10.5061/DRYAD.9W0VT4BCN
Silk, Matthew
0000-0002-8318-5383
University of Exeter
Evans, Julian
0000-0001-6810-199X
University of Zurich
Fisher, David
0000-0002-4444-4450
University of Aberdeen
The performance of permutations and exponential random graph models when
analysing animal networks (R code and data)
Dryad
dataset
2020
2020-08-18T00:00:00Z
2020-08-18T00:00:00Z
en
136160 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
Here we provide: a) The R code for the simulations used in the Beahvioral
Ecology paper The performance of permutations and exponential random graph
models when analysing animal networks 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.
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)