coef.SVC_mle

coef.SVC_selection

<code><a rd-options="" href="/link/SVC_mle?package=varycoef&version=0.3.2" data-mini-rdoc="varycoef::SVC_mle">SVC_mle</a></code> or <code><a rd-options="" href="/link/SVC_selection?package=varycoef&version=0.3.2" data-mini-rdoc="varycoef::SVC_selection">SVC_selection</a></code> object

object

Method to extract the mean effects from an <code><a rd-options="" href="/link/SVC_mle?package=varycoef&version=0.3.2" data-mini-rdoc="varycoef::SVC_mle">SVC_mle</a></code>
or <code><a rd-options="" href="/link/SVC_selection?package=varycoef&version=0.3.2" data-mini-rdoc="varycoef::SVC_selection">SVC_selection</a></code> object.

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

coef.SVC_mle function

<code><a rd-options='' href='SVC_mle'>SVC_mle</a></code> or <code><a rd-options='' href='SVC_selection'>SVC_selection</a></code> object

Method to extract the mean effects from an <code><a rd-options='' href='SVC_mle'>SVC_mle</a></code>
or <code><a rd-options='' href='SVC_selection'>SVC_selection</a></code> object.

coef.SVC_mle: Extact Mean Effects

Description

Usage

Arguments

Value