aout.gandh: Find alpha-outliers in data from the family of g-and-h...

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/aout.gandh.R

Description

Given the parameters of a g-and-h distribution, aout.gandh identifies α-outliers in a given data set.

Usage

1
aout.gandh(data, param, alpha = 0.1, hide.outliers = FALSE)

Arguments

data

a vector. The data set to be examined.

param

a vector. Contains the parameters of the g-and-h distribution: median, scale, g, h.

alpha

an atomic vector. Determines the maximum amount of probability mass the outlier region may contain. Defaults to 0.1.

hide.outliers

boolean. Returns the outlier-free data if set to TRUE. Defaults to FALSE.

Details

The concept of α-outliers is based on the p.d.f. of the random variable. Since for g-and-h distributions this does not exist in closed form, the computation of the outlier region is based on an optimization of the quantile function with side conditions.

Value

Data frame of the input data and an index named is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.

Note

Makes use of solnp.

Author(s)

A. Rehage

References

Xu, Y.; Iglewicz, B.; Chervoneva, I. (2014) Robust estimation of the parameters of g-and-h distributions, with applications to outlier detection. Computational Statistics and Data Analysis 75, 66-80.

Examples

1
2
durations <- faithful$eruptions
aout.gandh(durations, c(4.25, 1.14, 0.05, 0.05), alpha = 0.1)

alphaOutlier documentation built on May 30, 2017, 8:11 a.m.