print.summary.SVC_mle

<code><a rd-options="" href="/link/summary.SVC_mle?package=varycoef&version=0.3.3" 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>). 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

print.summary.SVC_mle function

<dl><dt>x</dt>
<dd><code>summary.SVC_mle</code></dd>
<dt>digits</dt>
<dd>the number of significant digits to use when printing.</dd>
<dt>...</dt>
<dd>further arguments</dd></dl>

Arguments

Printing Method for summary.SVC_mle — print.summary.SVC_mle

<dl>

<dt>x</dt>
<dd><code>summary.SVC_mle</code></dd>


<dt>digits</dt>
<dd>the number of significant digits to use when printing.</dd>


<dt>...</dt>
<dd>further arguments</dd>

</dl>

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

Description

Usage

Value

Arguments

See Also