part_ttest

Correlated t-test for paritioned GLS

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

part_ttest function

<dl><dt>coefs</dt>
<dd>vector average GLS coefficients</dd>
<dt>part.covar_coef</dt>
<dd>an array of covar_coef from each partition</dd>
<dt>rcoefficients</dt>
<dd>an rcoefficeints array, one for each partition</dd>
<dt>df2</dt>
<dd>second degree of freedom from partitioned GLS</dd>
<dt>npart</dt>
<dd>number of partitions</dd></dl>

Arguments

Correlated t-test for paritioned GLS — part_ttest

<dl>

<dt>coefs</dt>
<dd>vector average GLS coefficients</dd>


<dt>part.covar_coef</dt>
<dd>an array of covar_coef from each partition</dd>


<dt>rcoefficients</dt>
<dd>an rcoefficeints array, one for each partition</dd>


<dt>df2</dt>
<dd>second degree of freedom from partitioned GLS</dd>


<dt>npart</dt>
<dd>number of partitions</dd>

</dl>

part_ttest: Correlated t-test for paritioned GLS

Description

Usage

Value

Arguments