truelove_covar

This list object is an example of the output produced by function <code>make.covar</code>. It was produced by the following script, and is provided to reduce the computation load of some of the examples in this library.

<code>data=truelove</code>

<code>distancematrix&lt;-calc.dist(truelove)</code>

<code>Truelove_bins&lt;-make.bin(distancematrix,type="ALL",p.dist=20)</code>

<code>binsize&lt;-Truelove_bins[1][[1]] #truelove lowland bin sizes}

<code>binname&lt;-Truelove_bins[2][[1]] #truelove lowland bin names}

<code>covariances&lt;-make.covar(truelove,distancematrix,binsize,binname)</code></code>
<seealso><code><a href="/link/calc.dist?package=sesem&version=1.0.1" rd-options="" data-mini-rdoc="sesem::calc.dist">calc.dist</a></code>, <code><a href="/link/make.bin?package=sesem&version=1.0.1" rd-options="" data-mini-rdoc="sesem::make.bin">make.bin</a></code>, <code><a href="/link/make.covar?package=sesem&version=1.0.1" rd-options="" data-mini-rdoc="sesem::make.covar">make.covar</a></code></seealso>
<keyword>datasets</keyword></code>

Structural equation modeling (SEM) is a powerful statistical
    approach for the testing of networks of direct and indirect theoretical
    causal relationships in complex datasets with intercorrelated dependent and
    independent variables. Here we implement a simple method for spatially
    explicit SEM (SE-SEM) based on the analysis of variance covariance matrices
    calculated across a range of lag distances. This method provides readily
    interpretable plots of the change in path coefficients across scale.

truelove_covar: Truelove lowland example covariances

Description

Arguments

format