# assortment.continuous: Assortment on continuous vertex values In assortnet: Calculate the Assortativity Coefficient of Weighted and Binary Networks

## Description

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

## Usage

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

## 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.

## Value

This function returns a named list, with two elements:

\$r the assortativity coefficient \$SE the standard error

## Author(s)

Damien Farine [email protected]

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ``` # 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 May 29, 2017, 5:58 p.m.