Description Usage Arguments Details Value Author(s) References See Also Examples
Given the parameters of a Pareto distribution, aout.pareto
identifies α-outliers in a given data set.
1 | aout.pareto(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 Pareto distribution: λ, θ. |
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 |
We use the Pareto distribution with Lebesgue-density f(x) = \frac{λ θ^{λ}}{x^{λ + 1}}.
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.
A. Rehage
Gather, U.; Kuhnt, S.; Pawlitschko, J. (2003) Concepts of outlyingness for various data structures. In J. C. Misra (Ed.): Industrial Mathematics and Statistics. New Delhi: Narosa Publishing House, 545-585.
1 2 | data(citiesData)
aout.pareto(citiesData[[1]], c(1.31, 14815), alpha = 0.01)
|
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