RevWeibull {DescTools} | R Documentation |
Density function, distribution function, quantile function and random generation for the reverse (or negative) Weibull distribution with location, scale and shape parameters.
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)
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 |
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
.
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.
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.
Alec Stephenson <alec_stephenson@hotmail.com>
rFrechet
, rGenExtrVal
, rGumbel
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