Frechet | R Documentation |
Density function, distribution function, quantile function, random generation.
dfrechet(x, shape, xmin, log = FALSE)
pfrechet(q, shape, xmin, lower.tail=TRUE, log.p = FALSE)
qfrechet(p, shape, xmin, lower.tail=TRUE, log.p = FALSE)
rfrechet(n, shape, xmin)
dufrechet(x, log = FALSE)
pufrechet(q, lower.tail=TRUE, log.p = FALSE)
qufrechet(p, lower.tail=TRUE, log.p = FALSE)
rufrechet(n)
x , q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
shape |
shape parameter. |
xmin |
lower bound parameter. |
log , log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
The Frechet distribution is defined by the following density
f(x) = shape * (x - xmin)^{(-shape-1)} * exp(-(x - xmin)^{(-shape)})
for all x>xmin
.
The unit Frechet distribution corresponds to xmin=0
and
shape=1
.
dfrechet, dufrechet
give the density,
pfrechet, pufrechet
give the distribution function,
qfrechet, qufrechet
give the quantile function, and
rfrechet, rufrechet
generate random deviates.
The length of the result is determined by n
for
rfrechet, rufrechet
, and is the maximum of the lengths of the
numerical parameters for the other functions.
The numerical parameters other than n
are recycled to the
length of the result. Only the first elements of the logical
parameters are used.
Christophe Dutang
Kotz, S. and Nadarajah, S. (2000), Extreme Value Distributions: Theory and Applications, Imperial College Press.
Beirlant, J., Goegebeur, Y., Teugels, J., Segers (2004), Statistics of Extremes: Theory and Applications, John Wiley and Sons.
#####
# (1) density function
x <- seq(0, 5, length=24)
cbind(x, dfrechet(x, 1/2, 1/4))
#####
# (2) distribution function
cbind(x, pfrechet(x, 1/2, 1/4))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.