single_cs

Computing the Spherical covariance function for a scalar
distance.

internal

Provides functions to estimate, predict and interpolate areal
data. For estimation and prediction we assume areal data is an average
of an underlying continuous spatial process as in Moraga et
al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
<doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
<doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
(mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Lucas da Cunha Godoy

smile

Spatial Misalignment: Interpolation, Linkage, and Estimation

single_cs function

<dl><dt>d</dt>
<dd>a scalar representing the distance on which it is desired to
evaluate the covariance function.</dd>
<dt>sigsq</dt>
<dd>the \(\sigma^2\) parameter from the Spherical covariance.
function.</dd>
<dt>phi</dt>
<dd>the \(\phi\) parameter from the Spherical covariance function,
controls the range of the spatial dependence.</dd></dl>

Arguments

Cubic spline covariance function (scalar) — single_cs

<dl>

<dt>d</dt>
<dd>a scalar representing the distance on which it is desired to
evaluate the covariance function.</dd>


<dt>sigsq</dt>
<dd>the \(\sigma^2\) parameter from the Spherical covariance.
function.</dd>


<dt>phi</dt>
<dd>the \(\phi\) parameter from the Spherical covariance function,
controls the range of the spatial dependence.</dd>

</dl>

single_cs: Cubic spline covariance function (scalar)

Description

Usage

Value

Arguments

See Also