hypervolume_variable_importance: Hypervolume variable importance

View source: R/hypervolume_variable_importance.R

hypervolume_variable_importanceR Documentation

Hypervolume variable importance

Description

Assesses the contribution of each variable to the total hypervolume as a rough metric of variable importance.

Usage

hypervolume_variable_importance(hv, verbose = TRUE)

Arguments

hv

A hypervolume for which the importance of each variable should be calculated.

verbose

If TRUE, prints diagnostic progress messages.

Details

The algorithm proceeds by comparing the n-dimensional input hypervolume's volume to all possible n-1 dimensional hypervolumes where each variable of interest has been deleted. The importance score reported is the ratio of the n-dimensional hypervolume relative to each of the n-1 dimensional hypervolumes. Larger values indicate that a variable makes a proportionally higher contribution to the overall volume.

The algorithm can only be used on Hypervolumes that have a Data and Method value, because the variable deletion process is not well defined for objects that are not associated with a particular set of observations and construction method.

Value

A named vector with importance scores for each axis. Note that these scores are not dimensionless but rather have units corresponding to the original units of each variable.

Examples

# low parameter values for speed
data(penguins,package='palmerpenguins')
penguins_no_na = as.data.frame(na.omit(penguins))
penguins_adelie = penguins_no_na[penguins_no_na$species=="Adelie",
                    c("bill_length_mm","bill_depth_mm","flipper_length_mm")]

hv = hypervolume_box(penguins_adelie,name='Adelie')

varimp = hypervolume_variable_importance(hv,verbose=FALSE)
barplot(varimp,ylab='Importance',xlab='Variable')

hypervolume documentation built on May 17, 2022, 1:06 a.m.