assortment.discrete {assortnet} | R Documentation |

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

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

`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. |

`na.rm` |
Remove all nodes which have NA as type from both the network and the types object. If this is False and NAs are present, an error message will be displayed. |

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

Damien Farine dfarine@orn.mpg.de

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.

```
# 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 0.20 Index]