| dCor.parallel {NetworkToolbox} | R Documentation |
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
|
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 <alexpaulchristensen@gmail.com>
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)