parrevgum {lmomco} | R Documentation |
This function estimates the parameters of the Reverse Gumbel distribution given
the type-B L-moments of the data in an L-moment object such as that returned by
pwmRC
using pwm2lmom
. This distribution is important in the analysis of censored data. It is the distribution of a logarithmically transformed two-parameter Weibull distribution. The relation between distribution parameters and L-moments
is
α = λ^B_2/lbracelog(2) + mathrm{Ei}(-2log(1-zeta)) - mathrm{Ei}(-log(1-zeta))rbracembox{ and}
xi = λ^B_1 + αlbracemathrm{Ei}(-log(1-zeta))rbracembox{,}
where zeta is the right-tail censoring fraction of the sample o the nonexceedance probability of the right-tail censoring threshold, and mathrm{Ei}(x) is the exponential integral defined as
mathrm{Ei}(X) = int_X^{infty} x^{-1}e^{-x}mathrm{d}x mbox{,}
where mathrm{Ei}(-log(1-zeta)) rightarrow 0 as zeta rightarrow 1 and mathrm{Ei}(-log(1-zeta)) can not be evaluated as zeta rightarrow 0.
parrevgum(lmom,zeta=1,checklmom=TRUE)
lmom |
A L-moment object created by pwm2lmom
through pwmRC or other L-moment type object. The user intervention of the zeta differentiates this distribution and hence this function from similar parameter estimation functions in the lmomco package. |
zeta |
The right censoring fraction. Number of samples observed (noncensored) divided by the total number of samples. |
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. |
An R list
is returned.
type |
The type of distribution: revgum . |
para |
The parameters of the distribution. |
zeta |
The right censoring fraction. Number of samples observed (noncensored) divided by the total number of samples. |
source |
The source of the parameters: “parrevgum”. |
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, vol. 52, p. 105–124.
Hosking, J.R.M., 1995, The use of L-moments in the analysis of censored data, in Recent Advances in Life-Testing and Reliability, edited by N. Balakrishnan, chapter 29, CRC Press, Boca Raton, Fla., pp. 546–560.
pwm2lmom
, pwmRC
,
cdfrevgum
, quarevgum
# See p. 553 of Hosking (1995) # Data listed in Hosking (1995, table 29.3, p. 553) D <- c(-2.982, -2.849, -2.546, -2.350, -1.983, -1.492, -1.443, -1.394, -1.386, -1.269, -1.195, -1.174, -0.854, -0.620, -0.576, -0.548, -0.247, -0.195, -0.056, -0.013, 0.006, 0.033, 0.037, 0.046, 0.084, 0.221, 0.245, 0.296) D <- c(D,rep(.2960001,40-28)) # 28 values, but Hosking mentions 40 values in total z <- pwmRC(D,threshold=.2960001) str(z) # Hosking reports B-type L-moments for this sample are # lamB1 = -.516 and lamB2 = 0.523 btypelmoms <- pwm2lmom(z$Bbetas) # My version of R reports lamB1 = -0.5162 and lamB2 = 0.5218 str(btypelmoms) rg.pars <- parrevgum(btypelmoms,z$zeta) str(rg.pars) # Hosking reports xi = 0.1636 and alpha = 0.9252 for the sample # My version of R reports xi = 0.1635 and alpha = 0.9254