InverseGamma | R Documentation |
Density function, distribution function, quantile function, random generation,
raw moments, and limited moments for the Inverse Gamma distribution
with parameters 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(t, shape, rate =1, scale = 1/rate, log =FALSE)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
shape, scale |
parameters. Must be strictly positive. |
rate |
an alternative way to specify the scale. |
log, log.p |
logical; if |
lower.tail |
logical; if |
order |
order of the moment. |
limit |
limit of the loss variable. |
t |
numeric vector. |
The inverse gamma distribution with parameters shape
=
\alpha
and scale
= \theta
has density:
f(x) = \frac{u^\alpha e^{-u}}{x \Gamma(\alpha)}, %
\quad u = \theta/x
for x > 0
, \alpha > 0
and \theta > 0
.
(Here \Gamma(\alpha)
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]
, k < \alpha
, and the k
th
limited moment at some limit d
is E[\min(X, d)^k]
, all k
.
The moment generating function is given by E[e^{tX}]
.
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,
levinvgamma
gives the k
th moment of the limited loss
variable, and
mgfinvgamma
gives the moment generating function in t
.
Invalid arguments will result in return value NaN
, with a warning.
levinvgamma
computes the limited expected value using
gammainc
from package expint.
Also known as the Vinci distribution. See also Kleiber and Kotz (2003) for alternative names and parametrizations.
The "distributions"
package vignette provides the
interrelations between the continuous size distributions in
actuar and the complete formulas underlying the above functions.
Vincent Goulet vincent.goulet@act.ulaval.ca and Mathieu Pigeon
Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.
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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