corModelExponential

corModelGaussian

corModelSpherical

Note that, currently, only three of these models are implemented
in the <code>sptotal</code> package: <code>corModelExponential()</code>,
 <code>corModelGaussian()</code>, and <code>corModelSpherical()</code>.

Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.

Higham Matt

sptotal

Predicting Totals and Weighted Sums from Spatial Data

Matt Higham

Jay Ver Hoef

Bryce Frank

Michael Dumelle

corModelExponential function

<dl><dt>distance.matrix</dt>
<dd>The distance matrix for sampled sites</dd>
<dt>range</dt>
<dd>The range that determines how quickly covariance
among sites tapers</dd></dl>

Arguments

<ul>
<li><code>corModelExponential()</code>: Exponential Correlation Structure</li>
<li><code>corModelGaussian()</code>: Gaussian Correlation Structure</li>
<li><code>corModelSpherical()</code>: Spherical Correlation Structure</li>
</ul>

Functions

Spatial Correlation Models — corModelExponential

<dl>

<dt>distance.matrix</dt>
<dd>The distance matrix for sampled sites</dd>


<dt>range</dt>
<dd>The range that determines how quickly covariance
among sites tapers</dd>

</dl>

corModelExponential: Spatial Correlation Models

Description

Usage

Value

Arguments

Functions