fusedPCA: Fused PCA (C++ core)

In neuroconductor/fMRIscrub: Scrubbing and Other Data Cleaning Routines for fMRI

Description

From: https://github.com/Lei-D/PCATF and https://github.com/glmgen/glmgen/blob/master/c_lib/glmgen/src/tf/tf_dp.c .

Usage

fusedPCA(
  X,
  X.svd = NULL,
  solve_directions = TRUE,
  K = NULL,
  lambda = 5,
  niter_max = 1000,
  TOL = 1e-08,
  verbose = FALSE
)

Arguments

X	A numerical data matrix (observations x variables).
X.svd	(Optional) The svd decomposition of X. Save time by providing this argument if the svd has already been computed. Default NULL.
solve_directions	Should the principal directions be solved for? These will be needed to display the artifact images for outlying observations.
K	(Optional) The number of fused PCs to solve for. If not provided, it will be set to the number of regular PCs with variance above the mean, up to 100 PCs.
lambda	The trend filtering parameter; roughly, the filtering intensity. Default is 5.
niter_max	The number of iterations to use for approximating the PC.
TOL	The maximum 2-norm between iterations to accept as convergence.
verbose	Print statements about convergence?

Details

Inheriting from glmgen, this code is under the GNU Lesser General Public License: http://www.gnu.org/licenses/ .

Value

SVD The fused SVD decomposition of X (list with u, d, v).

References

• Kim, S.-J., Koh, K., Boyd, S. & Gorinevsky, D. \$\ell_1\$ Trend Filtering. SIAM Rev. 51, 339-360 (2009).

• Pham, D., McDonald, D., Ding, L., Nebel, M. B. & Mejia, A. Projection scrubbing: a more effective, data-driven fMRI denoising method. (2021).

• Tibshirani, R. J. Adaptive piecewise polynomial estimation via trend filtering. The Annals of Statistics 42, 285-323 (2014).

neuroconductor/fMRIscrub documentation built on Dec. 22, 2021, 1:10 a.m.