get.initial.values.rSPDE {rSPDE}

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.