precision.rSPDEobj1d {rSPDE} | R Documentation |
Get the precision matrix of rSPDEobj1d objects
Description
Function to get the precision matrix of a rSPDEobj1d object
Usage
## S3 method for class 'rSPDEobj1d'
precision(
object,
loc = NULL,
nu = NULL,
kappa = NULL,
sigma = NULL,
range = NULL,
tau = NULL,
m = NULL,
ordering = c("field", "location"),
ldl = FALSE,
...
)
Arguments
object |
The covariance-based rational SPDE approximation,
computed using |
loc |
If non-null, update the locations where to evaluate the model. |
nu |
If non-null, update the shape parameter of the covariance function. |
kappa |
If non-null, update the range parameter of the covariance function. |
sigma |
If non-null, update the standard deviation of the covariance function. |
range |
If non-null, update the range parameter of the covariance function. |
tau |
If non-null, update the parameter tau. |
m |
If non-null, update the order of the rational approximation, which needs to be a positive integer. |
ordering |
Return the matrices ordered by field or by location? |
ldl |
Directly build the LDL factorization of the precision matrix? |
... |
Currently not used. |
Value
A list containing the precision matrix Q
of the process and its derivatives if they exist, and
a matrix A
that extracts the elements corresponding to the process. If ldl=TRUE
, the LDL factorization
is returned instead of Q
. If the locations are not ordered, the precision matrix is given for the ordered locations,
but the A
matrix returns to the original order.
See Also
simulate.rSPDEobj1d()
, matern.rational()
Examples
# Compute the covariance-based rational approximation of a
# Gaussian process with a Matern covariance function on R
sigma <- 1
nu <- 0.8
range <- 0.2
# create mass and stiffness matrices for a FEM discretization
x <- seq(from = 0, to = 1, length.out = 101)
op_cov <- matern.rational(
loc = x, nu = nu,
range = range, sigma = sigma, m = 2,
parameterization = "matern"
)
# Get the precision matrix:
prec_matrix <- precision(op_cov)