aout.laplace {alphaOutlier}

Find \alpha-outliers in Laplace / double exponential data

Description

Given the parameters of a Laplace distribution, aout.laplace identifies \alpha-outliers in a given data set.

Usage

aout.laplace(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 Laplace distribution: \mu, \sigma.

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.

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.

Author(s)

A. Rehage

References

Dumonceaux, R.; Antle, C. E. (1973) Discrimination between the log-normal and the Weibull distributions. Technometrics, 15 (4), 923-926.

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.

Examples

# Using the flood data from Dumonceaux and Antle (1973):
temp <- c(0.265, 0.269, 0.297, 0.315, 0.3225, 0.338, 0.379, 0.380, 0.392, 0.402,
         0.412, 0.416, 0.418, 0.423, 0.449, 0.484, 0.494, 0.613, 0.654, 0.74)
aout.laplace(temp, c(median(temp), median(abs(temp - median(temp)))), 0.05)