getLimit {extremevalues} | R Documentation |
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 |
mu |
(lognormal only) Estimated |
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 |
lambda |
(weibull only) estimated scaling parameter |
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);