export_network {nethet} | R Documentation |
This function takes the output of het_cv_glasso
or
mixglasso
and exports it as a text table in CSV format, where each
entry in the table records an edge in one group and its partial correlation.
export_network(net.clustering, file = "network_table.csv", node.names = rownames(net.clustering$Mu), group.names = sort(unique(net.clustering$comp)), p.corrs.thresh = 0.2, ...)
net.clustering |
A network clustering object as returned by
|
file |
Filename to save the network table under. |
node.names |
Names for the nodes in the network. If NULL, names from net.clustering will be used. |
group.names |
Names for the clusters or groups. If NULL, names from net.clustering will be used (by default these are integets 1:numClusters). |
p.corrs.thresh |
Threshold applied to the absolute partial correlations. Edges that are below the threshold in all of the groups are not exported. Using a negative value will export all possible edges (including those with zero partial correlation). |
... |
Further parameters passed to write.csv. |
Function does not return anything.
Frank Dondelinger
n = 500 p = 10 s = 0.9 n.comp = 3 # Create different mean vectors Mu = matrix(0,p,n.comp) # Define non-zero means in each group (non-overlapping) nonzero.mean = split(sample(1:p),rep(1:n.comp,length=p)) # Set non-zero means to fixed value for(k in 1:n.comp){ Mu[nonzero.mean[[k]],k] = -2/sqrt(ceiling(p/n.comp)) } # Generate data sim.result = sim_mix_networks(n, p, n.comp, s, Mu=Mu) mixglasso.result = mixglasso(sim.result$data, n.comp=3) mixglasso.clustering = mixglasso.result$models[[mixglasso.result$bic.opt]] ## Not run: # Save network in CSV format suitable for Cytoscape import export_network(mixglasso.clustering, file='nethet_network.csv', p.corrs.thresh=0.25, quote=FALSE) ## End(Not run)