Usage
varcov.spatial(coords = NULL, dists.lowertri = NULL,
cov.model = "matern", kappa = 0.5, nugget = 0,
cov.pars = stop("no cov.pars argument"),
inv = FALSE, det = FALSE,
func.inv = c("cholesky", "eigen", "svd", "solve"),
scaled = FALSE, only.decomposition = FALSE,
sqrt.inv = FALSE, try.another.decomposition = TRUE,
only.inv.lower.diag = FALSE)Arguments
coords
an $n \times 2$ matrix with the coordinates
of the data locations. If not provided
the argument dists.lowertri should be provided instead.
dists.lowertri
a vector with the lower triangle of the matrix
of distances between pairs of data points. If not provided
the argument coords should be provided instead.
cov.model
a string indicating the type of the correlation
function. More details in the
documentation for cov.spatial.
Defaults are equivalent to the exponential model. kappa
values of the additional smoothness parameter, only required by
the following correlation
functions: "matern", "powered.exponential", "cauchy" and
"gneiting.matern".
nugget
the value of the nugget parameter $\tau^2$.
cov.pars
a vector with 2 elements or an $ns \times 2$ matrix with
the covariance parameters. The first element (if a vector) or first
column (if a matrix) corresponds to the variance parameter $\sigma^2$.
second element or column corresponds to the cor
inv
if TRUE the inverse of covariance
matrix is returned. Defaults to FALSE.
det
if TRUE the logarithmic of the square root of the
determinant of the covariance
matrix is returned. Defaults to FALSE.
func.inv
algorithm used for the decomposition and inversion of the covariance
matrix. Options are "chol" for Cholesky decomposition,
"svd" for singular value decomposition and "eigen" for
eigenvalues/eigenvectors
scaled
logical indicating whether the covariance matrix should
be scaled. If TRUE the partial sill
parameter $\sigma^2$ is set to 1. Defaults to FALSE.
only.decomposition
logical. If TRUE only the square root
of the covariance matrix is
returned. Defaults to FALSE.
sqrt.inv
if TRUE the square root of the inverse of covariance
matrix is returned. Defaults to FALSE.
try.another.decomposition
logical. If TRUE and the argument
func.inv is one of "cholesky", "svd" or
"solve", the matrix decomposition or inversion is tested and,
if it fails, the argument func.inv
only.inv.lower.diag
logical. If TRUE only the lower triangle and
the diagonal of the inverse of the covariance matrix are
returned. Defaults to FALSE.