assortment.continuous: Assortment on continuous vertex values

In assortnet: Calculate the Assortativity Coefficient of Weighted and Binary Networks

Assortment on continuous vertex values

Description

Calculates the assortativity coefficient for weighted and unweighted graphs with numerical vertex values

Usage

assortment.continuous(graph, vertex_values, weighted = TRUE, 
			SE = FALSE, M = 1, na.rm = FALSE)

Arguments

graph	A Adjacency matrix, as an N x N matrix. Can be weighted or binary.
vertex_values	Values on which to calculate assortment, vector of N numbers
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.
na.rm	Remove all nodes which have NA as vertex_values from both the network and the vertex_values object. If this is False and NAs are present, an error message will be displayed.

Value

This function returns a named list, with two elements:

$r the assortativity coefficient $SE the standard error

Author(s)

Damien Farine [e-mail]

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 continues trait values
	traits <- rnorm(N)
	
	# Test for assortment as binary network
	assortment.continuous(network,traits,weighted=FALSE)
	
	# Test for assortment as weighted network
	assortment.continuous(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 continues trait values
	traits <- rnorm(N)
	
	# Test for assortment as binary network
	assortment.continuous(network,traits,weighted=FALSE)
	
	# Test for assortment as weighted network
	assortment.continuous(network,traits,weighted=TRUE)

assortnet documentation built on March 7, 2023, 6:51 p.m.