shape and scale.dllogis(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pllogis(q, shape, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
qllogis(p, shape, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
rllogis(n, shape, rate = 1, scale = 1/rate)
mllogis(order, shape, rate = 1, scale = 1/rate)
levllogis(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, $P[X > x]$.dllogis gives the density,
pllogis gives the distribution function,
qllogis gives the quantile function,
rllogis generates random deviates,
mllogis gives the $k$th raw moment, and
levllogis gives the $k$th moment of the limited loss
variable. Invalid arguments will result in return value NaN, with a warning.
shape $=
\gamma$ and scale $= \theta$ has density:
$$f(x) = \frac{\gamma (x/\theta)^\gamma}{ x [1 + (x/\theta)^\gamma]^2}$$
for $x > 0$, $\gamma > 0$ and $\theta > 0$.The $k$th raw moment of the random variable $X$ is $E[X^k]$ and the $k$ limited moment at some limit $d$ is $E[\min(X, d)]$.
exp(dllogis(2, 3, 4, log = TRUE))
p <- (1:10)/10
pllogis(qllogis(p, 2, 3), 2, 3)
mllogis(1, 2, 3)
levllogis(10, 2, 3, order = 1)Run the code above in your browser using DataLab