test_covar_fun

Test passing a covariance function and arguments

These tools were created to test map-scale hypotheses about trends in large
remotely sensed data sets but any data with spatial and temporal variation
can be analyzed. Tests are conducted using the PARTS method for analyzing spatially
autocorrelated time series
(Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>).
The method's unique approach can handle extremely large data sets that other
spatiotemporal models cannot, while still appropriately accounting for
spatial and temporal autocorrelation. This is done by partitioning the data
into smaller chunks, analyzing chunks separately and then combining the
separate analyses into a single, correlated test of the map-scale hypotheses.

Clay Morrow

remotePARTS

Spatiotemporal Autoregression Analyses for Large Data Sets

Anthony Ives

test_covar_fun function

<dl><dt>d</dt>
<dd>numeric vector or matrix of distances</dd>
<dt>covar_FUN</dt>
<dd>distance-based covariance function to use,
which must take <code>d</code> as its first argument</dd>
<dt>covar.pars</dt>
<dd>vector or list of parameters (other than d) passed to the
covar function</dd></dl>

Arguments

Test passing a covariance function and arguments — test_covar_fun

<dl>

<dt>d</dt>
<dd>numeric vector or matrix of distances</dd>


<dt>covar_FUN</dt>
<dd>distance-based covariance function to use,
which must take <code>d</code> as its first argument</dd>


<dt>covar.pars</dt>
<dd>vector or list of parameters (other than d) passed to the
covar function</dd>

</dl>

test_covar_fun: Test passing a covariance function and arguments

Description

Usage

Arguments