Plot all anomaly projections (PCA and UMAP comparison) — plot_anomaly_projection

Create a comparison plot showing both PCA and UMAP projections of the same data, making it easy to see how different dimensionality reduction methods reveal anomalies. For large datasets, automatic sampling is applied to improve speed.

Usage

plot_anomaly_projection_all(
  model,
  data,
  method = "mtt",
  contamination = 0.05,
  point_size = 2,
  point_alpha = 0.6,
  umap_n_neighbors = 15,
  umap_min_dist = 0.1,
  sample_rate = 0.05
)

Arguments

model: An isoForest model object
data: The original data (must be numeric)
method: Threshold method for anomaly detection (default: "mtt")
contamination: Contamination rate (only used if method = "contamination")
point_size: Size of points in the plot (default: 2)
point_alpha: Transparency of points (default: 0.6)
umap_n_neighbors: Number of neighbors for UMAP (default: 15)
umap_min_dist: Minimum distance for UMAP (default: 0.1)
sample_rate: Target anomaly rate in the displayed data (default: 0.05). Set to NULL to disable sampling.

Value

A combined ggplot2 object showing both PCA and UMAP projections

Examples

if (FALSE) { # \dontrun{
model <- isoForest(iris[1:4])
comparison_plot <- plot_anomaly_projection_all(model, iris[1:4])
print(comparison_plot)
} # }