| 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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