Description Usage Arguments Details Value Note Author(s) References Examples
Given the parameters of a g-and-h distribution, aout.gandh
identifies α-outliers in a given data set.
1 | aout.gandh(data, param, alpha = 0.1, hide.outliers = FALSE)
|
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 |
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.
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.
Makes use of solnp
.
A. Rehage
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.
1 2 | durations <- faithful$eruptions
aout.gandh(durations, c(4.25, 1.14, 0.05, 0.05), alpha = 0.1)
|
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