MTD.No.Smooth.Test: Calculate coupling value with Multiplication of Temporal...

In LeiGuo0812/quickNet: For quick network construction

Calculate coupling value with Multiplication of Temporal Derivatives method.

Description

Calculate coupling value with Multiplication of Temporal Derivatives method.

Usage

MTD.No.Smooth.Test(data, nperm = 5000)

Arguments

data	A matrix or a data frame. The rows should be observations or time points. The number of columns should be two.
nperm	The number of permutations to test the significant of coupling.

Details

This function apply the method of Multiplication of Temporal Derivatives introduced by Shine, et al (2015). But note that this function doesn't apply time series smooth.

Value

A list.

• coupling: The coupling of the two columns in each observation.

• coupling_mean: The mean coupling value of all the observations, representing the overall coupling between the two columns.

• p.value: The p value of the coupling value from permutation test.

References

Shine, J. M., Koyejo, O., Bell, P. T., Gorgolewski, K. J., Gilat, M., & Poldrack, R. A. (2015). Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage, 122, 399–407. https://doi.org/10.1016/j.neuroimage.2015.07.064

Examples

MTD.No.Smooth.Test(mtcars[,1:2])

LeiGuo0812/quickNet documentation built on May 1, 2024, 10:42 p.m.