pca_centralities function

PCA Centrality Measures

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

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))

Returns

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)