Cluster extraction utility

Extract and fit clusters from an input graph.

extractClusters(
  graph,
  data,
  group = NULL,
  membership = NULL,
  map = FALSE,
  verbose = FALSE,
  ...
)

Arguments

graph

Input network as an igraph object.

data

A matrix or data.frame. Rows correspond to subjects, and columns to graph nodes (variables).

group

A binary vector. This vector must be as long as the number of subjects. Each vector element must be 1 for cases and 0 for control subjects. Group specification enables node perturbation testing. By default, group = NULL.

membership

A vector of cluster membership IDs. If NULL, clusters will be automatically generated with clusterGraph using the edge betweenness clustering ("ebc") algorithm.

map

Logical value. If TRUE, the plot of the input graph (coloured by cluster membership) will be generated along with independent module plots. If the input graph is very large, plotting could be computationally intensive (by default, map = FALSE).

verbose

Logical value. If TRUE, a plot will be showed for each cluster.

...

Currently ignored.

Value

A list of 3 objects:

1. "clusters", list of clusters as igraph objects;

2. "fit", list of fitting results for each cluster as a lavaan object;

3. "dfc", data.frame of summary results.

Author

Fernando Palluzzi fernando.palluzzi@gmail.com

Examples

# \donttest{

# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.

adjdata <- SEMbap(alsData$graph, als.npn)$data
#> Bow-free covariances search. Use method: cggm ...
#> Number of bow-free covariances / df : 220 / 420 
#> Max parent set(S) / Sparsity idx(s) : 10 / 4 
#> Number of clusters / number of nodes: 2 / 31 
#> 

# Clusters creation
clusters <- extractClusters(alsData$graph, adjdata, alsData$group)
#> modularity = 0.5588502 
#> 
#> Community sizes
#>  3  2  1  4 
#>  4  8  9 11 
#> 
#> 
 cluster= 1 of 3
 cluster= 2 of 3
 cluster= 3 of 3
#> 
#> 
#> Found 3 clusters with > 5 nodes
print(clusters$dfc)
#>   cluster n.nodes n.edges dev_df  srmr V.pv.act V.pv.inh
#> 1     HM1       9       8  1.062 0.052 0.000118 0.058611
#> 2     HM2       8       7  1.180 0.059 0.000468 0.927939
#> 3     HM4      11      25  1.840 0.059 0.000002 0.007126
head(parameterEstimates(clusters$fit$HM1))
#>     lhs op   rhs    est
#> 1  6647  ~ group -0.156
#> 2 10452  ~ group -0.021
#> 3 84134  ~ group -0.080
#> 4 79139  ~ group  0.000
#> 5  5530  ~ group -0.134
#> 6  5532  ~ group  0.169
head(parameterEstimates(clusters$fit$HM2))
#>     lhs op   rhs    est
#> 1 54205  ~ group  0.056
#> 2   836  ~ group  0.200
#> 3   581  ~ group -0.013
#> 4   572  ~ group -0.045
#> 5   596  ~ group  0.127
#> 6   598  ~ group  0.198
head(parameterEstimates(clusters$fit$HM4))
#>    lhs op   rhs    est
#> 1 4217  ~ group  0.086
#> 2 5606  ~ group  0.033
#> 3 5608  ~ group  0.188
#> 4 1432  ~ group -0.017
#> 5 5600  ~ group -0.296
#> 6 5603  ~ group  0.003
gplot(clusters$clusters$HM2)


# Map cluster on the input graph
g <- alsData$graph
c <- clusters$clusters$HM2
V(g)$color <- ifelse(V(g)$name %in% V(c)$name, "gold", "white")
gplot(g)


# }