clustNet (version 1.2.0)

get_clusters: get_clusters

Description

Network-based clustering

Usage

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
)

Value

a list containing the clusterMemberships and "assignprogress"

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

Examples

# \donttest{
# 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)
# }