Mathematical and statistical functions for the Erlang distribution, which is commonly used as a special case of the Gamma distribution when the shape parameter is an integer.

Value

Returns an R6 object inheriting from class SDistribution.

Details

The Erlang distribution parameterised with shape, \(\alpha\), and rate, \(\beta\), is defined by the pdf, $$f(x) = (\beta^\alpha)(x^{\alpha-1})(exp(-x\beta)) /(\alpha-1)!$$ for \(\alpha = 1,2,3,\ldots\) and \(\beta > 0\).

Distribution support

The distribution is supported on the Positive Reals.

Default Parameterisation

Erlang(shape = 1, rate = 1)

Omitted Methods

N/A

Also known as

N/A

References

McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.

Super classes

distr6::Distribution -> distr6::SDistribution -> Erlang

Public fields

name

Full name of distribution.

short_name

Short name of distribution for printing.

description

Brief description of the distribution.

alias

Alias of the distribution.

packages

Packages required to be installed in order to construct the distribution.

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage

Erlang$new(shape = NULL, rate = NULL, scale = NULL, decorators = NULL)

Arguments

shape

(integer(1))
Shape parameter, defined on the positive Naturals.

rate

(numeric(1))
Rate parameter of the distribution, defined on the positive Reals.

scale

numeric(1))
Scale parameter of the distribution, defined on the positive Reals. scale = 1/rate. If provided rate is ignored.

decorators

(character())
Decorators to add to the distribution during construction.


Method mean()

The arithmetic mean of a (discrete) probability distribution X is the expectation $$E_X(X) = \sum p_X(x)*x$$ with an integration analogue for continuous distributions.

Usage

Erlang$mean(...)

Arguments

...

Unused.


Method mode()

The mode of a probability distribution is the point at which the pdf is a local maximum, a distribution can be unimodal (one maximum) or multimodal (several maxima).

Usage

Erlang$mode(which = "all")

Arguments

which

(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all" returns all modes, otherwise specifies which mode to return.


Method variance()

The variance of a distribution is defined by the formula $$var_X = E[X^2] - E[X]^2$$ where \(E_X\) is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.

Usage

Erlang$variance(...)

Arguments

...

Unused.


Method skewness()

The skewness of a distribution is defined by the third standardised moment, $$sk_X = E_X[\frac{x - \mu}{\sigma}^3]$$ where \(E_X\) is the expectation of distribution X, \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation of the distribution.

Usage

Erlang$skewness(...)

Arguments

...

Unused.


Method kurtosis()

The kurtosis of a distribution is defined by the fourth standardised moment, $$k_X = E_X[\frac{x - \mu}{\sigma}^4]$$ where \(E_X\) is the expectation of distribution X, \(\mu\) is the mean of the distribution and \(\sigma\) is the standard deviation of the distribution. Excess Kurtosis is Kurtosis - 3.

Usage

Erlang$kurtosis(excess = TRUE, ...)

Arguments

excess

(logical(1))
If TRUE (default) excess kurtosis returned.

...

Unused.


Method entropy()

The entropy of a (discrete) distribution is defined by $$- \sum (f_X)log(f_X)$$ where \(f_X\) is the pdf of distribution X, with an integration analogue for continuous distributions.

Usage

Erlang$entropy(base = 2, ...)

Arguments

base

(integer(1))
Base of the entropy logarithm, default = 2 (Shannon entropy)

...

Unused.


Method mgf()

The moment generating function is defined by $$mgf_X(t) = E_X[exp(xt)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X.

Usage

Erlang$mgf(t, ...)

Arguments

t

(integer(1))
t integer to evaluate function at.

...

Unused.


Method cf()

The characteristic function is defined by $$cf_X(t) = E_X[exp(xti)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X.

Usage

Erlang$cf(t, ...)

Arguments

t

(integer(1))
t integer to evaluate function at.

...

Unused.


Method pgf()

The probability generating function is defined by $$pgf_X(z) = E_X[exp(z^x)]$$ where X is the distribution and \(E_X\) is the expectation of the distribution X.

Usage

Erlang$pgf(z, ...)

Arguments

z

(integer(1))
z integer to evaluate probability generating function at.

...

Unused.


Method clone()

The objects of this class are cloneable with this method.

Usage

Erlang$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.