Calculation Small-world-ness z-scores

The function calculates small-world-ness z-scores and 99\ using as null model the list of networks in the nullDist parameter.

Usage

calc_swness_zscore(g, nullDist, sLevel = 0.01, ncores = 0, weights = NA)

Arguments

g

igraph object

nullDist

list of igraph object with the null model simulations

sLevel

significance level to calculate CI (two tails)

ncores

number of cores to use paralell computation, if 0 sequential processing is used.

Value

a list with two data frames: one with indices z-scores and CI

Clustering

Clustering coefficient, measures the average fraction of pairs of neighbors of a node that are also neighbors of each other

PathLength

Mean of the shortest paths between all pair of vertices

Modularity

modularity measures how separated are different groups from each other, the algorithm cluster_spinglass was used to obtain the groups

zCC,zCP,zMO

Z-scores of Clustering,PathLength and Modularity with respect to nullDist models

CClow,CChigh,CPlow,CPhigh,MOlow,MOhigh

sLevel confidence intervals

SWness,SWnessCI

Small-world-ness and it CI value

isSW,isSWness

Logical variable signalling if the network is Small-world by the method of Marina 2018 or the method of Humprhies & Gurney 2008

Another data.frame with the values calculated for the nullDist.

References

Marina, T. I., Saravia, L. A., Cordone, G., Salinas, V., Doyle, S. R., & Momo, F. R. (2018). Architecture of marine food webs: To be or not be a ‘small-world.’ PLoS ONE, 13(5), 1–13. https://doi.org/10.1371/journal.pone.0198217

Examples

if (FALSE) { # \dontrun{
nullg <- generateERbasal(netData[[1]],10)
calc_swness_zscore(netData[[1]],nullg)
} # }