EcotoneFinder (version 0.2.3)

NetworkCommunity: Perform Spinglass algorythm and find networks communities

Description

Perform Spinglass algorythm and find networks communities

Usage

NetworkCommunity(networkeco, run = 100)

Arguments

networkeco

A network object (either qgraph or igraph) or a list created by the NetworkEco or NetworkEcoSeries functions.

run

Number of runs for the spinglass algorithm. Computation may be heavy for high numbers.

Value

A list containing the number of runs, the number of possible communities defined by the spinglass algorithm (with frequencies) and the mean and rounded mean of the assignement of the nodes of the network to these communities.

Details

The function perform spinglass algorithm on the provided network. (see spinglass.community() function of the igraph package for more details) The provided graph is internally transformed into a igraph object if needed. The function returns a number of summary statistics from the n runs of the spinglass algorithm. Each run of the spinglass algorithm is done with a different seed, to ensure different outputs. The seeds are internally recycled by the with_seed fuction of the withr package, so that the global environment is not modified. The frequencies at which a number of communities are recognised in the network and the average assignements (rounded or not) of the nodes into these communities are returned by the function. The latter can help to statistically define groups for network graphical representations.

Examples

# NOT RUN {
 #### Artificial data:
 SyntheticTrial <- SyntheticData(SpeciesNum = 21, CommunityNum = 3,
                                 SpCo = NULL, Length = 500,
                                 Parameters = list(a=rep(60, 3),
                                                   b=c(0,250,500),
                                                   c=rep(0.01,3)),
                                 pal = c("#008585", "#FBF2C4", "#C7522B"))

 # Building first network:
 Network <- DistEco(SyntheticTrial[,2:ncol(SyntheticTrial)],
                    transpose = TRUE, plot = c("network"), spinglass = FALSE,
                    return.network = TRUE)

 ### Spinglass algorithm (increase number of run for better accuracy):
 SpinglassTrial <- NetworkCommunity(Network, run = 5)

 ### Network with spinglass groups:
 DistEco(SyntheticTrial[,2:ncol(SyntheticTrial)], transpose = TRUE,
         plot = c("network"), spinglass = FALSE, return.network = FALSE,
         manual.groups = as.factor(SpinglassTrial$Memberships$RoundedMean))


# }