lmomco (version 2.3.1)

pargam: Estimate the Parameters of the Gamma Distribution

Description

This function estimates the parameters of the Gamma distribution given the L-moments of the data in an L-moment object such as that returned by lmoms. Both the two-parameter Gamma and three-parameter Generalized Gamma distributions are supported based on the desired choice of the user, and numerical-hybrid methods are required. The pdfgam documentation provides further details.

Usage

pargam(lmom, p=c("2", "3"), checklmom=TRUE, ...)

Arguments

lmom

A L-moment object created by lmoms or vec2lmom.

p

The number of parameters to estimate for the 2-p Gamma or 3-p Generalized Gamma.

checklmom

Should the lmom be checked for validity using the are.lmom.valid function. Normally this should be left as the default and it is very unlikely that the L-moments will not be viable (particularly in the \(\tau_4\) and \(\tau_3\) inequality). However, for some circumstances or large simulation exercises then one might want to bypass this check.

...

Other arguments to pass.

Value

An R list is returned.

type

The type of distribution: gam.

para

The parameters of the distribution.

source

The source of the parameters: “pargam”.

References

Hosking, J.R.M., 1990, L-moments---Analysis and estimation of distributions using linear combinations of order statistics: Journal of the Royal Statistical Society, Series B, v. 52, pp. 105--124.

Hosking, J.R.M., 1996, FORTRAN routines for use with the method of L-moments: Version 3, IBM Research Report RC20525, T.J. Watson Research Center, Yorktown Heights, New York.

Hosking, J.R.M., and Wallis, J.R., 1997, Regional frequency analysis---An approach based on L-moments: Cambridge University Press.

See Also

lmomgam, cdfgam, pdfgam, quagam

Examples

# NOT RUN {
pargam(lmoms(abs(rnorm(20, mean=10))))

# }
# NOT RUN {
pargam(lmomgam(vec2par(c(0.3,0.4,+1.2), type="gam")), p=3)$para
pargam(lmomgam(vec2par(c(0.3,0.4,-1.2), type="gam")), p=3)$para
#        mu      sigma         nu 
# 0.2999994  0.3999990  1.1999696
# 0.2999994  0.4000020 -1.2000567
# }