Plot a varPro isolation-forest anomaly score

Renders a gg_isopro object as a ranked elbow (observations sorted by anomaly score), a density of scores, or both side-by-side. Optionally annotates a threshold either in score-space (threshold) or in quantile-space (top_n_pct).

Usage

# S3 method for class 'gg_isopro'
plot(
  x,
  panel = c("both", "elbow", "density"),
  threshold = NULL,
  top_n_pct = NULL,
  ...
)

Arguments

x: A gg_isopro object from gg_isopro.
panel: One of "both" (default: a patchwork of elbow + density), "elbow", or "density" (each returns a single ggplot).
threshold: Numeric in [0, 1], or NULL (default). If set, draws a reference line at that howbad value on the elbow and density.
top_n_pct: Numeric in (0, 100), or NULL (default). If set, resolves to the matching howbad quantile and draws the same reference line. If both threshold and top_n_pct are supplied, threshold wins with a message().
...: Currently unused.

Value

A ggplot (single panel) or a patchwork (panel = "both").

Reading the elbow

The elbow plot is the canonical anomaly-detection picture. The x-axis is observation rank (observations sorted from most ordinary to most anomalous) and the y-axis is the howbad score. For a clean population the curve sits flat near zero across the bulk of the data and then bends sharply upward in the right tail; that bend is where the anomalous observations live. The point of the plot is not to read off a single score, it is to see where the curve breaks. Pick a cutoff there. Pass it back in as threshold (for a score) or top_n_pct (for "the top 5\ reference line so you can record the choice you made.

Reading the density

The density panel is the same scores viewed as a distribution. A single tight mode near zero with a long thin right tail is the picture you hope for: bulk of the data ordinary, a few clear anomalies. A bimodal density says you may have two populations rather than one clean cluster plus outliers, and the cutoff question becomes harder. Either way, this panel is a sanity check on what the elbow suggests.

Comparing methods

When the input object carries a method column (because you bound several gg_isopro calls together), both panels colour by method automatically. The point of comparing "rnd", "unsupv", and "auto" is not to pick a winner from the figure alone, it is to see whether the methods agree on which observations are anomalous. Curves that overlap in the right tail and elbow at roughly the same rank are telling you the same story three ways. Curves that diverge are telling you the score is method-sensitive, which is itself useful information.