fitFunctions {extremevalues} | R Documentation |
Fit model distribution to a set of observations.
fitNormal(y, p) fitLognormal(y, p) fitPareto(y, p) fitExponential(y, p) fitWeibull(y, p)
y |
Vector of one-dimensional nonnegative data |
p |
Corresponding quantile values |
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.
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 = 10^rnorm(50); L <- getOutliers(y,rho=0.5);