irlba (version 2.1.2)

prcomp_irlba: Principal Components Analysis

Description

Efficient computation of a truncated principal components analysis of a given data matrix using an implicitly restarted Lanczos method from the irlba package.

Usage

prcomp_irlba(x, n = 3, retx = TRUE, center = TRUE, scale. = FALSE, ...)

Arguments

x

a numeric or complex matrix (or data frame) which provides the data for the principal components analysis.

n

integer number of principal component vectors to return, must be less than min(dim(x)).

retx

a logical value indicating whether the rotated variables should be returned.

center

a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a centering vector of length equal the number of columns of x can be supplied.

scale.

a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with S, but scaling is often advisable. Alternatively, a vector of length equal the number of columns of x can be supplied.

...

additional arguments passed to irlba.

Value

A list with class "prcomp" containing the following components:

• sdev the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix).
• rotation the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors).
• x if retx is TRUE the value of the rotated data (the centred (and scaled if requested) data multiplied by the rotation matrix) is returned. Hence, cov(x) is the diagonal matrix diag(sdev^2).
• center, scale the centering and scaling used, or FALSE.

See Also

prcomp

Examples

set.seed(1)
x  <- matrix(rnorm(200), nrow=20)
p1 <- prcomp_irlba(x, n=3)
summary(p1)

# Compare with
p2 <- prcomp(x, tol=0.7)
summary(p2)