| EXP | R Documentation |
EXP provides the link between L-moments of a sample and the two parameter
exponential distribution.
f.exp (x, xi, alfa)
F.exp (x, xi, alfa)
invF.exp (F, xi, alfa)
Lmom.exp (xi, alfa)
par.exp (lambda1, lambda2)
rand.exp (numerosita, xi, alfa)
x |
vector of quantiles |
xi |
vector of exp location parameters |
alfa |
vector of exp scale parameters |
F |
vector of probabilities |
lambda1 |
vector of sample means |
lambda2 |
vector of L-variances |
numerosita |
numeric value indicating the length of the vector to be generated |
See https://en.wikipedia.org/wiki/Exponential_distribution for a brief introduction on the Exponential distribution.
Definition
Parameters (2): \xi (lower endpoint of the distribution), \alpha (scale).
Range of x: \xi \le x < \infty.
Probability density function:
f(x) = \alpha^{-1} \exp\{-(x-\xi)/\alpha\}
Cumulative distribution function:
F(x) = 1 - \exp\{-(x-\xi)/\alpha\}
Quantile function:
x(F) = \xi - \alpha \log(1-F)
L-moments
\lambda_1 = \xi + \alpha
\lambda_2 = 1/2 \cdot \alpha
\tau_3 = 1/3
\tau_4 = 1/6
Parameters
If \xi is known, \alpha is given by \alpha = \lambda_1 - \xi and the L-moment, moment, and maximum-likelihood estimators are identical.
If \xi is unknown, the parameters are given by
\alpha = 2 \lambda_2
\xi = \lambda_1 - \alpha
For estimation based on a single sample these estimates are inefficient, but in regional frequency analysis they can give reasonable estimates of upper-tail quantiles.
Lmom.exp and par.exp accept input as vectors of equal length. In f.exp, F.exp, invF.exp and rand.exp parameters (xi, alfa) must be atomic.
f.exp gives the density f, F.exp gives the distribution function F, invFexp gives
the quantile function x, Lmom.exp gives the L-moments (\lambda_1, \lambda_2, \tau_3, \tau_4), par.exp gives the parameters (xi, alfa), and rand.exp generates random deviates.
For information on the package and the Author, and for all the references, see nsRFA.
rnorm, runif, GENLOGIS, GENPAR, GEV, GUMBEL, KAPPA, LOGNORM, P3; DISTPLOTS, GOFmontecarlo, Lmoments.
data(hydroSIMN)
annualflows
summary(annualflows)
x <- annualflows["dato"][,]
fac <- factor(annualflows["cod"][,])
split(x,fac)
camp <- split(x,fac)$"45"
ll <- Lmoments(camp)
parameters <- par.exp(ll[1],ll[2])
f.exp(1800,parameters$xi,parameters$alfa)
F.exp(1800,parameters$xi,parameters$alfa)
invF.exp(0.7870856,parameters$xi,parameters$alfa)
Lmom.exp(parameters$xi,parameters$alfa)
rand.exp(100,parameters$xi,parameters$alfa)
Rll <- regionalLmoments(x,fac); Rll
parameters <- par.exp(Rll[1],Rll[2])
Lmom.exp(parameters$xi,parameters$alfa)
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