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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, ...)
dists.lowertri
should be provided instead.coords
should be provided instead.cov.spatial
.
Defaults are equivalent to the exponential model."matern"
, "powered.exponential"
, "cauchy"
and
"gneiting.matern"
.TRUE
the inverse of covariance
matrix is returned. Defaults to FALSE
.TRUE
the logarithmic of the square root of the
determinant of the covariance
matrix is returned. Defaults to FALSE
."chol"
for Cholesky decomposition,
"svd"
for singular value decomposition and "eigen"
for
eigenvalues/eigenvectors TRUE
the partial sill
parameter $\sigma^2$ is set to 1. Defaults to FALSE
.TRUE
only the square root
of the covariance matrix is
returned. Defaults to FALSE
.TRUE
the square root of the inverse of covariance
matrix is returned. Defaults to FALSE
.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
TRUE
only the lower triangle and
the diagonal of the inverse of the covariance matrix are
returned. Defaults to FALSE
.cov.spatial
. Typically this is an auxiliary function called by other
functions in the cov.spatial
for more information on the
correlation functions; chol
, solve
,
svd
and eigen
for matrix inversion and/or decomposition.