prep_par_output

(<code>numeric</code>) Found optimal value of
<code><a rd-options="stats" href="/link/optim?package=varycoef&version=0.3.2&to=stats" data-mini-rdoc="stats::optim">optim</a></code>.

output_par

(<code>spam</code> or <code>matrix(n, n)</code>) Covariance matrix
Sigma of SVC under final covariance parameters.

Sigma_final

(<code>NULL</code> or <code>spam.chol.NgPeyton</code>) If
covariance tapering is used, the Cholesky factor has been calculated
previously and can be used to efficiently update the Cholesky factor of
<code>Sigma_final</code>, which is an <code>spam</code> object.

Rstruct

(<code>logical(1)</code>) Indicates if optimization has been
conducted over full or profile likelihood.

profileLik

(<code>matrix(n, p)</code>) Design matrix

(<code>numeric(p)</code>) Response vector

(<code>NULL</code> or <code>matrix</code>) Hessian of MLE

(<code>numeric(1)</code>) Number of SVC

Prepares and computes the ML estimates and their respective standard errors.

Implements a maximum likelihood estimation (MLE)
method for estimation and prediction of Gaussian process-based
spatially varying coefficient (SVC) models
(Dambon et al. (2021a) <doi:10.1016/j.spasta.2020.100470>).
Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can
be applied such that the method scales to large data. Further, it implements
a joint variable selection of the fixed and random effects (Dambon et al.
(2021b) <arXiv:2101.01932>). The package and its capabilities are described
in (Dambon et al. (2021c) <arXiv:2106.02364>).

Jakob Dambon

varycoef

Modeling Spatially Varying Coefficients

Jakob A. Dambon

Fabio Sigrist

Reinhard Furrer

prep_par_output function

(<code>numeric</code>) Found optimal value of
<code><a rd-options='stats' href='optim'>optim</a></code>.

prep_par_output: Preparation of Parameter Output

Description

Usage

Arguments

Value