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Identifies outliers using the algorithm lookout.

Source: R/lookoutliers.R
lookout.Rd

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 = 0.01,
  beta = 0.9,
  gamma = 0.97,
  bw = NULL,
  gpd = NULL,
  scale = TRUE,
  fast = NROW(X) > 1000,
  old_version = FALSE
)

Arguments

X

The numerical input data in a data.frame, matrix or tibble format.

alpha

The level of significance. Default is 0.01. So there is a 1/100 chance of any point being falsely classified as an outlier.

beta

The quantile threshold used in the GPD estimation. Default is 0.90. To ensure there is enough data available, values greater than 0.90 are set to 0.90.

gamma

Parameter for bandwidth calculation giving the quantile of the Rips death radii to use for the bandwidth. Default is 0.97. Ignored under the old version; where the lower limit of the maximum Rips death radii difference is used. Also ignored if bw is provided.

bw

Bandwidth parameter. If NULL (default), the bandwidth is found using Persistent Homology.

gpd

Generalized Pareto distribution parameters. If NULL (the default), these are estimated from the data.

scale

If TRUE, the data is standardized. Using the old version, unit scaling is applied so that each column is in the range [0,1]. Under the new version, robust rotation and scaling is used so that the columns are approximately uncorrelated with unit variance. Default is TRUE.

fast

If TRUE (default), makes the computation faster by sub-setting the data for the bandwidth calculation.

old_version

Logical indicator of which version of the algorithm to use. Default is FALSE, meaning the newer version is used.

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.

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)
  )
)
lo <- lookout(X)
lo
#> Leave-out-out KDE outliers using lookout algorithm
#> 
#> Call: lookout(X = X)
#> 
#>   Outliers  Probability
#> 1      101 0.0019829565
#> 2      209 0.0004844054
#> 3      216 0.0047098869
#> 4      294 0.0000000000
#> 5      306 0.0001628749
#> 6      468 0.0002994101
#> 
autoplot(lo)