Constructor of Matern loglikelihood functions.

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,
  user_nu = NULL,
  user_tau = NULL,
  user_kappa = NULL,
  user_sigma = NULL,
  user_range = NULL,
  parameterization = c("spde", "matern"),
  user_m = NULL,
  log_scale = TRUE,
  return_negative_likelihood = TRUE
)

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).
user_nu: If non-null, the shape parameter will be kept fixed in the returned likelihood.
user_tau: If non-null, the tau parameter will be kept fixed in the returned likelihood. (Replaces sigma)
user_kappa: If non-null, the range parameter will be kept fixed in the returned likelihood.
user_sigma: If non-null, the standard deviation will be kept fixed in the returned likelihood.
user_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.
user_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?

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.

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")
))
#>               sigma     range        nu  sigma.e
#> Truth     1.0000000 0.2000000 0.8000000 0.100000
#> Estimates 0.9974914 0.1800131 0.9778594 0.100258
# }

Usage

Arguments

Value

See also

Examples