fitFunctions: Fit model distributions

In cannin/extremevalues: Univariate Outlier Detection

Description

Fit model distribution to a set of observations.

Usage

fitNormal(y, p)
fitLognormal(y, p)
fitPareto(y, p)
fitExponential(y, p)
fitWeibull(y, p)

Arguments

y	Vector of one-dimensional nonnegative data
p	Corresponding quantile values

Details

The function sorts the values of y and uses (log)linear regression to fit the values between the pmin and pmax quantile to the cdf of a model distribution.

Value

R2	R-squared value for the fit
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

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 = 10^rnorm(50);
L <- getOutliers(y,rho=0.5);

cannin/extremevalues documentation built on May 12, 2019, 5:22 p.m.