sample_existing_balanced

This function samples a given set of existing data using balanced sampling techniques,
which ensures that each stratum or subgroup of data is proportionally represented in the sample.

internal

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means
to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information
about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management
practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data
distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used,
enabling data-driven stratifications and sampling approaches.

Tristan RH Goodbody

sgsR

Structurally Guided Sampling

Nicholas C Coops

Martin Queinnec

sample_existing_balanced function

<dl><dt>existing</dt>
<dd>sf 'POINT'. Existing plot network.</dd>
<dt>nSamp</dt>
<dd>Numeric. Number of desired samples.</dd>
<dt>algorithm</dt>
<dd>Character. One of <code>lpm2_kdtree, lcube, lcubestratified</code>.</dd>
<dt>p</dt>
<dd>Numeric. Vector with length equal to the number of cells in <code>mraster</code> representing
the inclusion probability for each candidate sample. Default = <code>nSamp / N</code>, where <code>N</code>
is the number of cells.</dd>
<dt>filename</dt>
<dd>Character. Path to write output samples.</dd>
<dt>overwrite</dt>
<dd>Logical. Specify whether <code>filename</code> should be overwritten on disc.</dd>
<dt>...</dt>
<dd>Additional arguments to pass to the selected sampling algorithm.
This is leveraged when used by <code>sample_existing()</code> internally</dd></dl>

Arguments

Sample Existing Data Using Balanced Sampling — sample_existing_balanced

<dl>

<dt>existing</dt>
<dd>sf 'POINT'. Existing plot network.</dd>


<dt>nSamp</dt>
<dd>Numeric. Number of desired samples.</dd>


<dt>algorithm</dt>
<dd>Character. One of <code>lpm2_kdtree, lcube, lcubestratified</code>.</dd>


<dt>p</dt>
<dd>Numeric. Vector with length equal to the number of cells in <code>mraster</code> representing
the inclusion probability for each candidate sample. Default = <code>nSamp / N</code>, where <code>N</code>
is the number of cells.</dd>


<dt>filename</dt>
<dd>Character. Path to write output samples.</dd>


<dt>overwrite</dt>
<dd>Logical. Specify whether <code>filename</code> should be overwritten on disc.</dd>


<dt>...</dt>
<dd>Additional arguments to pass to the selected sampling algorithm.
This is leveraged when used by <code>sample_existing()</code> internally</dd>

</dl>

sample_existing_balanced: Sample Existing Data Using Balanced Sampling

Description

Usage

Value

Arguments