get.initial.values.rSPDE: Initial values for log-likelihood optimization in rSPDE...

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

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

Description

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.

rSPDE documentation built on Nov. 6, 2023, 1:06 a.m.