lmomtexp | R Documentation |
This function estimates the L-moments of the Truncated Exponential distribution. The parameter \psi
is the right truncation of the distribution and \alpha
is a scale parameter, letting \beta = 1/\alpha
to match nomenclature of Vogel and others (2008), the L-moments in terms of the parameters, letting \eta = \mathrm{exp}(-\alpha\psi)
, are
\lambda_1 = \frac{1}{\beta} - \frac{\psi\eta}{1-\eta} \mbox{,}
\lambda_2 = \frac{1}{1-\eta}\biggl[\frac{1+\eta}{2\beta} -
\frac{\psi\eta}{1-\eta}\biggr] \mbox{,}
\lambda_3 = \frac{1}{(1-\eta)^2}\biggl[\frac{1+10\eta+\eta^2}{6\alpha} -
\frac{\psi\eta(1+\eta)}{1-\eta}\biggr] \mbox{, and}
\lambda_4 = \frac{1}{(1-\eta)^3}\biggl[\frac{1+29\eta+29\eta^2+\eta^3}{12\alpha} -
\frac{\psi\eta(1+3\eta+\eta^2)}{1-\eta}\biggr] \mbox{.}
The distribution is restricted to a narrow range of L-CV (\tau_2 = \lambda_2/\lambda_1
). If \tau_2 = 1/3
, the process represented is a stationary Poisson for which the probability density function is simply the uniform distribution and f(x) = 1/\psi
. If \tau_2 = 1/2
, then the distribution is represented as the usual exponential distribution with a location parameter of zero and a scale parameter 1/\beta
. Both of these limiting conditions are supported.
If the distribution shows to be Uniform (\tau_2 = 1/3
), then \lambda_1 = \psi/2
, \lambda_2 = \psi/6
, \tau_3 = 0
, and \tau_4 = 0
. If the distribution shows to be Exponential (\tau_2 = 1/2
), then \lambda_1 = \alpha
, \lambda_2 = \alpha/2
, \tau_3 = 1/3
and \tau_4 = 1/6
.
lmomtexp(para)
para |
The parameters of the distribution. |
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: “lmomtexp”. |
W.H. Asquith
Vogel, R.M., Hosking, J.R.M., Elphick, C.S., Roberts, D.L., and Reed, J.M., 2008, Goodness of fit of probability distributions for sightings as species approach extinction: Bulletin of Mathematical Biology, DOI 10.1007/s11538-008-9377-3, 19 p.
partexp
, cdftexp
, pdftexp
, quatexp
set.seed(1) # to get a suitable L-CV
X <- rexp(1000, rate=.001) + 100
Y <- X[X <= 2000]
lmr <- lmoms(Y)
print(lmr$lambdas)
print(lmomtexp(partexp(lmr))$lambdas)
print(lmr$ratios)
print(lmomtexp(partexp(lmr))$ratios)
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