Description Usage Arguments Value Examples
This function takes the output of het_cv_glasso
or
mixglasso
and creates a plot showing the correlation between specified
node pairs in the network for all groups. The subplots for each node pair are
arranged in a numPairs by numGroups grid. Partial correlations associated
with each node pair are also displayed.
1 2 3 |
net.clustering |
A network clustering object as returned by
|
data |
Observed data for the nodes, a numObs by numNodes matrix. Note that nodes need to be in the same ordering as in node.names. |
node.pairs |
A matrix of size numPairs by 2, where each row contains a pair of nodes to display. If node.names is specified, names in node.pairs must correspond to elements of node.names. |
display |
If TRUE, print the plot to the current output device. |
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). |
cex |
Scale factor for text and symbols in plot. |
Returns a ggplot2 object. If display=TRUE, additionally displays the plot.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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$bic.opt]]
# Specify edges
node.pairs = rbind(c(1,3), c(6,9),c(7,8))
# Create scatter plots of specified edges
scatter_plot(mixglasso.clustering, data=sim.result$data,
node.pairs=node.pairs)
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