pca_centralities: PCA Centrality Measures

In CINNA: Deciphering Central Informative Nodes in Network Analysis

PCA Centrality Measures

Description

This function performs Principal Component Analysis (PCA) on centrality measures. It computes the contributions of variables to the principal components and visualizes them.

Usage

pca_centralities(
  x,
  scale.unit = TRUE,
  cut.off = 80,
  ncp = 2,
  graph = FALSE,
  axes = c(1, 2)
)

Arguments

x	a list containing the computed centrality values
scale.unit	a boolean constant indicating whether data should be scaled to unit variance (default = TRUE)
cut.off	The intensity that must be exceeded in cumulative percentage of variance of eigen values (default = 80)
ncp	number of dimensions in the final results (default = 5)
graph	a boolean constant indicating whether the graph should be displayed (default = FALSE)
axes	a length 2 vector describing the number of components to plot (default = c(1, 2))

Value

A plot illustrating the contributions of variables to the principal components. The x-axis represents the centrality measures, and the y-axis represents the contribution. The higher the contribution value, the more important the centrality measure is in the ranking. The plot helps in identifying the most influential centrality measures.

Examples

# Create a data frame with multiple observations
centralities <- data.frame(
  Betweenness = c(0.2, 0.3, 0.5),
 Closeness = c(0.4, 0.2, 0.6),
  Degree = c(0.3, 0.1, 0.4),
  Eigenvector = c(0.1, 0.5, 0.2)
)
pca_centralities(centralities)

CINNA documentation built on Aug. 8, 2023, 5:13 p.m.