Frechet | R Documentation |
The Frechet distribution is a special case of the \link{GEV}
distribution,
obtained when the GEV shape parameter \xi
is positive.
It may be referred to as a type II extreme value distribution.
Frechet(location = 0, scale = 1, shape = 1)
location |
The location (minimum) parameter |
scale |
The scale parameter |
shape |
The shape parameter |
We recommend reading this documentation on https://alexpghayes.github.io/distributions3/, where the math will render with additional detail and much greater clarity.
In the following, let X
be a Frechet random variable with location
parameter location
= m
, scale parameter scale
=
s
, and shape parameter shape
= \alpha
.
A Frechet(m, s, \alpha
) distribution is equivalent to a
\link{GEV}
(m + s, s / \alpha, 1 / \alpha
) distribution.
Support: (m, \infty)
.
Mean: m + s\Gamma(1 - 1/\alpha)
, for \alpha > 1
; undefined
otherwise.
Median: m + s(\ln 2)^{-1/\alpha}
.
Variance:
s^2 [\Gamma(1 - 2 / \alpha) - \Gamma(1 - 1 / \alpha)^2]
for \alpha > 2
; undefined otherwise.
Probability density function (p.d.f):
f(x) = \alpha s ^ {-1} [(x - m) / s] ^ {-(1 + \alpha)}%
\exp\{-[(x - m) / s] ^ {-\alpha} \}
for x > m
. The p.d.f. is 0 for x \leq m
.
Cumulative distribution function (c.d.f):
F(x) = \exp\{-[(x - m) / s] ^ {-\alpha} \}
for x > m
. The c.d.f. is 0 for x \leq m
.
A Frechet
object.
Other continuous distributions:
Beta()
,
Cauchy()
,
ChiSquare()
,
Erlang()
,
Exponential()
,
FisherF()
,
GEV()
,
GP()
,
Gamma()
,
Gumbel()
,
LogNormal()
,
Logistic()
,
Normal()
,
RevWeibull()
,
StudentsT()
,
Tukey()
,
Uniform()
,
Weibull()
set.seed(27)
X <- Frechet(0, 2)
X
random(X, 10)
pdf(X, 0.7)
log_pdf(X, 0.7)
cdf(X, 0.7)
quantile(X, 0.7)
cdf(X, quantile(X, 0.7))
quantile(X, cdf(X, 0.7))
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