shape and scale.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)length(n) > 1, the length is
taken to be the number required.TRUE, probabilities/densities
$p$ are returned as $\log(p)$.TRUE (default), probabilities are
$P[X \le x]$, otherwise, $P[X > x]$.dinvweibull gives the density,
pinvweibull gives the distribution function,
qinvweibull gives the quantile function,
rinvweibull generates random deviates,
minvweibull gives the $k$th raw moment, and
levinvweibull gives the $k$th moment of the limited loss
variable. Invalid arguments will result in return value NaN, with a warning.
shape $=
\tau$ and scale $= \theta$ has density:
$$f(x) = \frac{\tau (\theta/x)^\tau e^{-(\theta/x)^\tau}}{x}$$
for $x > 0$, $\tau > 0$ and $\theta > 0$. The special case shape == 1 is an
Inverse Exponential distribution.
The $k$th raw moment of the random variable $X$ is $E[X^k]$ and the $k$th limited moment at some limit $d$ is $E[\min(X, d)^k]$.
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)Run the code above in your browser using DataLab