Rfast2: pca – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

pca

Principal component analysis

Principal component analysis.

pca(x, center = TRUE, scale = TRUE, k = NULL, vectors = FALSE)

`x`	A numerical n \times p matrix with data where the rows are the observations and the columns are the variables.
`center`	Do you want your data centered? TRUE or FALSE.
`scale`	Do you want each of your variables scaled, i.e. to have unit variance? TRUE or FALSE.
`k`	If you want a specific number of eigenvalues and eigenvectors set it here, otherwise all eigenvalues (and eigenvectors if requested) will be returned.
`vectors`	Do you want the eigenvectors be returned? By dafault this is FALSE.

The function is a faster version of R's prcomp.

A list including:

`values`	The eigenvalues.
`vectors`	The eigenvectors.

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

x <- matrix( rnorm(1000 * 20 ), ncol = 20)
a <- pca(x)
x <- NULL

A Collection of Efficient and Extremely Fast R Functions II

v0.0.9

GPL (>= 2.0)

Authors

Manos Papadakis, Michail Tsagris, Stefanos Fafalios and Marios Dimitriadis.

Initial release

2021-03-21