Gamma | R Documentation |
Several important distributions are special cases of the Gamma
distribution. When the shape parameter is 1
, the Gamma is an
exponential distribution with parameter 1/\beta
. When the
shape = n/2
and rate = 1/2
, the Gamma is a equivalent to
a chi squared distribution with n degrees of freedom. Moreover, if
we have X_1
is Gamma(\alpha_1, \beta)
and
X_2
is Gamma(\alpha_2, \beta)
, a function of these two variables
of the form \frac{X_1}{X_1 + X_2}
Beta(\alpha_1, \alpha_2)
.
This last property frequently appears in another distributions, and it
has extensively been used in multivariate methods. More about the Gamma
distribution will be added soon.
Gamma(shape, rate = 1)
shape |
The shape parameter. Can be any positive number. |
rate |
The rate parameter. Can be any positive number. Defaults
to |
We recommend reading this documentation on https://alexpghayes.github.io/distributions3/, where the math will render with additional detail.
In the following, let X
be a Gamma random variable
with parameters
shape
= \alpha
and
rate
= \beta
.
Support: x \in (0, \infty)
Mean: \frac{\alpha}{\beta}
Variance: \frac{\alpha}{\beta^2}
Probability density function (p.m.f):
f(x) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{\alpha - 1} e^{-\beta x}
Cumulative distribution function (c.d.f):
f(x) = \frac{\Gamma(\alpha, \beta x)}{\Gamma{\alpha}}
Moment generating function (m.g.f):
E(e^{tX}) = \Big(\frac{\beta}{ \beta - t}\Big)^{\alpha}, \thinspace t < \beta
A Gamma
object.
Other continuous distributions:
Beta()
,
Cauchy()
,
ChiSquare()
,
Erlang()
,
Exponential()
,
FisherF()
,
Frechet()
,
GEV()
,
GP()
,
Gumbel()
,
LogNormal()
,
Logistic()
,
Normal()
,
RevWeibull()
,
StudentsT()
,
Tukey()
,
Uniform()
,
Weibull()
set.seed(27)
X <- Gamma(5, 2)
X
random(X, 10)
pdf(X, 2)
log_pdf(X, 2)
cdf(X, 4)
quantile(X, 0.7)
cdf(X, quantile(X, 0.7))
quantile(X, cdf(X, 7))
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