getLimit: Determine outlier limit

In extremevalues: Univariate Outlier Detection

Determine outlier limit

Description

Determine outlier limit. These functions are called by the wrapper function getOutliers

Usage

getExponentialLimit(y, p, N, rho)
getLognormalLimit(y, p, N, rho)
getNormalLimit(y, p, N, rho)
getParetoLimit(y, p, N, rho)
getWeibullLimit(y, p, N, rho)

Arguments

y	Vector of one-dimensional nonnegative data
p	Corresponding quantile values
N	Number of observations
rho	Limiting expected value

Details

The functions fit a model cdf to the observed y and p and returns the y-value above which less than rho values are expected, given N observations. See getOutlierLimit for a complete explanation.

Value

limit	The y-value above which less then rho observations are expected
R2	R-squared value for the fit
nFit	Number of values used in fit (length(y))
lamda	(exponential only) Estimated location (and spread) parameter for f(y)=\lambda\exp(-\lambda y)
mu	(lognormal only) Estimated {\sf E}(\ln(y)) for lognormal distribution
sigma	(lognormal only) Estimated Var(ln(y)) for lognormal distribution
ym	(pareto only) Estimated location parameter (mode) for pareto distribution
alpha	(pareto only) Estimated spread parameter for pareto distribution
k	(weibull only) estimated power parameter k for weibull distribution
lambda	(weibull only) estimated scaling parameter \lambda for weibull distribution

Author(s)

Mark van der Loo, see www.markvanderloo.eu

References

M.P.J. van der Loo, Distribution based outlier detection for univariate data. Discussion paper 10003, Statistics Netherlands, The Hague (2010). Available from www.markvanderloo.eu or www.cbs.nl.

Examples

y <- sort(exp(rnorm(100)));
p <- seq(1,100)/100;
II <- seq(10,90)
L <- getExponentialLimit(y[II],p[II],100,1.0);

extremevalues documentation built on April 4, 2025, 1 a.m.