getQQLimit {extremevalues} | R Documentation |
Determine outlier limit. These functions are called by the wrapper function getOutliersII
qqExponentialLimit(y, p, iLambda, alpha) qqLognormalLimit(y, p , iLambda, alpha) qqParetoLimit(y, p , iLambda, alpha) qqWeibullLimit(y, p , iLambda, alpha) qqNormalLimit(y, p , iLambda, alpha)
y |
Vector of real numbers |
p |
Corresponding quantile values |
N |
Index vector, indicating which elements of y should be used in the fit |
rho |
Confidence values for residual distribution |
The functions fit a model cdf to the observed y and p and returns the confidence limits for the fit residuals.
limit |
The residual-values corresponding to the confidence values |
R2 |
R-squared value for the fit |
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)/1000; L <- qqExponentialLimit(y,p,seq(10,90),0.05);