plot.nda {nda} | R Documentation |
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*: kosztyan.zsolt@gtk.uni-pannon.hu
References
KosztyƔn, 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
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)
[Package nda version 0.1.15 Index]