econetwork (version 0.1)

divPartition: Partitionning network diversity in alpha, beta and gamma diversity

Description

This function computes alpha, beta and gamma diversity of a list of networks. It measures either group, links, or probability of links diversity.

Usage

divPartition(gList, groups, eta=1, framework=c('RLC','Chao'), type=c('P','L','Pi'))

Arguments

gList

A list of graph objects of class igraph.

groups

A named vector of class character indicating the group to which each node belongs to. The length of groups must correspond to the number of different nodes present in gList. The names names(groups) must correspond to the nodes names in gList. If NULL, the groups are the initial nodes.

eta

A positive number that controls the weight given to abundant groups/links. Default value is 1.

framework

The framework used to partitionate diversity, either Reeve Leinster Cobbold ('RLC') or Chao ('Chao')

type

The type of diversity to measure and partitionate. It can be groups diversity ('P'), link diversity ('L') or probability of link diversity ('Pi').

Value

Returns a list the following components:

mAlpha

The mean value of alpha-diversity accross all networks.

Alphas

A vector of numeric containing the local alpha-diversities (i.e. the alpha-diversity value for each network).

Beta

The value of the overall beta-diversity

Gamma

The value of the gamma-diversity

References

Marc Ohlmann, Vincent Miele, Stephane Dray, Loic Chalmandrier, Louise O'Connor & Wilfried Thuiller, Diversity indices for ecological networks: a unifying framework using Hill numbers. Ecology Letters (2019) <doi:10.1111/ele.13221>

Examples

# NOT RUN {
# Generating a set of Erdos-Renyi graphs and give name to nodes
library(igraph)
nbGraph = 10
gList = c()
n = 57 # number of nodes of each graph
C = 0.1  # connectance of each graph
for(i in 1:nbGraph){
  graphLocal = erdos.renyi.game(n, type='gnp', p.or.m =C, directed=TRUE)
  V(graphLocal)$name = as.character(1:57)
  gList = c(gList,list(graphLocal))
}

groups = c(rep("a",23),rep("b",34)) # vector that gives the group of each node
names(groups) = as.character(1:57)

# Measure of link diversity
divPartition(gList, groups, framework='Chao', type = 'L') 
# }