print.summary.SVC_mle

<code><a rd-options="" href="/link/summary.SVC_mle?package=varycoef&version=0.3.0" data-mini-rdoc="varycoef::summary.SVC_mle">summary.SVC_mle</a></code>

the number of significant digits to use when printing.

digits

Printing Method for <code>summary.SVC_mle</code>

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>).

Jakob Dambon

varycoef

Modeling Spatially Varying Coefficients

Jakob A. Dambon

Fabio Sigrist

Reinhard Furrer

print.summary.SVC_mle function

<code><a rd-options='' href='summary.SVC_mle'>summary.SVC_mle</a></code>

print.summary.SVC_mle: Printing Method for `summary.SVC_mle`

Description

Usage

Arguments

Value

See Also