getLimit {extremevalues} | R Documentation |
Determine outlier limit. These functions are called by the wrapper function getOutliers
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)=\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 |
Mark van der Loo, see www.markvanderloo.eu
M.P.J. van der Loo, Distribution based outlier detection for univariate data. Discussion paper 10xxxx, Statistics Netherlands, The Hague (in press). Available from www.markvanderloo.eu or www.cbs.nl.
y <- sort(exp(rnorm(100))); p <- seq(1,100)/100; II <- seq(10,90) L <- getExponentialLimit(y[II],p[II],100,1.0);