setup_parallel_execution

Internal helper to manage parallel backend setup with support
for user-managed backends, external clusters, and internal clusters.

Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).

Blas M. Benito

spatialRF

Easy Spatial Modeling with Random Forest

setup_parallel_execution function

<dl><dt>cluster</dt>
<dd>A cluster object from parallel::makeCluster(), or NULL</dd>
<dt>n.cores</dt>
<dd>Number of cores for internal cluster creation</dd></dl>

Arguments

Setup parallel execution with automatic backend detection — setup_parallel_execution

<dl>

<dt>cluster</dt>
<dd>A cluster object from parallel::makeCluster(), or NULL</dd>


<dt>n.cores</dt>
<dd>Number of cores for internal cluster creation</dd>

</dl>

setup_parallel_execution: Setup parallel execution with automatic backend detection

Description

Usage

Value

Arguments

See Also