rweibull {evd}

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

drweibull(x, loc=0, scale=1, shape=1, log = FALSE) 
prweibull(q, loc=0, scale=1, shape=1, lower.tail = TRUE) 
qrweibull(p, loc=0, scale=1, shape=1, lower.tail = TRUE)
rrweibull(n, loc=0, scale=1, shape=1)

dnweibull(x, loc=0, scale=1, shape=1, log = FALSE) 
pnweibull(q, loc=0, scale=1, shape=1, lower.tail = TRUE) 
qnweibull(p, loc=0, scale=1, shape=1, lower.tail = TRUE)
rnweibull(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 TRUE, the log density is returned.

lower.tail

Logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x]

Details

The reverse (or negative) Weibull distribution function with parameters \code{loc} = a, \code{scale} = b and \code{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

drweibull and dnweibull give the density function, prweibull and pnweibull give the distribution function, qrweibull and qnweibull give the quantile function, rrweibull and rnweibull 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.

See Also

rfrechet, rgev, rgumbel

Examples

drweibull(-5:-3, -1, 0.5, 0.8)
prweibull(-5:-3, -1, 0.5, 0.8)
qrweibull(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
rrweibull(6, -1, 0.5, 0.8)
p <- (1:9)/10
prweibull(qrweibull(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