print.summary.SVC_mle

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) <doi:10.1080/13658816.2022.2097684>). The package and its
capabilities are described in (Dambon et al. (2021c)
<doi:10.48550/arXiv.2106.02364>).

Jakob A. Dambon

varycoef

Modeling Spatially Varying Coefficients

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>

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

Description

Usage

Value

Arguments

See Also