lookout {weird}

Lookout probabilities

Description

Compute leave-one-out log score probabilities using a Generalized Pareto distribution. These give the probability of each observation being an anomaly.

Usage

lookout(
  object = NULL,
  density_scores = NULL,
  loo_scores = density_scores,
  threshold_probability = 0.95
)

Arguments

object

A model object or a numerical data set.

density_scores

Numerical vector of log scores

loo_scores

Optional numerical vector of leave-one-out log scores

threshold_probability

Probability threshold when computing the POT model for the log scores.

Details

This function can work with several object types. If object is not NULL, then the object is passed to density_scores to compute density scores (and possibly LOO density scores). Otherwise, the density scores are taken from the density_scores argument, and the LOO density scores are taken from the loo_scores argument. Then the Generalized Pareto distribution is fitted to the scores, to obtain the probability of each observation.

Value

A numerical vector containing the lookout probabilities

Author(s)

Rob J Hyndman

References

Sevvandi Kandanaarachchi & Rob J Hyndman (2022) "Leave-one-out kernel density estimates for outlier detection", J Computational & Graphical Statistics, 31(2), 586-599. https://robjhyndman.com/publications/lookout/

Examples

# Univariate data
tibble(
  y = c(5, rnorm(49)),
  lookout = lookout(y)
)
# Bivariate data with score calculation done outside the function
tibble(
  x = rnorm(50),
  y = c(5, rnorm(49)),
  fscores = density_scores(y),
  loo_fscores = density_scores(y, loo = TRUE),
  lookout = lookout(density_scores = fscores, loo_scores = loo_fscores)
)
# Using a regression model
of <- oldfaithful |> filter(duration < 7200, waiting < 7200)
fit_of <- lm(waiting ~ duration, data = of)
of |>
  mutate(lookout_prob = lookout(fit_of)) |>
  arrange(lookout_prob)