nethet (version 1.4.2)

scatter_plot: Create a scatterplot showing correlation between specific nodes in the network for each pre-specified group.

Description

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.

Usage

scatter_plot(net.clustering, data, node.pairs, display = TRUE,
  node.names = rownames(net.clustering$Mu),
  group.names = sort(unique(net.clustering$comp)), cex = 1)

Arguments

net.clustering

A network clustering object as returned by het_cv_glasso or mixglasso.

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.

Value

• Returns a ggplot2 object. If display=TRUE, additionally displays the plot.

Examples

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)