lmomgam | R Documentation |
This function estimates the L-moments of the Gamma distribution given the parameters (\alpha
and \beta
) from pargam
. The L-moments in terms of the parameters are complicated and solved numerically. This function is adaptive to the 2-parameter and 3-parameter Gamma versions supported by this package. For legacy reasons, lmomco continues to use a port of Hosking's FORTRAN into R if the 2-parameter distribution is used but the 3-parameter generalized Gamma distribution calls upon theoLmoms.max.ostat
. Alternatively, the theoTLmoms
could be used: theoTLmoms(para)
is conceptually equivalent to the internal calls to theoLmoms.max.ostat
made for the lmomgam
implementation.
lmomgam(para, ...)
para |
The parameters of the distribution. |
... |
Additional arguments to pass to |
An R list
is returned.
lambdas |
Vector of the L-moments. First element is
|
ratios |
Vector of the L-moment ratios. Second element is
|
trim |
Level of symmetrical trimming used in the computation, which is |
leftrim |
Level of left-tail trimming used in the computation, which is |
rightrim |
Level of right-tail trimming used in the computation, which is |
source |
An attribute identifying the computational source of the L-moments: “lmomgam”. |
W.H. Asquith
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, p. 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.
pargam
, cdfgam
, pdfgam
, quagam
lmomgam(pargam(lmoms(c(123,34,4,654,37,78))))
## Not run:
# 3-p Generalized Gamma Distribution and comparisons of 3-p Gam parameterization.
# 1st parameter A[lmomco] = A[gamlss] = exp(A[flexsurv])
# 2nd parameter B[lmomco] = B[gamlss] = B[flexsurv]
# 3rd parameter C[lmomco] = C[gamlss] --> C[flexsurv] = B[lmomco]/C[lmomco]
lmomgam(vec2par(c(7.4, 0.2, 14), type="gam"), nmom=5)$lambdas # numerics
lmoms(gamlss.dist::rGG(50000, mu=7.4, sigma=0.2, nu=14))$lambdas # simulation
lmoms(flexsurv::rgengamma(50000, log(7.4), 0.2, Q=0.2*14))$lambdas # simulation
#[1] 5.364557537 1.207492689 -0.110129217 0.067007941 -0.006747895
#[1] 5.366707749 1.209455502 -0.108354729 0.066360223 -0.006716783
#[1] 5.356166684 1.197942329 -0.106745364 0.069102821 -0.008293398#
## End(Not run)
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