Learn R Programming

extremevalues (version 2.3.4)

fitFunctions: Fit model distributions

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)

Value

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

Arguments

y

Vector of one-dimensional nonnegative data

p

Corresponding quantile values

Author

Mark van der Loo, see www.markvanderloo.eu

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.

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

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

Run the code above in your browser using DataLab