lmom (version 1.1)

pel-functions: Parameter estimation for specific distributions by the method of L-moments

Description

Computes the parameters of a probability distribution as a function of the $L$-moments. The following distributions are recognized: ll{ pelexp exponential pelgam gamma pelgev generalized extreme-value pelglo generalized logistic pelgpa generalized Pareto pelgno generalized normal pelgum Gumbel (extreme-value type I) pelkap kappa pelln3 three-parameter lognormal pelnor normal pelpe3 Pearson type III pelwak Wakeby pelwei Weibull }

Usage

pelexp(lmom)
pelgam(lmom)
pelgev(lmom)
pelglo(lmom)
pelgno(lmom)
pelgpa(lmom, bound = NULL)
pelgum(lmom)
pelkap(lmom)
pelln3(lmom, bound = NULL)
pelnor(lmom)
pelpe3(lmom)
pelwak(lmom, bound = NULL, verbose = FALSE)
pelwei(lmom, bound = NULL)

Arguments

lmom

Numeric vector containing the $L$-moments of the distribution or of a data sample.

bound

Lower bound of the distribution. If NULL (the default), the lower bound will be estimated along with the other parameters.

verbose

Logical: whether to print a message when not all parameters of the distribution can be computed.

Value

• A numeric vector containing the parameters of the distribution.

Details

Numerical methods and accuracy are as described in Hosking (1996, pp. 8--9).

References

Hosking, J. R. M. (1996). Fortran routines for use with the method of $L$-moments, Version 3. Research Report RC20525, IBM Research Division, Yorktown Heights, N.Y.

See Also

pelp for parameter estimation of a general distribution specified by its cumulative distribution function or quantile function. lmrexp, etc., to compute the $L$-moments of a distribution given its parameters. For individual distributions, see their cumulative distribution functions: ll{ cdfexp exponential cdfgam gamma cdfgev generalized extreme-value cdfglo generalized logistic cdfgpa generalized Pareto cdfgno generalized normal cdfgum Gumbel (extreme-value type I) cdfkap kappa cdfln3 three-parameter lognormal cdfnor normal cdfpe3 Pearson type III cdfwak Wakeby cdfwei Weibull }

Examples

# Sample L-moments of Ozone from the airquality data
data(airquality)
lmom <- samlmu(airquality$Ozone)

# Fit a GEV distribution
pelgev(lmom)