Update parameters of CBrSPDEobj objects

Function to change the parameters of a CBrSPDEobj object

Usage

# S3 method for CBrSPDEobj
update(
  object,
  user_nu = NULL,
  user_alpha = NULL,
  user_kappa = NULL,
  user_tau = NULL,
  user_sigma = NULL,
  user_range = NULL,
  user_theta = NULL,
  user_m = NULL,
  mesh = NULL,
  loc_mesh = NULL,
  graph = NULL,
  range_mesh = NULL,
  compute_higher_order = object$higher_order,
  parameterization = NULL,
  type_rational_approximation = object$type_rational_approximation,
  return_block_list = object$return_block_list,
  ...
)

Arguments

object

The covariance-based rational SPDE approximation, computed using matern.operators()

user_nu

If non-null, update the shape parameter of the covariance function. Will be used if parameterization is 'matern'.

user_alpha

If non-null, update the fractional SPDE order parameter. Will be used if parameterization is 'spde'.

user_kappa

If non-null, update the parameter kappa of the SPDE. Will be used if parameterization is 'spde'.

user_tau

If non-null, update the parameter tau of the SPDE. Will be used if parameterization is 'spde'.

user_sigma

If non-null, update the standard deviation of the covariance function. Will be used if parameterization is 'matern'.

user_range

If non-null, update the range parameter of the covariance function. Will be used if parameterization is 'matern'.

user_theta

For non-stationary models. If non-null, update the vector of parameters.

user_m

If non-null, update the order of the rational approximation, which needs to be a positive integer.

mesh

An optional inla mesh. Replaces d, C and G.

loc_mesh

The mesh locations used to construct the matrices C and G. This option should be provided if one wants to use the rspde_lme() function and will not provide neither graph nor mesh. Only works for 1d data. Does not work for metric graphs. For metric graphs you should supply the graph using the graph argument.

graph

An optional metric_graph object. Replaces d, C and G.

range_mesh

The range of the mesh. Will be used to provide starting values for the parameters. Will be used if mesh and graph are NULL, and if one of the parameters (kappa or tau for spde parameterization, or sigma or range for matern parameterization) are not provided.

compute_higher_order

Logical. Should the higher order finite element matrices be computed?

parameterization

If non-null, update the parameterization. Only works for stationary models.

type_rational_approximation

Which type of rational approximation should be used? The current types are "chebfun", "brasil" or "chebfunLB".

return_block_list

Logical. For type = "covariance", should the block parts of the precision matrix be returned separately as a list?

...

Currently not used.

Value

It returns an object of class "CBrSPDEobj. This object contains the same quantities listed in the output of matern.operators().

See also

simulate.CBrSPDEobj(), matern.operators()

Examples

# Compute the covariance-based rational approximation of a
# Gaussian process with a Matern covariance function on R
kappa <- 10
sigma <- 1
nu <- 0.8
range <- sqrt(8 * nu) / kappa

# create mass and stiffness matrices for a FEM discretization
x <- seq(from = 0, to = 1, length.out = 101)
fem <- rSPDE.fem1d(x)

# compute rational approximation of covariance function at 0.5
op_cov <- matern.operators(
  loc_mesh = x, nu = nu,
  range = range, sigma = sigma, d = 1, m = 2,
  parameterization = "matern"
)
op_cov
#> Type of approximation:  Covariance-Based Matern SPDE Approximation 
#> Parameterization:  matern 
#> Type of rational approximation:  chebfun 
#> Parameters of covariance function: range =  0.2529822 , sigma =  1 , nu =  0.8 
#> Order or rational approximation:  2 
#> Size of discrete operators:  101  x  101 
#> Stationary Model

# Update the range parameter of the model:
op_cov <- update(op_cov, user_kappa = 20)
op_cov
#> Type of approximation:  Covariance-Based Matern SPDE Approximation 
#> Parameterization:  matern 
#> Type of rational approximation:  chebfun 
#> Parameters of covariance function: range =  0.2529822 , sigma =  1 , nu =  0.8 
#> Order or rational approximation:  2 
#> Size of discrete operators:  101  x  101 
#> Stationary Model