pca.centralities: Ranking centrality measure based on contributions

In jafarilab/CINNA: Deciphering Central Informative Nodes in Network Analysis

Ranking centrality measure based on contributions

Description

This function demonstrates ranks of centrality measures in order of information levels.

Usage

pca.centralities(x, scale.unit = TRUE, cut.off = 80, ncp = 5,
  graph = FALSE, axes = c(1, 2))

Arguments

x	a list containg the computed centrality values
scale.unit	a boolean constant, 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 final results (default=5)
graph	a boolean constant, whether the graph shoul be displayed
axes	a length 2 vector describing the number of components to plot(default=c(1,2))

Details

This function represents centralities in the ranking list based on variable contribution to make principal components. PCA is a method for drawing out important variables from a data set. It helps user to reduced the dimensions in high dimensional data. It is more common to use for more than 3 dimensional datasets.

Value

a plot illustrating significant centralities in the order of contribution

Author(s)

Minoo Ashtiani, Mohieddin Jafari

References

Husson, F., Lê, S., & Pagès, J. (2010). Exploratory Multivariate Analysis by Example using R. Chapman & Hall/CRC Computer Science & Data Analysis, 40(April), 240.

http://www.sthda.com/english/

See Also

PCA

jafarilab/CINNA documentation built on Aug. 19, 2023, 4:49 p.m.