This function demonstrates ranks of centrality measures in order of information levels.
pca_centralities(x, scale.unit = TRUE, cut.off = 80, ncp = 5, graph = FALSE, axes = c(1, 2))
a list containg the computed centrality values
a boolean constant, whether data should be scaled to unit variance(default=TRUE)
The intensity that must be exceeded in cumulative percentage of variance of eigen values.(default=80)
number of dimensions in final results (default=5)
a boolean constant, whether the graph shoul be displayed
a length 2 vector describing the number of components to plot(default=c(1,2))
a plot illustrating significant centralities in the order of contribution
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.
Husson, F., Lê, S., & Pagès, J. (2010). Exploratory Multivariate Analysis by Example using R. Chapman & Hall/CRC Computer Science & Data Analysis, 40(April), 240.