Helper functions to set sensible default values for anisotropy parameters and for controlling what variogram and anisotropy parameters should be estimated.
default.aniso(f1 = 1., f2 = 1., omega = 90., phi = 90., zeta = 0.)default.fit.param(
variance = TRUE, snugget = FALSE, nugget = TRUE, scale = TRUE,
alpha = FALSE, beta = FALSE, delta = FALSE, gamma = FALSE,
kappa = FALSE, lambda = FALSE, mu = FALSE, nu = FALSE)
default.fit.aniso(f1 = FALSE, f2 = FALSE, omega = FALSE,
phi = FALSE, zeta = FALSE)
variance (sill \(\sigma^2\)) of the auto-correlated component of the Gaussian random field \(B(\mbox{\boldmath$s$\unboldmath})\).
variance (spatial nugget
\(\sigma^2_{\mathrm{n}}\)) of the seemingly spatially
uncorrelated component of \(B(\mbox{\boldmath$s$\unboldmath})\)
(micro-scale spatial variation; default value snugget = 0
).
variance (nugget \(\tau^2\)) of the independent errors \(\varepsilon(\mbox{\boldmath$s$\unboldmath})\).
range parameter (\(\alpha\)) of the variogram.
names of
additional variogram parameters such as the smoothness parameter
\(\nu\) of the Whittle-Mat<U+008E>rn model (see
RMmodel
and param.names
).
ratio \(f_1\) of lengths of second and first semi-principal
axes of an ellipsoidal surface with constant semi-variance in
\(\mathrm{I}\!\mathrm{R}^3\) (default f1 = 1
).
ratio \(f_2\) of lengths of third and first semi-principal
axes of the semi-variance ellipsoid (default f2 = 1
).
azimuth in degrees of the first semi-principal axis of the
semi-variance ellipsoid (default omega = 90
).
90 degrees minus altitude of the first semi-principal axis of
the semi-variance ellipsoid (default phi = 90
).
angle in degrees between the second semi-principal axis and
the direction of the line defined by the intersection between the
\(x\)-\(y\)-plane and the plane orthogonal to the first
semi-principal axis of the semi-variance ellipsoid through the origin
(default zeta = 0
).
Either a named numeric with initial values of anisotropy parameters
(default.aniso
) or named logical vector, controlling
what parameters should be estimated (default.fit.param
default.fit.aniso
).
georobIntro
for a description of the model and a brief
summary of the algorithms; georob
for (robust) fitting of
spatial linear models.
# NOT RUN {
default.aniso(f1 = 0.5, omega = 45)
default.fit.param(scale=FALSE, alpha = TRUE)
default.fit.aniso(f1 = TRUE, omega = TRUE)
# }
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