alphaOutlier (version 1.2.0)

aout.laplace: 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

is.outlier that flags the outliers with TRUE. If hide.outliers is set to TRUE, a simple vector of the outlier-free data.

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)