summary_pca_centralities {CINNA}R Documentation

Summarize PCA result related to centrality measures

Description

This function summarizes the PCA result related to centrality measures.

Usage

summary_pca_centralities(x, scale.unit = TRUE, ncp = 5)

Arguments

x

A list containing the computed centrality values.

scale.unit

A boolean value indicating whether the data should be scaled to unit variance (default = TRUE).

ncp

The number of dimensions in the final results (default = 5).

Value

The result of the pca_centralities function, which includes the PCA analysis results such as eigenvalues, variance explained, and scores. The returned value is an object of class "PCA" from the FactoMineR package. It contains the following components:

eig

A numeric vector of eigenvalues, indicating the amount of variance explained by each principal component.

var

A numeric vector of proportions of variance explained by each principal component.

ind

A data frame of individual scores, where each row represents an individual and each column represents a principal component.

call

The function call used to create the PCA object.

call2

The call used to compute the PCA analysis.

call3

The call used to project the individuals.

svd

The singular value decomposition of the data matrix.

cor

The correlation matrix of the variables.

cos2

The squared cosines of the variables.

contrib

The contributions of the variables to the principal components.

proj

The projected coordinates of the individuals on the principal components.

quali.sup

The results of the supplementary qualitative variables analysis.

quali.sup.ind

The results of the supplementary individuals analysis.

quanti.sup

The results of the supplementary quantitative variables analysis.

quanti.sup.var

The results of the supplementary variables analysis.

Author(s)

Minoo Ashtiani, Mehdi Mirzaie, Mohieddin Jafari


[Package CINNA version 1.2.2 Index]