condor.cluster: Main clustering function for condor.
In jplatig/condor: COmplex Network Description Of Regulators

Description Usage Arguments Value Examples

This function performs community structure clustering using the bipartite modularity described in condor.modularity.max. This function uses a standard (non-bipartite) community structure clustering of the uni-partite, weighted projection of the original bipartite graph as an initial guess for the bipartite modularity.

1 2	condor.cluster(condor.object, cs.method = "LCS", project = TRUE, low.memory = FALSE, deltaQmin = "default")

`condor.object`	Output of make.condor.object. This function uses `condor.object$edges`
`cs.method`	is a string to specify which unipartite community structure algorithm should be used for the seed clustering. Options are `LCS` (`multilevel.community`), `LEC` (`leading.eigenvector.community`), `FG` (`fastgreedy.community`).
`project`	Provides options for initial seeding of the bipartite modularity maximization. If TRUE, the nodes in the first column of `condor.object$edges` are projected and clustered using `cs.method`. If FALSE, the complete bipartite network is clustered using the unipartite clustering methods listed in `cs.method`.
`low.memory`	If TRUE, uses `condor.modularity.max` instead of `condor.matrix.modularity`. This is a slower implementation of the modularity maximization, which does not store any matrices in memory. Useful on a machine with low RAM. However, runtimes are (much) longer.
`deltaQmin`	convergence parameter determining the minimum required increase in the modularity for each iteration. Default is min(10^-4,1/(number of edges)), with number of edges determined by `nrow(condor.object$edges)`. User can set this parameter by passing a numeric value to deltaQmin.

condor.object with condor.modularity.max output included.

r = c(1,1,1,2,2,2,3,3,3,4,4);
b = c(1,2,3,1,2,4,2,3,4,3,4);
reds <- c("Alice","Sue","Janine","Mary")
blues <- c("Bob","John","Ed","Hank")
elist <- data.frame(red=reds[r],blue=blues[b])
condor.object <- create.condor.object(elist)
condor.object <- condor.cluster(condor.object)