pca_standardize_norm: Data standardization for PCA

In booklet: Multivariate Exploratory Data Analysis

Data standardization for PCA

Description

Perform data standardization for multivariate exploratory data analysis.

Usage

pca_standardize_norm(X, center = TRUE, scale = TRUE)

pca_standardize(X, scale = TRUE, weighted_row = rep(1, nrow(X))/nrow(X))

Arguments

X	matrix
center	centering by the mean
scale	scaling by the standard deviation
weighted_row	row weights

Details

Standardization depends on what you need to perform factor analysis. Two methods are implemented:

• standardize: standardization is performed by centering the data matrix and dividing by the square root of the sum of squares of the weights. This is the same method used in FactoMineR::PCA().

• standardize_norm: standardization is performed by centering and scaling the data matrix. (X - µ) / S, where µ is the mean and S is the standard deviation.

Value

A dataframe of the same size as X.

Examples

library(booklet)

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

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