Description Usage Arguments Details Value Author(s) References Examples
Determine outlier limit. These functions are called by the wrapper function getOutliers
1 2 3 4 5 | 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)
|
y |
Vector of one-dimensional nonnegative data |
p |
Corresponding quantile values |
N |
Number of observations |
rho |
Limiting expected value |
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.
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)=λ\exp(-λ 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 λ for weibull distribution |
Mark van der Loo, see www.markvanderloo.eu
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.
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