shape and scale.
dinvgamma(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pinvgamma(q, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)
qinvgamma(p, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE)
rinvgamma(n, shape, rate = 1, scale = 1/rate)
minvgamma(order, shape, rate = 1, scale = 1/rate)
levinvgamma(limit, shape, rate = 1, scale = 1/rate, order = 1)
mgfinvgamma(x, shape, rate =1, scale = 1/rate, log =FALSE)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
$P[X <= x]$,="" otherwise,="" $p[x=""> x]$.dinvgamma gives the density,
pinvgamma gives the distribution function,
qinvgamma gives the quantile function,
rinvgamma generates random deviates,
minvgamma gives the $k$th raw moment, and
levinvgamma gives the $k$th moment of the limited loss
variable,
mgfinvgamma gives the moment generating function in x.Invalid arguments will result in return value NaN, with a warning.
shape $= a$ and scale $= s$ has density:
$$f(x) = \frac{u^\alpha e^{-u}}{x \Gamma(\alpha)}, %
\quad u = \theta/x$$
for $x > 0$, $a > 0$ and $s > 0$.
(Here $Gamma(a)$ is the function implemented
by R's gamma() and defined in its help.) 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 $E[min(X, d)^k]$.
The moment generating function is given by $E[e^{xX}]$.
exp(dinvgamma(2, 3, 4, log = TRUE))
p <- (1:10)/10
pinvgamma(qinvgamma(p, 2, 3), 2, 3)
minvgamma(-1, 2, 2) ^ 2
levinvgamma(10, 2, 2, order = 1)
mgfinvgamma(-1, 3, 2)
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