pca_eigen: Compute eigenvalues and eigenvectors

In booklet: Multivariate Exploratory Data Analysis

Compute eigenvalues and eigenvectors

Description

Return eigenvalues and eigenvectors of a matrix

Usage

pca_eigen(X)

pca_weighted_eigen(
  X,
  weighted_row = rep(1, nrow(X))/nrow(X),
  weighted_col = rep(1, ncol(X))
)

Arguments

X	X_active
weighted_row	row weights
weighted_col	column weights

Details

Standardization depends on what you need to perform factor analysis. We implemented two types:

• pca_weighted_eigen: This is the default method in FactoMineR to compute eigvalues, eigvectors and U matrix.

• pca_eigen: This is the standard method to compute eigenvalues, eigenvectors.

Value

A list containing results of Single Value Decomposition (SVD).

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_eigen()

booklet documentation built on June 8, 2025, 11:40 a.m.