get_clusters function

get_clusters

Network-based clustering

get_clusters(
  myData,
  k_clust = 3,
  n_bg = 0,
  quick = TRUE,
  EMseeds = 1,
  edgepmat = NULL,
  blacklist = NULL,
  bdepar = list(chi = 0.5, edgepf = 8),
  newallrelativeprobabs = NULL
)

Arguments

• myData: Data to be clustered, must be either binary (with levels "0"/"1") or categorical (with levels "0"/"1"/"2"/...)
• k_clust: Number of clusters
• n_bg: Number of covariates to be adjusted for; the position of the covariates must be in the last column of the myData matrix
• quick: if TRUE, then the runtime is quick but accuracy is lower
• EMseeds: Seeds
• edgepmat: Matrix of penalized edges in the search space
• blacklist: Matrix of forbidden edges in the search space
• bdepar: Hyperparameters for structure learning (BDE score)
• newallrelativeprobabs: relative probability of cluster assignment of each sample

Returns

a list containing the clusterMemberships and "assignprogress"

Examples

# choose data
sampled_data <- sampleData(n_vars = 15, n_samples = c(300,300,300))$sampled_data
# learn clusters
cluster_results <- get_clusters(sampled_data)
# visualize the networks
library(ggplot2)
library(ggraph)
library(igraph)
library(ggpubr)
plot_clusters(cluster_results)