compress_mts_pca: Core function for computing multivariate time series...

In UNCDEPENdLab/fmri.pipeline: Analysis of fMRI Data on HPC clusters

Core function for computing multivariate time series compression scores by principal components

Description

Core function for computing multivariate time series compression scores by principal components

Usage

compress_mts_pca(mts, pexp_target = 0.9, scale_columns = TRUE)

Arguments

mts	multivariate time series structured time x signals
pexp_target	Proportion of variance explained by principal components. This can be a vector, in which case compression is calculated at different thresholds.
scale_columns	whether to z-score the time series prior to eigendecomposition (recommended)

Details

This function accepts a time x signals (e.g., voxels) time series matrix. It computes the singular value decomposition (SVD) and then examines how many eigenvectors are needed to explain at least pexp_target proportion of variance.

Compression scores are normalized 0 – 1.0 by the equation: 1 - (n_components / n_timeseries). Thus, if 6 components explain 91

Given that the number of eigenvectors is an integer, a linear approximation to the exact proportion of variance explained is also calculated. For example, if 3 components explain 84 92 get us to precisely 90

Value

a list containing compression estimates of the matrix. For each pexp_target value, two values are included, one representing the compression calculated using integer

Author(s)

Michael Hallquist

UNCDEPENdLab/fmri.pipeline documentation built on April 3, 2025, 3:21 p.m.