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Introduction: From Clustering to Density Plots

library(clustNet)

Clustering

First, we need to learn the networks. Here, we simulate data from three clusters. This process takes around two minutes on a local PC.

library(clustNet)

# Simulate data
k_clust <- 3 # numer of clusters
ss <- c(400, 500, 600) # samples in each cluster
simulation_data <- sampleData(k_clust = k_clust, n_vars = 20, n_samples = ss)
sampled_data <- simulation_data$sampled_data

# Network-based clustering
cluster_results <- get_clusters(sampled_data, k_clust = k_clust)

Visualization of networks

We can visualize the networks as follows.

# Load additional pacakges to visualize the networks
library(ggplot2)
library(ggraph)
library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

library(ggpubr)

# Visualize networks
plot_clusters(cluster_results)

Visualization of networks

Finally, we can create a density plot of our clustering.

# Load additional pacakges to create a 2d dimensionality reduction
library(car)

## Loading required package: carData

library(ks)

## 
## Attaching package: 'ks'

## The following object is masked from 'package:igraph':
## 
##     compare

library(graphics)
library(stats)

# Plot a 2d dimensionality reduction
density_plot(cluster_results)

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They may not be fully stable and should be used with caution. We make no claims about them.