persisting_outliers: Computes outlier persistence for a range of significance...

In lookout: Leave One Out Kernel Density Estimates for Outlier Detection

Computes outlier persistence for a range of significance values.

Description

This function computes outlier persistence for a range of significance values, using the algorithm lookout, an outlier detection method that uses leave-one-out kernel density estimates and generalized Pareto distributions to find outliers.

Usage

persisting_outliers(
  X,
  alpha = seq(0.01, 0.1, by = 0.01),
  st_qq = 0.9,
  unitize = TRUE,
  num_steps = 20
)

Arguments

X	The input data in a matrix, data.frame, or tibble format. All columns should be numeric.
alpha	Grid of significance levels.
st_qq	The starting quantile for death radii sequence. This will be used to compute the starting bandwidth value.
unitize	An option to normalize the data. Default is TRUE, which normalizes each column to [0,1].
num_steps	The length of the bandwidth sequence.

Value

A list with the following components:

out	A 3D array of N x num_steps x num_alpha where N denotes the number of observations, num_steps denote the length of the bandwidth sequence and num_alpha denotes the number of significance levels. This is a binary array and the entries are set to 1 if that observation is an outlier for that particular bandwidth and significance level.
bw	The set of bandwidth values.
gpdparas	The GPD parameters used.
lookoutbw	The bandwidth chosen by the algorithm lookout using persistent homology.

Examples

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))
)
plot(X, pch = 19)
outliers <- persisting_outliers(X, unitize = FALSE)
outliers
autoplot(outliers)

lookout documentation built on Oct. 14, 2022, 1:09 a.m.