.scalePredictors

internal

Implements species distribution modeling and ecological niche
modeling, including: bias correction, spatial cross-validation, model
evaluation, raster interpolation, biotic "velocity" (speed and
direction of movement of a "mass" represented by a raster), interpolating
across a time series of rasters, and use of spatially imprecise records.
The heart of the package is a set of "training" functions which
automatically optimize model complexity based number of available
occurrences. These algorithms include MaxEnt, MaxNet, boosted regression
trees/gradient boosting machines, generalized additive models,
generalized linear models, natural splines, and random forests. To enhance
interoperability with other modeling packages, no new classes are created.
The package works with 'PROJ6' geodetic objects and coordinate reference
systems.

Adam B. Smith

enmSdmX

Species Distribution Modeling and Ecological Niche Modeling

.scalePredictors function

<dl><dt>scale</dt>
<dd>Either <code>NA</code> (default), or <code>TRUE</code> or <code>FALSE</code>. If <code>TRUE</code>, the predictors will be centered and scaled by dividing by subtracting their means then dividing by their standard deviations. The means and standard deviations will be returned in the model object under an element named "<code>scales</code>". For example, if you do something like <code>model &lt;- trainGLM(data, scale=TRUE)</code>, then you can get the means and standard deviations using <code>model$scales$means</code> and <code>model$scales$sds</code>. If <code>FALSE</code>, no scaling is done. If <code>NA</code> (default), then the function will check to see if non-factor predictors have means ~0 and standard deviations ~1. If not, then a warning will be printed, but the function will continue to do it's operations.</dd>
<dt>preds</dt>
<dd>A character vector with names of the predictors in <code>data</code>.</dd>
<dt>data</dt>
<dd>A data frame.</dd></dl>

Arguments

Scales predictors — .scalePredictors

<dl>

<dt>scale</dt>
<dd>Either <code>NA</code> (default), or <code>TRUE</code> or <code>FALSE</code>. If <code>TRUE</code>, the predictors will be centered and scaled by dividing by subtracting their means then dividing by their standard deviations. The means and standard deviations will be returned in the model object under an element named "<code>scales</code>". For example, if you do something like <code>model &lt;- trainGLM(data, scale=TRUE)</code>, then you can get the means and standard deviations using <code>model$scales$means</code> and <code>model$scales$sds</code>. If <code>FALSE</code>, no scaling is done. If <code>NA</code> (default), then the function will check to see if non-factor predictors have means ~0 and standard deviations ~1. If not, then a warning will be printed, but the function will continue to do it's operations.</dd>


<dt>preds</dt>
<dd>A character vector with names of the predictors in <code>data</code>.</dd>


<dt>data</dt>
<dd>A data frame.</dd>

</dl>

.scalePredictors: Scales predictors

Description

Usage

Arguments