loglik |
the maximized (restricted) Gaussian loglikelihood of a
non-robust (RE)ML fit or NA for a robust fit if
tuning.psi is less than tuning.psi.nr .
|
variogram.model |
the name of the fitted parametric variogram
model.
|
param |
a named numeric vector with the (estimated) variogram
parameters.
|
aniso |
a list with the following components:
isotropic : logical indicating whether an isotropic
variogram was fitted.
aniso : a named numeric vector with the (estimated)
anisotropy parameters.
sincos : a list with sin and cos of the
angles ω, φ and ζ that define the
orientation of the anisotropy ellipsoid.
rotmat : the matrix
(C_1, C_2, C_3) (see
georobIntro ).
sclmat : a vector with the elements 1, 1/f_1,
1/f_2 (see georobIntro ).
|
gradient |
a named numeric vector with the estimating equations
(robust REML) or the gradient of the maximized (restricted) loglikelihood
(Gaussian (RE)ML) evaluated at the solution .
|
tuning.psi |
the value of the tuning constant c of the
ψ_c-function.
|
coefficients |
a named vector with the estimated regression coefficients.
|
fitted.values |
a named vector with the fitted values of the
external drift
X
hatβ.
|
bhat |
a named vector with the predicted spatial random effects
hatB at the data locations.
|
residuals |
a named vector with the residuals
hatε=Y-X hatβ - hatB.
|
rweights |
a named numeric vector with the “robustness weights”
ψ(hatε_i/hatτ) / (hatε_i/hatτ).
|
converged |
logical indicating whether numerical maximization of
the (restricted) loglikelihood by nlminb or optim or root
finding by nleqslv converged.
|
convergence.code |
a diagnostic integer issued by
nlminb , optim (component
convergence ) or nleqslv (component
termcd ) about convergence.
|
iter |
a named integer vector of length two, indicating either
the number of function and gradient evaluations when maximizing
the (restricted) Gaussian loglikelihood by nlminb
or optim , or
the number of function and Jacobian evaluations when solving
the robustified estimating equations by
nleqslv .
|
Tmat |
the compressed design matrix for replicated observations at
coincident locations (integer vector that contains for each observation
the row index of the respective unique location).
|
cov |
a list with covariance matrices (or diagonal variance
vectors). Covariance matrices are stored in compressed form (see
compress ) and can be expanded to square matrices by
expand . What cov actually contains depends on the
flags passed to georob for computing covariances (see
control.georob ). Possible components are:
-
cov.bhat : the covariances of
hatB.
-
cov.betahat : the covariances of
hatβ.
-
cov.bhat.betahat : the covariances of
hatB and
hatβ.
-
cov.delta.bhat : the covariances of
B-hatB.
-
cov.delta.bhat.betahat : the covariances of
B-hatB
and
hatβ.
-
cov.ehat : the covariances of
hatε=Y-X hatβ - hatB.
-
cov.ehat.p.bhat : the covariances of
hatε+ hatB=Y-X hatβ.
-
cov.pred.target : a covariance term required for the
back-trans- formation of kriging predictions of log-transformed data.
|
expectations |
a named numeric vector with the expectations of
dψ_c'(x)/dx (dpsi ) and
ψ_c^2(x) (psi2 ) with respect to a standard normal
distribution.
|
Valphaxi.objects |
a list of matrices in compressed form with
(among others) the following components:
-
gcr.constant : the constant γ_0 (see
expression for V_{α,ξ} in
section Model of georobIntro) .
-
Valphaxi : the correlation matrix
V_{α, ξ} = Γ_θ /
(σ_n^2+σ^2) that includes the spatial nugget effect.
-
Valphaxi.inverse : the inverse of
V_{α, ξ}.
-
log.det.Valphaxi :
log(det(V_{α, ξ})).
|
zhat.objects |
a list of matrices in (partly) compressed form with
the following components:
-
Aalphaxi : the matrix
(X^T V_{α, ξ}^-1 X)^-1 X^T V_{α, ξ}^-1 .
-
Palphaxi : the matrix
I - X A_{α, ξ}.
-
Valphaxi.inverse.Palphaxi : the matrix
V^-1_{α, ξ}
P_{α, ξ}.
|
locations.object |
a list with 3 components:
-
locations : a formula indicating the coordinates of the
measurement locations.
-
locations.coords : a numeric matrix with the coordinates
of the measurement locations.
-
lag.vectors : a numeric matrix with the lag vectors
between any distinct pairs of measurement locations.
|
initial.objects |
a list with 5 components:
-
coefficients : initial estimates of
β computed either by
lmrob or rq .
-
bhat : initial predictions of
B.
-
param : numeric vector with initial estimates of the
variogram parameters, either computed (initial.param = TRUE )
or as passed to georob (initial.param = FALSE ).
-
fit.param : logical vector indicating which variogram
parameters were fitted.
-
aniso : numeric vector with initial estimates of the
anisotropy parameters, either either computed (initial.param = TRUE )
or as passed to georob (initial.param = FALSE ).
-
fit.aniso : logical vector indicating which anisotropy
parameters were fitted.
|
hessian |
a symmetric matrix giving an estimate of the Hessian at
the solution if the model was fitted non-robustly with the argument
hessian = TRUE (see control.georob ). Missing
otherwise.
|
control |
a list with control parameters generated by
control.georob .
|
MD |
optionally a matrix of robust distances in the space spanned by
X (see argument compute.rd
of lmrob.control and
control.georob ).
|
model, x, y |
if requested the model frame, the model matrix and the
response, respectively.
|
na.action , offset , contrasts , xlevels ,
rank , df.residual , call , terms
|
further
components of the fit as described for an object of class
lm .
|