strength_multiple: Calculate signed strength statistics on a multiple graphs.

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

This calculates strength statistics on a list of graphs.

Usage

strength_multiple(
  graphs,
  which = "star",
  Scale = TRUE,
  col.names = NULL,
  row.names = NULL
)

Arguments

graphs	a list of networks in matrix format or as an igraph objects.
which	defaults to the weighted strength metric "star". Can also be "pos", "neg", or "all".
Scale	Should Strength* be scaled to the range -1 to 1 (assuming correlations are the edge weights)? Defaults to "TRUE"
col.names	The names of each column (node labels)
row.names	The names of each row (subject)

Details

Strength star is the weighted combination of the positive connections and negative connections in a network proposed by Rubinov and Sporns (2011). The statistic is calculated by the following formula:

s_i^{*}=s_i^+-≤ft(\frac{s_i^-}{s_i^++s_i^-}\right)s_i^-

where the positive and negative strength are respectively the sum of positive/negative weights:

s_i^\pm = ∑_{j\neq i}^N w_{ij}

When normalized to the -1 to 1 interval with scale=TRUE the positive and negative strength are normalized with the following formula first then plugged into the above formula:

s_i^\pm = \frac1{n-1}s_i^\pm

Value

A matrix of the statistic for the chosen strength measure, or a list of matrices if which="all"

Author(s)

Brandon Vaughan

References

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Node Degree and Strength. Chapter 4. Fundamentals of Brain Network Analysis, 115-136. doi:10.1016/B978-0-12-407908-3.00004-2

Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56(4), 2068-2079. doi:10.1016/j.neuroimage.2011.03.069

See Also

strength_signed degree_centr strength degree

Examples

strength = strength_multiple(graphs, which="star")

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.