aout.weibull: Find alpha-outliers in Weibull data

In alphaOutlier: Obtain Alpha-Outlier Regions for Well-Known Probability Distributions

Description

Given the parameters of a Weibull distribution, aout.weibull identifies α-outliers in a given data set.

Usage

aout.weibull(data, param, alpha = 0.1, hide.outliers = FALSE, lower = auto.l, 
             upper = auto.u, method.in = "Broyden", global.in = "qline", 
             control.in = list(sigma = 0.1, maxit = 1000, xtol = 1e-12, 
                               ftol = 1e-12, btol = 1e-04))

Arguments

data	a vector. The data set to be examined.
param	a vector. Contains the parameters of the Weibull distribution: β, λ.
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.
lower	an atomic vector. First element of x from nleqslv.
upper	an atomic vector. Second element of x from nleqslv.
method.in	See method in nleqslv
global.in	See global in nleqslv
control.in	See control in nleqslv

Details

The α-outlier region of a Weibull distribution is generally not available in closed form or via the tails, such that a non-linear equation system has to be solved.

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

Dodson, B. (2006) The Weibull Analysis Handbook. American Society for Quality, 2nd edition.

See Also

dweibull, nleqslv

Examples

# lifetime data example taken from Table 2.2, Dodson (2006)
temp <- c(12.5, 24.4, 58.2, 68.0, 69.1, 95.5, 96.6, 97.0, 
          114.2, 123.2, 125.6, 152.7)
aout.weibull(temp, c(2.25, 97), 0.1)

Example output

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   12.5       TRUE
2   24.4      FALSE
3   58.2      FALSE
4   68.0      FALSE
5   69.1      FALSE
6   95.5      FALSE
7   96.6      FALSE
8   97.0      FALSE
9  114.2      FALSE
10 123.2      FALSE
11 125.6      FALSE
12 152.7       TRUE

alphaOutlier documentation built on May 2, 2019, 3:59 p.m.