density.gradientForest: Gaussian kernel density estimates

Description Usage Arguments Value Author(s) References See Also Examples

Description

Extracts Gaussian kernel density estimates for a specified physical variable.

Usage

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## S3 method for class 'gradientForest'
density(x, predictor, ...)

## S3 method for class 'combinedGradientForest'
density(x, predictor, gridded=F, gf.name, ...)

Arguments

x

an object of class gradientForest generated by gradientForest or an object of an object of class combinedGradientForest generated by combinedGradientForest.

predictor

name of predictor variable.

gridded

if TRUE, supply density on the predictor grid that was used to create the combinedGradientForest object. This ensures that densities for different gradientForest objects, or the combined density, are aligned with the same predictor values. Default FALSE.

gf.name

name of the gradientForest object for which density is required. If missing, the combined density is supplied.

...

further arguments passed to or from other methods.

Value

Provides Gaussian kernel density estimates for a specified physical variable.

Author(s)

N. Ellis, CSIRO, Cleveland, Australia. <[email protected]>

References

Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.

See Also

gradientForest, ~~~

Examples

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data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
f1 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs, ntree=10)
density(f1,preds[1])

f2 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
f12 <- combinedGradientForest(west=f1,east=f2)
density(f12,preds[1])

slarge/gradientForest documentation built on May 3, 2019, 4:05 p.m.