Display visualizations from the clustering analysis. Supports multiple plot types including profile plots, heatmaps, bar charts, cluster sizes, and model comparison charts.
Usage
# S3 method for class 'moe_analysis'
plot(x, type = "profile", model = NULL, scale = "original", ...)Arguments
- x
An moe_analysis object
- type
Type of plot: "profile" (default), "heatmap", "barchart", "sizes", "bic", "aic", "icl", "comparison" (all three information criteria as separate plots), or "all" (all plot types for every fitted model, plus all model-comparison plots)
- model
Model to plot. If NULL (default), uses best model by BIC.
- scale
Data scale for plots: "original" (default) or "scaled"
- ...
Additional arguments (currently ignored)
Value
The plot object(s) invisibly. type = "comparison" returns the
three criterion plots. type = "all" returns a nested list containing
every fitted model's plots and all comparison plots.
Examples
if (FALSE) { # \dontrun{
results <- clustering(data, vars, n_clusters = 3)
# Profile plot (default)
plot(results)
# Heatmap
plot(results, type = "heatmap")
# All plot types
plot(results, type = "all")
# Information criteria, separately
plot(results, type = "bic")
plot(results, type = "aic")
plot(results, type = "icl")
# Specific model with scaled data
plot(results, type = "profile", model = "VVV", scale = "scaled")
} # }