| RevWeibull {DescTools} | R Documentation |
The Reverse Weibull Distribution
Description
Density function, distribution function, quantile function and random generation for the reverse (or negative) Weibull distribution with location, scale and shape parameters.
Usage
dRevWeibull(x, loc=0, scale=1, shape=1, log = FALSE)
pRevWeibull(q, loc=0, scale=1, shape=1, lower.tail = TRUE)
qRevWeibull(p, loc=0, scale=1, shape=1, lower.tail = TRUE)
rRevWeibull(n, loc=0, scale=1, shape=1)
dNegWeibull(x, loc=0, scale=1, shape=1, log = FALSE)
pNegWeibull(q, loc=0, scale=1, shape=1, lower.tail = TRUE)
qNegWeibull(p, loc=0, scale=1, shape=1, lower.tail = TRUE)
rNegWeibull(n, loc=0, scale=1, shape=1)
Arguments
x, q |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
Number of observations. |
loc, scale, shape |
Location, scale and shape parameters (can be given as vectors). |
log |
Logical; if |
lower.tail |
Logical; if |
Details
The reverse (or negative) Weibull distribution function with parameters
loc = a, scale = b and
shape = s is
G(z) = \exp\left\{-\left[-\left(\frac{z-a}{b}\right)
\right]^s\right\}
for z < a and one otherwise, where b > 0 and
s > 0.
Value
dRevWeibull and dNegWeibull give the density function,
pRevWeibull and pNegWeibull give the distribution function,
qRevWeibull and qNegWeibull give the quantile function,
rRevWeibull and rNegWeibull generate random deviates.
Note
Within extreme value theory the reverse Weibull distibution (also known as the negative Weibull distribution) is often referred to as the Weibull distribution. We make a distinction to avoid confusion with the three-parameter distribution used in survival analysis, which is related by a change of sign to the distribution given above.
Author(s)
Alec Stephenson <alec_stephenson@hotmail.com>
See Also
rFrechet, rGenExtrVal, rGumbel
Examples
dRevWeibull(-5:-3, -1, 0.5, 0.8)
pRevWeibull(-5:-3, -1, 0.5, 0.8)
qRevWeibull(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
rRevWeibull(6, -1, 0.5, 0.8)
p <- (1:9)/10
pRevWeibull(qRevWeibull(p, -1, 2, 0.8), -1, 2, 0.8)
## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9