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
for all
and
otherwise. We need that
,
and
.
Beirlant et al. (2004) uses parameters which correspond to
,
and
.
Value
dburr
gives the density function evaluated in ,
pburr
the CDF evaluated in and
qburr
the quantile function evaluated in . The length of the result is equal to the length of
or
.
rburr
returns a random sample of length .
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")