pca: Principal component analysis

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Principal component analysis

Description

Principal component analysis.

Usage

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

Arguments

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.

Details

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

Value

A list including:

values	The eigenvalues.
vectors	The eigenvectors.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

See Also

reg.mle.lda

Examples

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

Rfast2 documentation built on Aug. 8, 2023, 1:11 a.m.