directOutlyingness: Directional Outlyingness for Skewed Distribution

In ChristianGoueguel/specProc: Preprocessing Tools For Laser-Induced Breakdown Spectroscopy (LIBS) Spectra

Directional Outlyingness for Skewed Distribution

Description

This function computes the directional outlyingness of a numeric vector, as proposed by Rousseeuw et al. (2018), which is a measure of outlyingness of data point that takes the skewness of the underlying distribution into account.

Usage

directOutlyingness(
  x,
  cutoff.quantile = 0.995,
  rmZeroes = FALSE,
  maxRatio = NULL,
  precScale = 1e-10
)

Arguments

x	A numeric vector
cutoff.quantile	A numeric value between 0 and 1 specifying the quantile for outlier detection (default: 0.995).
rmZeroes	A logical value. If TRUE, removes values close to zero (default: FALSE).
maxRatio	A numeric value greater than 2. If provided, constrains the ratio between positive and negative scales (default: NULL).
precScale	A numeric value specifying the precision scale for near-zero comparisons (default: 1e-10).

Details

Directional outlyingness takes the potential skewness of the underlying distribution into account, by the splitting the univariate dataset in two half samples around the median. And then apply one-step M-estimator with Huber \rho-function for scaling each part.

Value

A tibble with columns:

• data: The original numeric values.

• score: The calculated outlyingness score.

• flag: A logical vector indicating whether each value is a potential outlier or not.

Author(s)

Christian L. Goueguel

References

• Rousseeuw, P.J., Raymaekers, J., Hubert, M., (2018). A Measure of Directional Outlyingness With Applications to Image Data and Video. Journal of Computational and Graphical Statistics, 27(2):345–359.

Examples

x <- c(1, 5, 3, 9, 2, 6, 4, 8, 7, 1e3)
directOutlyingness(x)

ChristianGoueguel/specProc documentation built on Nov. 9, 2024, 3:23 p.m.