nda (version 0.2.4)

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_model_type=4,
mod_mode=6,min_evalue=0,min_communality=0,com_communalities=0,use_rotation=FALSE,
rotation="oblimin",weight=NULL,seed=NULL)

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

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)

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 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)