Burr {ReIns} | R Documentation |
The Burr distribution
Description
Density, distribution function, quantile function and random generation for the Burr distribution (type XII).
Usage
dburr(x, alpha, rho, eta = 1, log = FALSE)
pburr(x, alpha, rho, eta = 1, lower.tail = TRUE, log.p = FALSE)
qburr(p, alpha, rho, eta = 1, lower.tail = TRUE, log.p = FALSE)
rburr(n, alpha, rho, eta = 1)
Arguments
x |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
Number of observations. |
alpha |
The |
rho |
The |
eta |
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 |
Details
The Cumulative Distribution Function (CDF) of the Burr distribution is equal to
F(x) = 1-((\eta+x^{-\rho\times\alpha})/\eta)^{1/\rho}
for all x \ge 0
and F(x)=0
otherwise. We need that \alpha>0
, \rho<0
and \eta>0
.
Beirlant et al. (2004) uses parameters \eta, \tau, \lambda
which correspond to \eta
, \tau=-\rho\times\alpha
and \lambda=-1/\rho
.
Value
dburr
gives the density function evaluated in x
, pburr
the CDF evaluated in x
and qburr
the quantile function evaluated in p
. The length of the result is equal to the length of x
or p
.
rburr
returns a random sample of length n
.
Author(s)
Tom Reynkens.
References
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
See Also
Examples
# Plot of the PDF
x <- seq(0, 10, 0.01)
plot(x, dburr(x, alpha=2, rho=-1), xlab="x", ylab="PDF", type="l")
# Plot of the CDF
x <- seq(0, 10, 0.01)
plot(x, pburr(x, alpha=2, rho=-1), xlab="x", ylab="CDF", type="l")