InverseWeibull {actuar}R Documentation

The Inverse Weibull Distribution

Description

Density function, distribution function, quantile function, random generation, raw moments and limited moments for the Inverse Weibull distribution with parameters shape and scale.

Usage

dinvweibull(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pinvweibull(q, shape, rate = 1, scale = 1/rate,
            lower.tail = TRUE, log.p = FALSE)
qinvweibull(p, shape, rate = 1, scale = 1/rate,
            lower.tail = TRUE, log.p = FALSE)
rinvweibull(n, shape, rate = 1, scale = 1/rate)
minvweibull(order, shape, rate = 1, scale = 1/rate)
levinvweibull(limit, shape, rate = 1, scale = 1/rate,
              order = 1)

Arguments

x, q vector of quantiles.
p vector of probabilities.
n number of observations. If length(n) > 1, the length is taken to be the number required.
shape, scale parameters. Must be strictly positive.
rate an alternative way to specify the scale.
log, log.p logical; if TRUE, probabilities/densities p are returned as log(p).
lower.tail logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].
order order of the moment.
limit limit of the loss variable.

Details

The Inverse Weibull distribution with parameters shape = a and scale = s has density:

f(x) = a (s/x)^a exp(-(s/x)^a)/x

for x > 0, a > 0 and s > 0.

The special case shape == 1 is an Inverse Exponential distribution.

The kth raw moment of the random variable X is E[X^k] and the kth limited moment at some limit d is E[min(X, d)^k].

Value

dinvweibull gives the density, pinvweibull gives the distribution function, qinvweibull gives the quantile function, rinvweibull generates random deviates, minvweibull gives the kth raw moment, and levinvweibull gives the kth moment of the limited loss variable.
Invalid arguments will result in return value NaN, with a warning.

Note

Distribution also knonw as the log-Gompertz.

Author(s)

Vincent Goulet vincent.goulet@act.ulaval.ca and Mathieu Pigeon

References

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2004), Loss Models, From Data to Decisions, Second Edition, Wiley.

Examples

exp(dinvweibull(2, 3, 4, log = TRUE))
p <- (1:10)/10
pinvweibull(qinvweibull(p, 2, 3), 2, 3)
mlgompertz(-1, 3, 3)
levinvweibull(10, 2, 3, order = 2)
[Package actuar version 1.0-2 Index]