assortment.discrete {assortnet}R Documentation

Assortment on discrete vertex values

Description

Calculates the assortativity coefficient for weighted and unweighted graphs with nominal/categorical vertex values

Usage

assortment.discrete(graph, types, weighted = TRUE, SE = FALSE, M = 1)

Arguments

graph

Adjacency matrix, as an N x N matrix. Can be weighted or binary.

types

Values on which to calculate assortment, vector of N labels

weighted

Flag: TRUE to use weighted edges, FALSE to turn edges into binary (even if weights are given)

SE

Calculate standard error using the Jackknife method.

M

Binning value for Jackknife, where M edges are removed rather than single edges. This helps speed up the estimate for large networks with many edges.

Value

This function returns a named list, with three elements:

$r the assortativity coefficient $SE the standard error $mixing_matrix the mixing matrix with the distribution of edges or edge weights by category

Author(s)

Damien Farine dfarine@orn.mpg.de

References

Newman (2003) Mixing patterns in networks. Physical Review E (67) Farine, D.R. (2014) Measuring phenotypic assortment in animal social networks: weighted associations are more robust than binary edges. Animal Behaviour 89: 141-153.

Examples

	# DIRECTED NETWORK EXAMPLE
	# Create a random directed network
	N <- 20
	dyads <- expand.grid(ID1=1:20,ID2=1:20)
	dyads <- dyads[which(dyads$ID1 != dyads$ID2),]
	weights <- rbeta(nrow(dyads),1,15)
	network <- matrix(0, nrow=N, ncol=N)
	network[cbind(dyads$ID1,dyads$ID2)] <- weights

	# Create random discrete trait values
	traits <- rpois(N,2)
	
	# Test for assortment as binary network
	assortment.discrete(network,traits,weighted=FALSE)
	
	# Test for assortment as weighted network
	assortment.discrete(network,traits,weighted=TRUE)
	
	
	
	# UNDIRECTED NETWORK EXAMPLE
	# Create a random undirected network
	N <- 20
	dyads <- expand.grid(ID1=1:20,ID2=1:20)
	dyads <- dyads[which(dyads$ID1 < dyads$ID2),]
	weights <- rbeta(nrow(dyads),1,15)
	network <- matrix(0, nrow=N, ncol=N)
	network[cbind(dyads$ID1,dyads$ID2)] <- weights
	network[cbind(dyads$ID2,dyads$ID1)] <- weights
	
	# Create random discrete trait values
	traits <- rpois(N,2)
	
	# Test for assortment as binary network
	assortment.discrete(network,traits,weighted=FALSE)
	
	# Test for assortment as weighted network
	assortment.discrete(network,traits,weighted=TRUE)


[Package assortnet version 0.12 Index]