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

Source:`R/util.R`

`get.initial.values.rSPDE.Rd`

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?