DOI: 10.5061/dryad.9w0vt4bcn
title: The performance of permutations and exponential random graph models when analysing animal networks (R code and data)

description: Social network analysis is a suite of approaches for exploring relational
 data. Two approaches commonly used to analyse animal social network data
 are permutation-based tests of significance and exponential random graph
 models. However, the performance of these approaches when analysing
 different types of network data has not been simultaneously evaluated.
 Here we test both approaches to determine their performance when analysing
 a range of biologically realistic simulated animal social networks. We
 examined the false positive and false negative error rate of an effect of
 a two-level explanatory variable (e.g. sex) on the number and combined
 strength of an individual’s network connections. We measured error rates
 for two types of simulated data collection methods in a range of network
 structures, and with/without a confounding effect and missing
 observations. Both methods performed consistently well in networks of
 dyadic interactions, and worse on networks constructed using observations
 of individuals in groups. Exponential random graph models had a marginally
 lower rate of false positives than permutations in most cases. Phenotypic
 assortativity had a large influence on the false positive rate, and a
 smaller effect on the false negative rate for both methods in all network
 types. Aspects of within- and between-group network structure influenced
 error rates, but not to the same extent. In grouping-event based networks,
 increased sampling effort marginally decreased rates of false negatives,
 but increased rates of false positives for both analysis methods. These
 results provide guidelines for biologists analysing and interpreting their
 own network data using these methods.
description: Here we provide: a) The R code for the
 simulations used in the Beahvioral Ecology paper <em>The performance
 of permutations and exponential random graph models when analysing animal
 networks </em>alongside a csv used to provide parameters for network
 generation b) A summary dataset produced by our run of
 the simulations c) The necessary code to produce the
 network plot and all results plots used in the paper
 The R code provided for the simulations are the full set of
 functions used to generate and analyse the networks as described in the
 paper. We do not provide the wrapper code we used to run these functions
 on a specific high performance computing cluster based at the University
 of Exeter Cornwall Campus. We also provide the .csv file that provided
 parameter information to generate networks The summary
 dataset provided contains (summarised) output from our simulation run used
 in the paper. It is sufficient to reproduce the plots used in the results
 section of the paper. We also provide the R code used
 to generate these plots, and also to generate plots of networks generated
 using the simulation functions provided.
description: Simulation R code is provided in a format where it can be used
 flexibly as desired by a researcher. Use in a HPC environment will require
 use of wrapper scripts to run the functions multiple times with different
 parameter sets. The plotting code will run with the
 input data files provided (network plotting requires the parameter set csv
 and result plotting requires the summarised data csv)