cleanPredictors: Clean predictor variables regarding corelation and variance

In environmentalinformatics-marburg/gpm: Geospatial predictive modeling using parameterized and unparameterized models.

Description

Clean predictor variables regarding zero or near-zero variability or highly correlated variables.

Usage

## S4 method for signature 'GPM'
cleanPredictors(x, nzv = TRUE, highcor = TRUE,
  cutoff = 0.9, rmvna = TRUE)

Arguments

x	An object of class gpm
nzv	Remove (near) zero variability predictor variables
highcor	Remove highly correlated predictor variables
cutoff	Correlation cutoff value for highcor
rmvna	Remove predictors with na values

Details

The function is realized by using the caret::nearZeroVar and caret::findCorrelation functions.

Value

A layer within the gpm object with the information on the n individual resamplings.

References

The function uses functions from: Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang and Can Candan. (2016). caret: Classification and Regression Training. https://CRAN.R-project.org/package=caret

See Also

NONE

Examples

## Not run: 
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environmentalinformatics-marburg/gpm documentation built on July 11, 2020, 11:12 a.m.