standard_error

Computes the standard error of the mean of a numeric vector as <code>round(sqrt(var(x)/length(x)), 3)</code>

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

standard_error function

<dl><dt>x</dt>
<dd>A numeric vector.</dd></dl>

Arguments

Standard error of the mean of a numeric vector — standard_error

<dl>

<dt>x</dt>
<dd>A numeric vector.</dd>

</dl>

standard_error: Standard error of the mean of a numeric vector

Description

Usage

Value

Arguments

Details

See Also

Examples