KAPPA {nsRFA} | R Documentation |
Four parameter kappa distribution and L-moments
Description
KAPPA
provides the link between L-moments of a sample and the four parameter
kappa distribution.
Usage
f.kappa (x, xi, alfa, k, h)
F.kappa (x, xi, alfa, k, h)
invF.kappa (F, xi, alfa, k, h)
Lmom.kappa (xi, alfa, k, h)
par.kappa (lambda1, lambda2, tau3, tau4)
rand.kappa (numerosita, xi, alfa, k, h)
Arguments
x |
vector of quantiles |
xi |
vector of kappa location parameters |
alfa |
vector of kappa scale parameters |
k |
vector of kappa third parameters |
h |
vector of kappa fourth parameters |
F |
vector of probabilities |
lambda1 |
vector of sample means |
lambda2 |
vector of L-variances |
tau3 |
vector of L-CA (or L-skewness) |
tau4 |
vector of L-kurtosis |
numerosita |
numeric value indicating the length of the vector to be generated |
Details
Definition
Parameters (4): \xi
(location), \alpha
(scale), k
, h
.
Range of x
: upper bound is \xi + \alpha/k
if k>0
, \infty
if k \le 0
;
lower bound is \xi + \alpha(1-h^{-k})/k
if h>0
, \xi + \alpha/k
if h \le 0
and k<0
and -\infty
if h \le 0
and k \ge 0
Probability density function:
f(x)=\alpha^{-1} [1-k(x-\xi)/\alpha]^{1/k-1} [F(x)]^{1-h}
Cumulative distribution function:
F(x)=\{1-h[1-k(x-\xi)/\alpha]^{1/k}\}^{1/h}
Quantile function:
x(F)= \xi + \frac{\alpha}{k} \left[ 1-\left( \frac{1-F^h}{h} \right)^k \right]
h=-1
is the generalized logistic distribution;
h=0
is the generalized eztreme value distribution;
h=1
is the generalized Pareto distribution.
L-moments
L-moments are defined for h \ge 0
and k>-1
, or if h<0
and -1<k<-1/h
.
\lambda_1 = \xi + \alpha(1-g_1)/k
\lambda_2 = \alpha(g_1 - g_2)/k
\tau_3 = (-g_1 + 3g_2 - 2g_3)/(g_1 - g_2)
\tau_4 = (-g_1 + 6g_2 - 10g_3 + 5g_4)/(g_1 - g_2)
where
g_r = \frac{r\Gamma(1+k)\Gamma(r/h)}{h^{1+k}\Gamma(1+k+r/h)}
if h>0
;
g_r = \frac{r \Gamma(1+k)\Gamma(-k-r/h)}{(-h)^{1+k}\Gamma(1-r/h)}
if h<0
;
Here \Gamma
denote the gamma function
\Gamma (x) = \int_0^{\infty} t^{x-1} e^{-t} dt
Parameters
There are no simple expressions for the parameters in terms of the L-moments.
However they can be obtained with a numerical algorithm considering the formulations of \tau_3
and \tau_4
in terms of k
and h
.
Here we use the function optim
to minimize (t_3-\tau_3)^2 + (t_4-\tau_4)^2
where t_3
and t_4
are the sample L-moment ratios.
Lmom.kappa
and par.kappa
accept input as vectors of equal length. In f.kappa
, F.kappa
,
invF.kappa
and rand.kappa
parameters (xi
, alfa
, k
, h
) must be atomic.
Value
f.kappa
gives the density f
, F.kappa
gives the distribution function F
, invFkappa
gives
the quantile function x
, Lmom.kappa
gives the L-moments (\lambda_1
, \lambda_2
, \tau_3
, \tau_4
), par.kappa
gives the parameters (xi
, alfa
, k
, h
), and rand.kappa
generates random deviates.
Note
For information on the package and the Author, and for all the references, see nsRFA
.
See Also
rnorm
, runif
, EXP
, GENLOGIS
, GENPAR
, GEV
, GUMBEL
, LOGNORM
, P3
; optim
, DISTPLOTS
, GOFmontecarlo
, Lmoments
.
Examples
data(hydroSIMN)
annualflows
summary(annualflows)
x <- annualflows["dato"][,]
fac <- factor(annualflows["cod"][,])
split(x,fac)
camp <- split(x,fac)$"45"
ll <- Lmoments(camp)
parameters <- par.kappa(ll[1],ll[2],ll[4],ll[5])
f.kappa(1800,parameters$xi,parameters$alfa,parameters$k,parameters$h)
F.kappa(1800,parameters$xi,parameters$alfa,parameters$k,parameters$h)
invF.kappa(0.771088,parameters$xi,parameters$alfa,parameters$k,parameters$h)
Lmom.kappa(parameters$xi,parameters$alfa,parameters$k,parameters$h)
rand.kappa(100,parameters$xi,parameters$alfa,parameters$k,parameters$h)
Rll <- regionalLmoments(x,fac); Rll
parameters <- par.kappa(Rll[1],Rll[2],Rll[4],Rll[5])
Lmom.kappa(parameters$xi,parameters$alfa,parameters$k,parameters$h)