disPairwise {econetwork}

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.

Usage

disPairwise(gList, groups=NULL, eta=1,
            type=c('P','L','Pi'), abTable=NULL)

Arguments

gList

A list of graph objects of class igraph. The nodes must have a name.

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').

abTable

A matrix of size the number of nodes of the metanetwork times the number of networks. The rownames of this matrix must be the node names of metanetwork and the columns must be in an order corresponding to gList. The element (i,j) of this matrix is the abundance of species i in network j. Importantly, the non-nul elements in each column of abTalbe must correspond to the nodes present in each element of gList

Value

Return a dist object whose elements are the pairwise dissimilarities.

Author(s)

Authors: Stephane Dray, Vincent Miele, Marc Ohlmann, Wilfried Thuiller Maintainer: Wilfried Thuiller <[e-mail]>

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

# Generating a set of Erdos-Renyi graphs and give node names.
library(econetwork)
library(igraph)
nbGraph <- 3
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))
}

# vector that gives the group of each node
groups <- c(rep("a",23),rep("b",34))
names(groups) <- as.character(1:57)
#generating random (non-nul) abundances data
abTable <- sapply(1:nbGraph,function(x) rpois(n,1)+1)
rownames(abTable) = unlist(unique(lapply(gList,function(g) V(g)$name)))

# Dissimilarity matrices based on links beta-diversity
# at a node level
disPairwise(gList, type = 'L')
# at a node level while taking into account node abundances
disPairwise(gList, type = 'L', abTable = abTable)
# at a group level
disPairwise(gList, groups, type = 'L')
# at a group level while taking into account node abundances
disPairwise(gList, groups, type = 'L', abTable = abTable)