The Gamma distribution

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.

dist_gamma(shape, rate = 1/scale, scale = 1/rate)

Arguments

shape, scale: shape and scale parameters. Must be positive, scale strictly.
rate: an alternative way to specify the scale.

Details

We recommend reading this documentation on pkgdown which renders math nicely. https://pkg.mitchelloharawild.com/distributional/reference/dist_gamma.html

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):

$$ E(e^{tX}) = \Big(\frac{\beta}{ \beta - t}\Big)^{\alpha}, \thinspace t < \beta $$

Examples

dist <- dist_gamma(shape = c(1,2,3,5,9,7.5,0.5), rate = c(0.5,0.5,0.5,1,2,1,1))

dist
#> <distribution[7]>
#> [1] Γ(1, 0.5) Γ(2, 0.5) Γ(3, 0.5) Γ(5, 1)   Γ(9, 2)   Γ(7.5, 1) Γ(0.5, 1)
mean(dist)
#> [1] 2.0 4.0 6.0 5.0 4.5 7.5 0.5
variance(dist)
#> [1]  4.00  8.00 12.00  5.00  2.25  7.50  0.50
skewness(dist)
#> [1] 2.0000000 1.4142136 1.1547005 0.8944272 0.6666667 0.7302967 2.8284271
kurtosis(dist)
#> [1]  6.0000000  3.0000000  2.0000000  1.2000000  0.6666667  0.8000000 12.0000000

generate(dist, 10)
#> [[1]]
#>  [1] 1.92844748 0.28476430 1.96941941 0.37799871 0.19960735 1.63124589
#>  [7] 2.69152950 3.42133263 3.36522354 0.06021366
#> 
#> [[2]]
#>  [1] 4.6919852 1.6774169 6.9300763 2.4075783 2.7981832 0.9606417 2.4928332
#>  [8] 3.5876883 4.7164778 3.2879543
#> 
#> [[3]]
#>  [1] 10.874383  3.349401  2.440180  9.581556  3.700975  2.776136  1.685701
#>  [8] 11.539812  3.171098 11.688644
#> 
#> [[4]]
#>  [1] 4.521361 8.021796 5.086454 5.397582 2.886631 3.136584 3.538219 2.895133
#>  [9] 2.788660 3.254850
#> 
#> [[5]]
#>  [1] 5.765367 4.046219 3.046654 3.874765 3.516896 6.806378 3.704758 4.776282
#>  [9] 6.596898 5.439818
#> 
#> [[6]]
#>  [1] 10.429745  6.642370 11.189164  8.225343  7.454479  7.458766  8.989552
#>  [8]  6.518382  5.594818 10.792694
#> 
#> [[7]]
#>  [1] 0.614291086 0.750182563 0.013971211 0.876081103 0.515207496 1.966032130
#>  [7] 0.026281564 0.336123303 0.005911172 0.001257043
#> 

density(dist, 2)
#> [1] 0.183939721 0.183939721 0.091969860 0.090223522 0.059540363 0.006545958
#> [7] 0.053990967
density(dist, 2, log = TRUE)
#> [1] -1.693147 -1.693147 -2.386294 -2.405465 -2.821101 -5.028908 -2.918939

cdf(dist, 4)
#> [1] 0.8646647 0.5939942 0.3233236 0.3711631 0.4074527 0.0762173 0.9953223

quantile(dist, 0.7)
#> [1] 2.4079456 4.8784330 7.2311353 5.8903613 5.1503385 8.6608472 0.5370971

Arguments

Details

See also

Examples