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

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

Parallelization of Distance Correlation for ROI Time Series

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)

AlexChristensen/NetworkToolbox documentation built on March 6, 2023, 5:08 p.m.