lookout: Identifies outliers using the algorithm lookout.

Description

This function identifies outliers using the algorithm lookout, an outlier detection method that uses leave-one-out kernel density estimates and generalized Pareto distributions to find outliers.

Usage

lookout(X, alpha = 0.05, unitize = TRUE, bw = NULL, gpd = NULL, fast = TRUE)

Value

A list with the following components:

outliers: The set of outliers.
outlier_probability: The GPD probability of the data.
outlier_scores: The outlier scores of the data.
bandwidth: The bandwdith selected using persistent homology.
kde: The kernel density estimate values.
lookde: The leave-one-out kde values.
gpd: The fitted GPD parameters.

Arguments

X: The input data in a dataframe, matrix or tibble format.
alpha: The level of significance. Default is 0.05.
unitize: An option to normalize the data. Default is TRUE, which normalizes each column to [0,1].
bw: Bandwidth parameter. Default is NULL as the bandwidth is found using Persistent Homology.
gpd: Generalized Pareto distribution parameters. If `NULL` (the default), these are estimated from the data.
fast: If set to TRUE, makes the computation faster by sub-setting the data for the bandwidth calculation.

Examples

Run this code

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2))
)
lo <- lookout(X)
lo
autoplot(lo)

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