GPD | R Documentation |
Density, distribution function, quantile function and random generation for the Generalised Pareto Distribution (GPD).
dgpd(x, gamma, mu = 0, sigma, log = FALSE)
pgpd(x, gamma, mu = 0, sigma, lower.tail = TRUE, log.p = FALSE)
qgpd(p, gamma, mu = 0, sigma, lower.tail = TRUE, log.p = FALSE)
rgpd(n, gamma, mu = 0, sigma)
x |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
Number of observations. |
gamma |
The |
mu |
The |
sigma |
The |
log |
Logical indicating if the densities are given as |
lower.tail |
Logical indicating if the probabilities are of the form |
log.p |
Logical indicating if the probabilities are given as |
The Cumulative Distribution Function (CDF) of the GPD for \gamma \neq 0
is equal to
F(x) = 1-(1+\gamma (x-\mu)/\sigma)^{-1/\gamma}
for all x \ge \mu
and F(x)=0
otherwise.
When \gamma=0
, the CDF is given by
F(x) = 1-\exp((x-\mu)/\sigma)
for all x \ge \mu
and F(x)=0
otherwise.
dgpd
gives the density function evaluated in x
, pgpd
the CDF evaluated in x
and qgpd
the quantile function evaluated in p
. The length of the result is equal to the length of x
or p
.
rgpd
returns a random sample of length n
.
Tom Reynkens.
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
tGPD
, Pareto
, EPD
, Distributions
# Plot of the PDF
x <- seq(0, 10, 0.01)
plot(x, dgpd(x, gamma=1/2, sigma=5), xlab="x", ylab="PDF", type="l")
# Plot of the CDF
x <- seq(0, 10, 0.01)
plot(x, pgpd(x, gamma=1/2, sigma=5), xlab="x", ylab="CDF", type="l")
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