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