plot.nda function

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

Plot variable network graph

## 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.

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>.

Author(s)

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

e-mail*: kosztyan.zsolt@gtk.uni-pannon.hu

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)