make_netassoc_network {netassoc} | R Documentation |
Infer species-association network
Description
Infers a species association network by determining which co-occurrence patterns between species are more or less likely than expected under a null model of community assembly. Defaults to estimation of association using a robust shrinkage estimator for inverse covariance matrices.
Usage
make_netassoc_network(obs, nul=vegan::permatfull(obs)$perm[[1]],
method="partial_correlation", args=list(method="shrinkage",verbose=FALSE),
p.method="fdr", alpha=0.05, numnulls=1000,
plot=TRUE,plot.legend=TRUE, plot.title=TRUE, verbose=TRUE)
Arguments
obs |
A m x n community matrix describing the abundance or presence/absence of m species at n sites. Represents the observed data. |
nul |
A m x n community matrix describing the abundance or presence/absence of m species at n sites. Represents the regional null expectation data. The default value is a resampling of the observed data that preserves row and column sums, but this default method is not recommended. |
method |
The name of a function used to calculate relationships between species. The function must accept at least the arguments |
args |
A list of additional arguments to be passed to the |
p.method |
The method used to correct p-values for multiple comparisons. See |
alpha |
Analysis-wide Type I error rate, controlled via the argument |
numnulls |
Number of resamples of the |
plot |
If |
plot.title |
If |
plot.legend |
If |
verbose |
If |
Details
Steps taken are:
1) obtaining input data and trimming to eliminate species that do not occur in any site 2) resampling a set of null community matrices from the expectation with the same richness and abundance as the observed community 3) calculating species co-occurrence scores for each pair of species within the observed matrix and all resampled null matrices 4) calculating standardized effect sizes and p-values for species' co-occurrence scores 5) thresholding effect sizes to retain only significant associations 6) converting matrix of scores to association network
The resulting network can be analyzed using functions from the igraph
network package.
The user should specify a nul
matrix of the same dimensionality as obs
based on some regional distribution modeling approach (e.g. MaxEnt). The default reshuffling method is not recommended but provided to allow immediate output from the function.
This process by default builds a Gaussian graphical model via estimating an inverse covariance matrix (precision matrix, which can be used to calculate partial correlation coefficients) for all species pairs. This graph is then compared to a distribution of null graphs, such that the final output is a graph with edge weights corresponding to standardized effect sizes after correction for multiple comparisons.
A range of different methods are provided in partial_correlation
for estimating relationships between species. Note that while a method is provided for the graphical lasso (L1-regularization) its use is not recommended, as it will produce very sparse null networks and then a narrow (or singular) distribution of null edge weights.
The inverse covariance methods implemented in partial_correlation
result in symmetric association metrics. Non-symmetric metrics (e.g. describing predation or commensalism) are possible mathematically but their usage is not well-established. For an example of how to implement these, see pairwise_association
.
Value
A list with the following components:
matrix_spsite_obs |
Trimmed |
matrix_spsite_nul |
Trimmed |
matrix_spsp_obs |
Observed co-occurrence scores for all species |
matrix_spsp_ses_thresholded |
Observed co-occurrence scores for all species after removing those with non-significant p-values |
matrix_spsp_pvalue |
P-values for all species after correction for multiple comparisons |
network_all |
An |
network_pos |
An |
network_pos |
An |
See Also
vegan::permat
Examples
set.seed(1)
nsp <- 10
nsi <- 50
m_obs <- floor(matrix(rpois(nsp*nsi,lambda=5),ncol=nsi,nrow=nsp))
m_nul <- floor(matrix(rpois(nsp*nsi,lambda=5),ncol=nsi,nrow=nsp))
m_obs[1,1:(nsi/2)] <- rpois(n=nsi/2,lambda=20)
m_obs[2,1:(nsi/2)] <- rpois(n=nsi/2,lambda=20)
n <- make_netassoc_network(m_obs, m_nul,
method="partial_correlation",args=list(method="shrinkage"),
p.method='fdr',
numnulls=100, plot=TRUE,alpha=0.05)
# experimental demonstration of non-symmetric metrics
#n <- make_netassoc_network(m_obs, m_nul,
# method="pairwise_association",args=list(method="condentropy"),
# p.method='fdr',
# numnulls=100, plot=TRUE,alpha=0.05)
n$network_all