disPairwise: Computation of the dissimilarity matrix (pairwise beta-diversity) for a set of networks

Description

Computation of the dissimilarity matrix for a set of networks. Each value of the matrix is the pairwise beta-diversity, computed using Hill numbers. It measures the dissimilarity in terms of groups, links, or probability of links respectively.

Usage

disPairwise(gList, groups=NULL, eta=1, 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.

type

The type of diversity used to measure dissimilarity. It can be groups diversity ('P'), links diversity ('L') or probability of links diversity ('Pi').

Value

Return a matrix whose elements are the pairwise dissimilarities.

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

Run this code

# 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)

# Dissimilarity matrix based on links beta-diversity
disPairwise(gList, groups, type = 'L') 
# }

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