Description Usage Arguments Value Author(s) References Examples
Given the parameters of a Laplace distribution, aout.laplace
identifies α-outliers in a given data set.
1 | aout.laplace(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 Laplace distribution: μ, σ. |
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 |
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
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.
1 2 3 4 |
Loading required package: Rsolnp
Loading required package: nleqslv
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
data is.outlier
1 0.2650 FALSE
2 0.2690 FALSE
3 0.2970 FALSE
4 0.3150 FALSE
5 0.3225 FALSE
6 0.3380 FALSE
7 0.3790 FALSE
8 0.3800 FALSE
9 0.3920 FALSE
10 0.4020 FALSE
11 0.4120 FALSE
12 0.4160 FALSE
13 0.4180 FALSE
14 0.4230 FALSE
15 0.4490 FALSE
16 0.4840 FALSE
17 0.4940 FALSE
18 0.6130 FALSE
19 0.6540 TRUE
20 0.7400 TRUE
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