assortment.continuous {assortnet} R Documentation

## 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 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 <- dyads[which(dyads$ID1 != dyads$ID2),]
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 <- dyads[which(dyads$ID1 < dyads$ID2),]
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)



[Package assortnet version 0.20 Index]