ndr: Genearlized Network-based Dimensionality Reduction and...

In kzst/nda: Generalized Network-Based Dimensionality Reduction and Analysis

Genearlized Network-based Dimensionality Reduction and Analysis (GNDA)

Description

The main function of Generalized Network-based Dimensionality Reduction and Analysis (GNDA).

Usage

ndr(r,covar=FALSE,cor_method=1,cor_type=1,min_R=0,min_comm=2,Gamma=1,null_model_type=4,
mod_mode=6,min_evalue=0,min_communality=0,com_communalities=0,use_rotation=FALSE,
rotation="oblimin",weight=NULL,seed=NULL)

Arguments

r	A numeric data frame
covar	If this value is FALSE (default), it finds the correlation matrix from the raw data. If this value is TRUE, it uses the matrix r as a correlation/similarity matrix.
cor_method	Correlation method (optional). '1' Pearson's correlation (default), '2' Spearman's correlation, '3' Kendall's correlation, '4' Distance correlation
cor_type	Correlation type (optional). '1' Bivariate correlation (default), '2' partial correlation, '3' semi-partial correlation
min_R	Minimal square correlation between indicators (default: 0).
min_comm	Minimal number of indicators per community (default: 2).
Gamma	Gamma parameter in multiresolution null modell (default: 1).
null_model_type	'1' Differential Newmann-Grivan's null model, '2' The null model is the mean of square correlations between indicators, '3' The null model is the specified minimal square correlation, '4' Newmann-Grivan's modell (default)
mod_mode	Community-based modularity calculation mode: '1' Louvain modularity, '2' Fast-greedy modularity, '3' Leading Eigen modularity, '4' Infomap modularity, '5' Walktrap modularity, '6' Leiden modularity (default)
min_evalue	Minimal eigenvector centrality value (default: 0)
min_communality	Minimal communality value of indicators (default: 0)
com_communalities	Minimal common communalities (default: 0)
use_rotation	FALSE no rotation (default), TRUE the rotation is used.
rotation	"none", "varimax", "quartimax", "promax", "oblimin", "simplimax", and "cluster" are possible rotations/transformations of the solution. "oblimin" is the default, if use_rotation is TRUE.
weight	The weights of columns. The defalt is NULL (no weights).
seed	default seed value (default=NULL, no seed)

Details

NDA both works on low and high simple size datasets. If min_evalue=min_communality=com_communalities=0 than there is no feature selection.

Value

communality	Communality estimates for each item. These are merely the sum of squared factor loadings for that item. It can be interpreted in correlation matrices.
loadings	A standard loading matrix of class “loadings".
uniqueness	Uniqueness values of indicators.
factors	Number of found factors.
EVCs	The list eigenvector centrality value of indicators.
membership	The membership value of indicators.
weight	The weight of indicators.
scores	Estimates of the factor scores are reported (if covar=FALSE).
centers	Colum mean of unstandardized score values.
n.obs	Number of observations specified or found.
use_rotation	FALSE no rotation (default), TRUE the rotation is used.
rotation	"none", "varimax", "quartimax", "promax", "oblimin", "simplimax", and "cluster" are possible rotations/transformations of the solution. "oblimin" is the default, if use_rotation is TRUE.
fn	Factor name: NDA
seed	applied seed value (default=NULL, no seed)
Call	Callback function

Author(s)

Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona

e-mail*: [e-mail]

References

Kosztyan, Z. T., Kurbucz, M. T., & Katona, A. I. (2022). Network-based dimensionality reduction of high-dimensional, low-sample-size datasets. Knowledge-Based Systems, 109180. doi:10.1016/j.knosys.2022.109180

See Also

plot, biplot, summary.

Examples

# Dimension reduction without using any hyperparameters

data(swiss)
df<-swiss
p<-ndr(df)
summary(p)
plot(p)
biplot(p)

# Dimension reduction with using hyperparameters
# min_R=0.1 # The mininal square correlation must be grater than 0.1

p<-ndr(df,min_R = 0.1)
summary(p)
plot(p)

# min_evalue=0.1 # Minimal evector centalities must be greater than 0.1

p<-ndr(df,min_evalue = 0.1)
summary(p)
plot(p)

# minimal and common communality value must be greater than 0.25

p<-ndr(df,min_communality = 0.25,
 com_communalities = 0.25)

# Print factor matrix
cor(p$scores)
plot(p)

# Use factor rotation

p<-ndr(df,min_communality = 0.25,
 com_communalities = 0.25,use_rotation=TRUE)

# Print factor matrix
cor(p$scores)
biplot(p)

# Data reduction - clustering
# Distance is Euclidean's distance
# covar=TRUE means only the distance matrix is considered.

q<-ndr(1-normalize(as.matrix(dist(df))),covar=TRUE)
summary(q)
plot(q)

kzst/nda documentation built on Feb. 19, 2025, 3:33 p.m.