Efficient computation of a truncated principal components analysis of a
given data matrix using an implicitly restarted Lanczos method from the
irlba package.
sparse_prcomp_irlba(x, n = 3, retx = TRUE, center = TRUE, scale. = FALSE, ...)a numeric or complex matrix (or data frame) which provides the data for the principal components analysis.
integer number of principal component vectors to return, must be
less than min(dim(x)).
a logical value indicating whether the rotated variables should be returned.
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.
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.
The value of scale determines how column scaling is performed
(after centering). If scale is a numeric vector with length equal
to the number of columns of x, then each column of x is
divided by the corresponding value from scale. If scale is
TRUE then scaling is done by dividing the (centered) columns of
x by their standard deviations if center=TRUE, and the root
mean square otherwise. If scale is FALSE, no scaling is done.
See scale for more details.
additional arguments passed to irlba.
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 centered (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.
# NOT RUN {
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)
# }
# NOT RUN {
# }
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