multiCV

Multiple Cross-Validation with increasing feature space clusteres

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>.

Hanna Meyer

CAST

'caret' Applications for Spatial-Temporal Models

Carles Milà

Marvin Ludwig

Jan Linnenbrink

Philipp Otto

Chris Reudenbach

Thomas Nauss

Edzer Pebesma

multiCV function

<dl><dt>model</dt>
<dd>the model used to get the AOA</dd>
<dt>length.out</dt>
<dd>Numeric. Only used if multiCV=TRUE. Number of cross-validation folds. See details.</dd>
<dt>method</dt>
<dd>Character. Method used for distance calculation. Currently euclidean distance (L2) and Mahalanobis distance (MD) are implemented but only L2 is tested. Note that MD takes considerably longer. See ?aoa for further explanation</dd>
<dt>useWeight</dt>
<dd>Logical. Only if a model is given. Weight variables according to importance in the model?</dd>
<dt>...</dt>
<dd>additional parameters to trainDI</dd></dl>

Arguments

MultiCV — multiCV

<dl>

<dt>model</dt>
<dd>the model used to get the AOA</dd>


<dt>length.out</dt>
<dd>Numeric. Only used if multiCV=TRUE. Number of cross-validation folds. See details.</dd>


<dt>method</dt>
<dd>Character. Method used for distance calculation. Currently euclidean distance (L2) and Mahalanobis distance (MD) are implemented but only L2 is tested. Note that MD takes considerably longer. See ?aoa for further explanation</dd>


<dt>useWeight</dt>
<dd>Logical. Only if a model is given. Weight variables according to importance in the model?</dd>


<dt>...</dt>
<dd>additional parameters to trainDI</dd>

</dl>

multiCV: MultiCV

Description

Usage

Value

Arguments