worldclim_simulation

This data is adapted from the CAST vignette
<code>vignette("cast02-AOA-tutorial", package = "CAST")</code>.
The original data is derived from the Worldclim global climate variables.

datasets

Assessing predictive models of spatial data can be
challenging, both because these models are typically built for
extrapolating outside the original region represented by training data
and due to potential spatially structured errors, with "hot spots" of
higher than expected error clustered geographically due to spatial
structure in the underlying data. Methods are provided for assessing
models fit to spatial data, including approaches for measuring the
spatial structure of model errors, assessing model predictions at
multiple spatial scales, and evaluating where predictions can be made
safely. Methods are particularly useful for models fit using the
'tidymodels' framework. Methods include Moran's I ('Moran' (1950)
<doi:10.2307/2332142>), Geary's C ('Geary' (1954)
<doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)
<doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from
'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement
metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>)
and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an
implementation of the area of applicability methodology from 'Meyer'
and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an
implementation of multi-scale assessment as described in 'Riemann'
'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).

Michael Mahoney

waywiser

Ergonomic Methods for Assessing Spatial Models

Lucas Johnson

Virgilio Gómez-Rubio

Jakub Nowosad

Posit Software, PBC 

worldclim_simulation function

An sf object with 10,000 rows and 6 columns:<dl>
<dt>bio2</dt>
<dd>Mean Diurnal Range (Mean of monthly (max temp - min temp))</dd><dt>bio10</dt>
<dd>Mean Temperature of Warmest Quarter</dd><dt>bio13</dt>
<dd>Precipitation of Wettest Month</dd><dt>bio19</dt>
<dd>Precipitation of Coldest Quarter</dd><dt>geometry</dt>
<dd>The location of the sampled point.</dd><dt>response</dt>
<dd>A virtual species distribution, generated using the <code>generateSpFromPCA()</code> function from the <code>virtualspecies</code> package.</dd>
</dl>

Format

Simulated data based on WorldClim Bioclimatic variables — worldclim_simulation

An sf object with 10,000 rows and 6 columns:<dl>
<dt>bio2</dt>
<dd>Mean Diurnal Range (Mean of monthly (max temp - min temp))</dd>

<dt>bio10</dt>
<dd>Mean Temperature of Warmest Quarter</dd>

<dt>bio13</dt>
<dd>Precipitation of Wettest Month</dd>

<dt>bio19</dt>
<dd>Precipitation of Coldest Quarter</dd>

<dt>geometry</dt>
<dd>The location of the sampled point.</dd>

<dt>response</dt>
<dd>A virtual species distribution, generated using the <code>generateSpFromPCA()</code> function from the <code>virtualspecies</code> package.</dd>


</dl>

worldclim_simulation: Simulated data based on WorldClim Bioclimatic variables

Description

Usage

Arguments

Format