PCA {templateICAr}

PCA

Description

Efficient PCA for a tall matrix (many more rows than columns). Uses the SVD of the covariance matrix.

Usage

PCA(X, center = TRUE, Q = NULL, Q_max = 100, nV = 0)

Arguments

X

V \times T fMRI timeseries data matrix, centered by columns.

center

Center the columns of X? Default: TRUE. Set to FALSE if already centered.

Q

Number of latent dimensions to estimate. If NULL (default), estimated using PESEL (Sobczyka et al. 2020).

Q_max

Maximal number of principal components for automatic dimensionality selection with PESEL. Default: 100.

nV

Number of principal directions to obtain. Default: 0. Can also be "Q" to set equal to the value of Q. Note that setting this value less than Q does not speed up computation time, but does save on memory. Note that the directions will be with respect to X, not its covariance matrix.

Value

The SVD decomposition