Model-Based Clustering Analysis

Performs comprehensive model-based clustering analysis using the MoEClust package. Systematically tests multiple covariance models and provides results on both original and scaled data scales for interpretation.

Usage

clustering(
  data,
  vars,
  n_clusters,
  scaling = "standardize",
  models = "all",
  verbose = TRUE,
  na_action = "omit"
)

cluster(
  data,
  vars,
  n_clusters,
  scaling = "standardize",
  models = "all",
  verbose = TRUE,
  na_action = "omit"
)

Arguments

data

A data frame containing the dataset.

vars

A character vector of column names to use for clustering.

n_clusters

An integer or vector specifying the number of clusters (G) to fit.

scaling

Scaling method: "standardize" (z-score), "center" (mean only), "minmax" (0-1 range), or "none". Defaults to "standardize".

models

Character vector of model names to test, or "all" for all 14 models. Valid models: EII, VII, EEI, VEI, EVI, VVI, EEE, EVE, VEE, VVE, EEV, VEV, EVV, VVV.

verbose

Logical. If TRUE, prints progress messages. Defaults to TRUE.

na_action

How to handle NAs: "omit" (remove rows) or "fail" (stop with error). Defaults to "omit".

Value

An object of class "moe_analysis" containing:

• models: List of fitted models with results

• data: Original and scaled data used

• parameters: Analysis parameters

• summary: Summary statistics including best model

Examples

if (FALSE) { # \dontrun{
# Basic usage with iris data
results <- clustering(
  data = iris,
  vars = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
  n_clusters = 3
)

# Test specific models
results <- clustering(
  data = iris,
  vars = c("Sepal.Length", "Sepal.Width"),
  n_clusters = 2:4,
  models = c("EEE", "VVV", "VEV")
)

# View results using plot()
plot(results, type = "profile")
plot(results, type = "heatmap")
plot(results, type = "all")

# Get cluster assignments
data_clustered <- get_cluster_assignments(results)

# Assess stability
stability <- assess_cluster_stability(results)

# Generate report
generate_cluster_report(results)
} # }