PCA: PCA

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

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

templateICAr documentation built on Feb. 16, 2023, 8:14 p.m.