lake

Lake data collected as part of the United States Environmental Protection
Agency's 2012 and 2017 National Lakes Assessment and LakeCat.

datasets

Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.

Michael Dumelle

spmodel

Spatial Statistical Modeling and Prediction

Matt Higham

Ryan A. Hill

Michael Mahon

Jay M. Ver Hoef

lake function

An <code>sf</code> object with 102 rows and 9 columns:
<ul>
<li>comid: A common identifier from NHDPlusV2.</li>
<li>log_cond: The natural logarithm of lake conductivity.</li>
<li>state: The US state: One of Arizona (AZ), Colorado (CO), Nevada (NV),
Utah (UT).</li>
<li>temp: Lake catchment 30-year average temperature (in degrees Celsius).</li>
<li>precip: Lake watershed 30-year average precipitation (in centimeters).</li>
<li>elev: Lake elevation (in meters).</li>
<li>origin: Lake origin (human-made or natural).</li>
<li>year: A factor representing year (2012 or 2017).</li>
<li>geometry: <code>POINT</code> geometry representing coordinates in a NAD83
projection (EPSG: 5070). Distances between points are in meters.</li>
</ul>

Format

National Lakes Assessment Data — lake

National Lakes Assessment Data

lake: National Lakes Assessment Data

Description

Usage

Arguments

Format