Assesses the contribution of each variable to the total hypervolume as a rough metric of variable importance.
hypervolume_variable_importance(hv, verbose = TRUE)
A hypervolume for which the importance of each variable should be calculated.
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
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.
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.
# 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')
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