Description Usage Arguments Value Author(s) References See Also Examples
Given the parameters of a logistic distribution, aout.logis
identifies α-outliers in a given data set.
1 | aout.logis(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 logistic 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
Balakrishnan, N. (1992) Maximum likelihood estimation based on complete and type II censored samples. In N. Balakrishnan (Ed.): Handbook of the Logistic Distribution. Dekker, New York, 49-78.
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 | # Data example from Balakrishnan (1967)
lifetime <- c(785, 855, 905, 918, 919, 920, 929, 936, 948, 950)
aout.logis(lifetime, c(949.9, 63.44))
|
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