrSPDEobj()

Examples

# this example illustrates how the function can be used for maximum
# likelihood estimation

set.seed(123)
# Sample a Gaussian Matern process on R using a rational approximation
nu <- 0.8
sigma <- 1
sigma.e <- 0.1
n.rep <- 10
n.obs <- 200
n.x <- 51
range <- 0.2
# create mass and stiffness matrices for a FEM discretization
x <- seq(from = 0, to = 1, length.out = n.x)
# Compute the covariance-based rational approximation
op_cov <- matern.operators(
  loc_mesh = x, nu = nu,
  range = range, sigma = sigma, d = 1, m = 2,
  parameterization = "matern"
)
# Sample the model
u <- simulate(op_cov, n.rep)
# Create some data
obs.loc <- runif(n = n.obs, min = 0, max = 1)
A <- rSPDE.A1d(x, obs.loc)
noise <- rnorm(n.obs * n.rep)
dim(noise) <- c(n.obs, n.rep)
Y <- as.matrix(A %*% u + sigma.e * noise)
# \donttest{
# Define the negative likelihood function for optimization
# using CBrSPDE.matern.loglike
# Matern parameterization
loglike <- rSPDE.construct.matern.loglike(op_cov, Y, A, parameterization = "matern")

# The parameters can now be estimated by minimizing mlik with optim

# Choose some reasonable starting values depending on the size of the domain
theta0 <- c(
  get.initial.values.rSPDE(mesh.range = 1, dim = 1),
  log(0.1 * sd(as.vector(Y)))
)
# run estimation and display the results
theta <- optim(theta0, loglike,
  method = "L-BFGS-B"
)
print(data.frame(
  sigma = c(sigma, exp(theta$par[1])), range = c(range, exp(theta$par[2])),
  nu = c(nu, exp(theta$par[3])), sigma.e = c(sigma.e, exp(theta$par[4])),
  row.names = c("Truth", "Estimates")
))
# }