Description Usage Arguments Value References Examples

View source: R/calcTopologicalIndices.r

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

1 | ```
calc_swness_zscore(g, nullDist, sLevel = 0.01, ncores = 0, weights = NA)
``` |

`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. |

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 |

`zCC,zCP,zMO` |
Z-scores of Clustering,PathLength and Modularity with respect to a random Erdos-Renyi null model |

`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.

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

1 2 3 4 5 | ```
## Not run:
nullg <- generateERbasal(netData[[1]],10)
calc_swness_zscore(netData[[1]],nullg)
## End(Not run)
``` |

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