dCor.parallel: Parallelization of Distance Correlation for ROI Time Series

In NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Description

Parallelizes the dCor function for faster computation times

Usage

dCor.parallel(neurallist, cores)

Arguments

neurallist	List of lists. A list containing the time series list from all participants imported from the convertConnBrainMat function
cores	Number of computer processing cores to use when performing covariate analyses. Defaults to n - 1 total number of cores. Set to any number between 1 and maximum amount of cores on your computer

Value

Returns a m x m x n array corresponding to distance correlations between ROIs (m x m matrix) for n participants

Author(s)

Alexander Christensen <[e-mail]>

References

Yoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019). Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage, 197, 212-223.

Examples

## Not run: 
# Import time series data 
for(i in 1:5)

# Run distance correlation
dCor.parallel(mat.list, cores = 2)


## End(Not run)

NetworkToolbox documentation built on May 28, 2021, 5:11 p.m.