weights_from_distance_matrix

Transforms a distance matrix into weights (1/distance.matrix) normalized by the row sums. Used to compute Moran's I values and Moran's Eigenvector Maps. Allows to apply a threshold to the distance matrix before computing the weights.

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

weights_from_distance_matrix function

<dl><dt>distance.matrix</dt>
<dd>Distance matrix. Default: <code>NULL</code>.</dd>
<dt>distance.threshold</dt>
<dd>Numeric, positive, in the range of values of <code>distance.matrix</code>. Distances below this value in the distance matrix are set to 0., Default: <code>0</code>.</dd></dl>

Arguments

Transforms a distance matrix into a matrix of weights — weights_from_distance_matrix

<dl>

<dt>distance.matrix</dt>
<dd>Distance matrix. Default: <code>NULL</code>.</dd>


<dt>distance.threshold</dt>
<dd>Numeric, positive, in the range of values of <code>distance.matrix</code>. Distances below this value in the distance matrix are set to 0., Default: <code>0</code>.</dd>

</dl>

weights_from_distance_matrix: Transforms a distance matrix into a matrix of weights

Description

Usage

Value

Arguments

See Also

Examples