Package 'hetcorFS'

Employee Satisfaction Index (ESI) Data Set

Description

The Employee Satisfaction Index (ESI) data set, from Kaggle (Harris, 2023), is a fictional data set that measures employee satisfaction

Usage

data(ESI)

Format

A data frame with 500 rows and 10 features.

emp_id

label.

age

continuous from 23 to 45.

Dept

categorical.

location

binary.

education

binary.

recruitment_type

categorical.

job_level

ordinal from 1 to 5.

rating

ordinal from 1 to 5.

onsite

binary.

awards

number of awards 0-9.

certifications

binary.

salary

continuous from 24.1 to 86.8.

satisfied

binary.

Source

Harris, M. (2023). Employee Satisfaction Index Dataset. Evanston, Illinois: Kaggle. Version 1

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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

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Heterogeneous correlation and p-value matrices

Description

Extends the traditional correlation matrix (between numerical data) to also include binary and ordinal categorical data and computes the p-values for the tests of uncorrelation.

Usage

HCPM(data = NULL)

Arguments

data	A data frame. Values of type 'numeric' or 'integer' are treated as numerical.

Value

A list with with elements:

cor_mat	An $p$ by $p$ heterogeneous correlation matrix
p_value	An $p$ by $p$ heterogeneous p-values matrix

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
HCPM(data)

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Jaccard Rate

Description

Computes the Jaccard index using Gower's dissimilarity.

Usage

JaccardRate(
  data, 
  data_red,
  k=6
)

Arguments

data	A data frame. Values of type 'numeric' or 'integer' are treated as numerical.
data_red	A data frame. A subset of data with the selected features.
k	number of neighbors

Value

Jaccard Index

numeric

References

Zhao, Z., L. Wang, and H. Liu (2010). Efficient spectral feature selection with minimum redundancy. In Proceedings of the AAAI conference on artificial intelligence, Volume 24, pp. 673–678.

Examples

data(ESI)
data=ESI[,-c(1,3,4,6,9)] ##removing categorical features
out=UFS(data,alpha=0.01,method='c',pv_adj='BH')
JR=JaccardRate(data,out$selected.features)
JR #visualize the index

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Redundancy Rate

Description

Computes the Redundancy Rate using heterogeneous correlation matrix.

Usage

RedRate(
  data_red
)

Arguments

data_red

A data frame. A subset of data with the selected features.

Value

Redundancy Rate

numeric

References

Zhao, Z., L. Wang, and H. Liu (2010). Efficient spectral feature selection with minimum redundancy. In Proceedings of the AAAI conference on artificial intelligence, Volume 24, pp. 673–678.

Examples

data(ESI)
data=ESI[,-c(1,3,4,6,9)] ##removing categorical features
out=UFS(data,alpha=0.01,method='c',pv_adj='BH')
RR=RedRate(out$selected.features)
RR #visualize the index

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Unsupervised Feature Selection

Description

Performs unsupervised feature selection for mixed type data. Both algorithms are based on the heterogeneous correlation matrix.

Usage

UFS(
  data = NULL,
  alpha = 0.05,
  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, factors as ordinal categorical.
alpha	Significance level to be used for testing, default = 0.05.
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

An list of elements:

rearranged.data.set	Original data frame with with numerical features first
selected.features	A data frame of the selected features
feature.indices	The indices of the selected features from the original data frame
original.corr.matrix	The $p$ by $p$ extended correlation matrix of all the inputted features
corr.matrix	The $d$ by $d$ extended correlation matrix of the selected features
original.p.value.matrix	The $p$ by $p$ p-values matrix of all the inputted features
p.value.matrix	The $d$ by $d$ p-values matrix of the selected 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)#Loading the data
data = ESI[,-c(1,3,4,6,9)]##removing categorical features
res = UFS(data)

### visualize selected features
colnames(res$selected.features)

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