ex_data_1D

<p>A dataset containing daily CO2 efflux levels as a dependent variable and
temperature as an independent variables within a temperate forest
for a full year.</p>

datasets

Implementation of the autocorrelated conditioned Latin
Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial)
data. The acLHS algorithm is an extension of the conditioned Latin Hypercube
Sampling (cLHS) algorithm that allows sampled data to have similar
correlative and statistical features of the original data. Only a properly
formatted dataframe needs to be provided to yield subsample indices from
the primary function. For more details about the cLHS algorithm, see Minasny
and McBratney (2006), <doi:10.1016/j.cageo.2005.12.009>. For acLHS, see Le
and Vargas (2024) <doi:10.1016/j.cageo.2024.105539>.

Gabriel Laboy

aclhs

Autocorrelated Conditioned Latin Hypercube Sampling

Van Huong Le

Rodrigo Vargas

ex_data_1D function


<div class="section" id="ex-data-d">
<h3><code>ex_data_1D</code></h3><p></p><p>
</p><p>A data frame with 365 rows and 3 columns:</p><dl>
<dt>Time</dt>
<dd><p>The day of the year</p></dd><p></p><p><dt>Temp</dt>
<dd><p>The temperature</p></dd></p><p><dt>CO2</dt>
<dd><p>The carbon dioxide efflux</p></dd></p><p>
</p></dl><p>tools:::Rd_expr_doi("10.1007/s11104-017-3506-4")</p>
</div>


Format

Daily CO2 Efflux Measurements within a Temperate Forest — ex_data_1D


<div class='section' id='ex-data-d'>
<h3><code>ex_data_1D</code></h3>


<p>A data frame with 365 rows and 3 columns:</p><dl>
<dt>Time</dt>
<dd><p>The day of the year</p></dd>

<dt>Temp</dt>
<dd><p>The temperature</p></dd>

<dt>CO2</dt>
<dd><p>The carbon dioxide efflux</p></dd>


</dl><p>tools:::Rd_expr_doi("10.1007/s11104-017-3506-4")</p>
</div>


ex_data_1D: Daily CO2 Efflux Measurements within a Temperate Forest

Description

Usage

Arguments

Format

`ex_data_1D`

Examples