FS_barplot: Feature importance bar plot

In hetcorFS: Unsupervised Feature Selection using the Heterogeneous Correlation Matrix

Feature importance bar plot

Description

Displays retained features for different values of alpha in a bar plot.

Usage

FS_barplot(
  data = NULL,
  grid.alpha = seq(0.01, 0.99, by = 0.01),
  missing = FALSE,
  pv_adj = "none",
  smooth.tol = 10^-12,
  method = "c"
)

Arguments

data	A data frame. Values of type 'numeric' or 'integer' are treated as numerical.
grid.alpha	A vector of alpha values to be plotted, default = seq(0.01,0.99,by=0.01).
missing	Pairwise complete by default, set to TRUE for complete deletion.
pv_adj	Correction method for p-value, "none" by default. For options see p.adjust.
smooth.tol	Minimum acceptable eigenvalue for the smoothing, default 10^-12.
method	Algorithm used. c (cell-wise) by default, r (row-wise) as the alternative.

Value

Displays a bar plot depicting which features are selected at each value of alpha (multiplied by 100) and a list with elements:

survivors	Vector depicting how many alphas a variable is selected for
data_names	Vector depicting the corresponding names of the features

References

Tortora C., Madhvani S., Punzo A. (2025). Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix. International Statistical Review. https://doi.org/10.1111/insr.70016

Examples

 
data(ESI)
data=ESI[,-c(1,3,4,6,9)]##removing categorical features
FS_barplot(data, pv_adj='BH') #using BH adkustment for the p-values

hetcorFS documentation built on Nov. 24, 2025, 5:07 p.m.