pca_standardize_norm {booklet} | R Documentation |
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 inFactoMineR::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()
[Package booklet version 1.0.0 Index]