hypervolume_variable_importance: Hypervolume variable importance

In hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

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 29, 2024, 8:19 a.m.