plot.nda: Plot function for Generalized Network-based Dimensionality...

In nda: Generalized Network-Based Dimensionality Reduction and Analysis

Plot function for Generalized Network-based Dimensionality Reduction and Analysis (GNDA)

Description

Plot variable network graph

Usage

## S3 method for class 'nda'
plot(x, cuts=0.3, interactive=TRUE,edgescale=1.0,labeldist=-1.5,show_weights=FALSE,...)

Arguments

x	an object of class 'NDA'.
cuts	minimal square correlation value for an edge in the correlation network graph (default 0.3).
interactive	Plot interactive visNetwork graph or non-interactive igraph plot (default TRUE).
edgescale	Proportion scale value of edge width.
labeldist	Vertex label distance in non-interactive igraph plot (default value =-1.5).
show_weights	Show edge weights (default FALSE)).
...	other graphical parameters.

Author(s)

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

e-mail*: [e-mail]

References

Kosztyán, Z. T., Katona, A. I., Kurbucz, M. T., & Lantos, Z. (2024). Generalized network-based dimensionality analysis. Expert Systems with Applications, 238, 121779. <URL: https://doi.org/10.1016/j.eswa.2023.121779>.

See Also

biplot, summary, ndr.

Examples

# Plot function with feature selection

data("CrimesUSA1990.X")
df<-CrimesUSA1990.X
p<-ndr(df)
biplot(p,main="Biplot of CrimesUSA1990 without feature selection")

# Plot function with feature selection
# minimal eigen values (min_evalue) is 0.0065
# minimal communality value (min_communality) is 0.1
# minimal common communality value (com_communalities) is 0.1

p<-ndr(df,min_evalue = 0.0065,min_communality = 0.1,com_communalities = 0.1)

# Plot with default (cuts=0.3)
plot(p)

# Plot with higher cuts
plot(p,cuts=0.6)

# GNDA is used for clustering, where the similarity function is the 1-Euclidean distance
# Data is the swiss data

SIM<-1-normalize(as.matrix(dist(swiss)))
q<-ndr(SIM,covar = TRUE)
plot(q,interactive = FALSE)

nda documentation built on Oct. 14, 2024, 5:10 p.m.