R: Descriptive Statistics for Networks

network.descriptives {EGAnet}

R Documentation

Descriptive Statistics for Networks

Description

Computes descriptive statistics for network models

Usage

network.descriptives(network)

Arguments

network

Matrix, data frame, qgraph, or EGA object

Value

Numeric vector including:

`Mean_weight`	The average of the edge weights in the network
`SD_weight`	The standard deviation of the edge weights in the network
`Min_weight`	The minimum of the edge weights in the network
`Max_weight`	The minimum of the edge weights in the network
`Density`	The density of the network
`ASPL`	The average shortest path length (ASPL) of the network (computed as unweighted)
`CC`	The clustering coefficent (CC) of the network (computed as unweighted)
`swn.rand`	Small-worldness measure based on random networks: `swn.rand = (ASPL / ASPL_random) / (CC / CC_random)` `swn.rand` > 1 suggests the network is small-world
`swn.HG`	Small-worldness measure based on Humphries & Gurney (2008): `swn.HG = (transitivity / transitivity_random) / (ASPL / ASPL_random)` `swn.HG` > 1 suggests the network is small-world
`swn.TJHBL`	Small-worldness measure based on Telesford, Joyce, Hayasaka, Burdette, & Laurienti (2011): `swn.TJHBL = (ASPL_random / ASPL) - (CC / CC_lattice)` `swn.TJHBL` near 0 suggests the network is small-world, positive values suggest more random network characteristics, negative values suggest more lattice network characteristics
`scale-free_R-sq`	The R-squared fit of whether the degree distribution follows the power-law (many small degrees, few large degrees)

Author(s)

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>

References

# swn.HG
Humphries, M. D., & Gurney, K. (2008). Network 'small-world-ness': A quantitative method for determining canonical network equivalence. PLoS one, 3, e0002051

# swn.TJHBL
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011). The ubiquity of small-world networks. Brain Connectivity, 1(5), 367-375

# scale-free_R-sq
Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559

Examples

# Load data
wmt <- wmt2[,7:24]

# EGA example
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
ega.wmt <- EGA(data = wmt, plot.type = "qgraph")


# Compute descriptives
network.descriptives(ega.wmt)

[Package EGAnet version 1.1.0 Index]