nda (version 0.1.15)

ndr: 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_modell_type=4,
mod_mode=6,min_evalue=0,min_communality=0,com_communalities=0,use_rotation=FALSE,
rotation="oblimin")

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.

scores

Estimates of the factor scores are reported (if covar=FALSE).

n.obs

Number of observations specified or found.

fn

Factor name: NDA

Call

Callback function

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_modell_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.

Author

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

e-mail*: [e-mail]

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.

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

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

# Data reduction
# 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)