compVarImp: Compute variable importance over all model responses and...

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

Description

Compute mean variable importance over all model responses and resamplings.

Usage

compVarImp(models, scale = FALSE)

Arguments

models	The trained model for each response variable and all resamplings resulting from trainModel
scale	Scale variable importance over all resamplings or use individual values (TRUE/FALSE)

Details

The variable importance is extracted from the model training dataset based on functions supplied by the caret package.

Value

A data frame containing the variable importance over for each response variable and all 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

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environmentalinformatics-marburg/gpm documentation built on July 11, 2020, 11:12 a.m.