single_pexp

Computing the Powered Exponential 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

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Spatial Misalignment: Interpolation, Linkage, and Estimation

single_pexp 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 Exponential covariance
function.</dd>
<dt>phi</dt>
<dd>the \(\phi\) parameter from the Exponential covariance function,
controls the range of the spatial dependence.</dd>
<dt>nu</dt>
<dd>the \(\nu \in (0, 2]\) parameter representing the "power"</dd></dl>

Arguments

Powered Exponential covariance function (scalar) — single_pexp

<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 Exponential covariance
function.</dd>


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


<dt>nu</dt>
<dd>the \(\nu \in (0, 2]\) parameter representing the "power"</dd>

</dl>

single_pexp: Powered Exponential covariance function (scalar)

Description

Usage

Value

Arguments

See Also