Community Network Analysis
Naia Morueta-Holme
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1 Setup
library(netassoc)1.1 Generate some random ‘observed’ data
set.seed(1)
# Number of m species
nsp <- 10
# Number of n sites
nsi <- 50
# Observed m x n community matrix (abundance or presence/absence)
m_obs <- floor(matrix(rpois(nsp*nsi,lambda=5),ncol=nsi,nrow=nsp))
# "Force" some species associations to the observed community matrix
m_obs[1,1:(nsi/2)] <- rpois(n=nsi/2,lambda=20)
m_obs[2,1:(nsi/2)] <- rpois(n=nsi/2,lambda=20)1.2 What is the null expectation?
# Null expected m x n community matrix (abundance or presence/absence)
m_nul <- floor(matrix(rpois(nsp*nsi,lambda=5),ncol=nsi,nrow=nsp))Note that here we are simply resampling the observed data preserving row and column sums, which is NOT recommended. Instead, we should use our expected null model of community assembly.
1.3 Infer the species association network
# What species co-occurrence patterns are more or less likely than expected under the null model?
n <- make_netassoc_network(m_obs, m_nul,
method="partial_correlation",
args=list(method="shrinkage"), # for alternative estimators see ?partial_correlation
p.method='fdr',
numnulls=100,
plot=TRUE,
alpha=0.05)
## Calculating observed co-occurrence scores...
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## Calculating standardized effect sizes...
## Adjusting p-values for multiple comparisons...
## Building network...
n$network_all## IGRAPH DNW- 10 2 --
## + attr: name (v/c), weight (e/n)
## + edges (vertex names):
## [1] Species 1->Species 2 Species 2->Species 1