Initial values for log-likelihood optimization in rSPDE models with a latent stationary Gaussian Matern model

Auxiliar function to obtain domain-based initial values for log-likelihood optimization in rSPDE models with a latent stationary Gaussian Matern model

Usage

get.initial.values.rSPDE(
  mesh = NULL,
  mesh.range = NULL,
  graph.obj = NULL,
  n.spde = 1,
  dim = NULL,
  B.tau = NULL,
  B.kappa = NULL,
  B.sigma = NULL,
  B.range = NULL,
  nu = NULL,
  parameterization = c("matern", "spde"),
  include.nu = TRUE,
  log.scale = TRUE,
  nu.upper.bound = NULL
)

Arguments

mesh: An in INLA mesh
mesh.range: The range of the mesh.
graph.obj: A metric_graph object. To be used in case both mesh and mesh.range are NULL.
n.spde: The number of basis functions in the mesh model.
dim: The dimension of the domain.
B.tau: Matrix with specification of log-linear model for \(\tau\). Will be used if parameterization = 'spde'.
B.kappa: Matrix with specification of log-linear model for \(\kappa\). Will be used if parameterization = 'spde'.
B.sigma: Matrix with specification of log-linear model for \(\sigma\). Will be used if parameterization = 'matern'.
B.range: Matrix with specification of log-linear model for \(\rho\), which is a range-like parameter (it is exactly the range parameter in the stationary case). Will be used if parameterization = 'matern'.
nu: The smoothness parameter.
parameterization: Which parameterization to use? matern uses range, std. deviation and nu (smoothness). spde uses kappa, tau and nu (smoothness). The default is matern.
include.nu: Should we also provide an initial guess for nu?
log.scale: Should the results be provided in log scale?
nu.upper.bound: Should an upper bound for nu be considered?

Value

A vector of the form (theta_1,theta_2,theta_3) or where theta_1 is the initial guess for tau, theta_2 is the initial guess for kappa and theta_3 is the initial guess for nu.