Skip to contents

Plot anomaly boxplot with faceted feature distributions

Source: R/anomaly_plot.R
plot_anomaly_boxplot_faceted.Rd

Create faceted boxplots for better comparison when many features are involved. All data points are shown as small black dots, with the anomalous point(s) highlighted in red for easy identification. This is useful when top_n is large or you want to see all features. Can work with or without a contribution object.

Usage

plot_anomaly_boxplot_faceted(
  contribution_obj = NULL,
  data,
  sample_id = NULL,
  top_n = 8,
  ncol = 2,
  highlight_color = "red",
  highlight_size = 3,
  scales = "free_y"
)

Arguments

contribution_obj

A feature_contribution object from feature_contribution(), or NULL. If NULL, you can specify multiple sample_ids to highlight multiple anomalies.

data

The original training data

sample_id

The sample ID(s) to visualize. Can be a single value or a vector.

  • If contribution_obj is provided: only single value is used

  • If contribution_obj is NULL: can be a vector to mark multiple anomalies

  • If NULL and contribution_obj provided: uses first sample from contribution_obj

top_n

Number of top contributing features to display (default 8, NULL for all)

ncol

Number of columns in facet grid (default 2)

highlight_color

Color for the anomaly point(s) (default "red")

highlight_size

Size of the anomaly point(s) (default 3). Automatically adjusted to 1 when >5 anomalies are highlighted

scales

Should scales be fixed ("fixed") or free ("free", "free_y")? Default "free_y"

Value

A ggplot object

Examples

# With contribution object
model <- isoForest(iris[1:4])
contributions <- feature_contribution(model, sample_ids = 42, data = iris[1:4])
plot_anomaly_boxplot_faceted(contributions, iris[1:4], sample_id = 42)


# Without contribution object - mark multiple anomalies
anomaly_ids <- c(42, 107, 119, 132, 135)
plot_anomaly_boxplot_faceted(NULL, iris[1:4], sample_id = anomaly_ids)