| Burr | R Documentation |
Density, distribution function, quantile function and random generation for the Burr distribution (type XII).
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)
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 |
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.
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.
Tom Reynkens.
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
tBurr, Distributions
# 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")
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