GammaDistribution {rdecision}

A parametrized Gamma distribution

Description

An R6 class representing a Gamma distribution.

Details

An object representing a Gamma distribution with hyperparameters shape (k) and scale (theta). In econometrics this parametrization is more common but in Bayesian statistics the shape (alpha) and rate (beta) parametrization is more usual. Note, however, that although Briggs et al (2006) use the shape, scale formulation, they use alpha, beta as parameter names. Inherits from class Distribution.

Super class

rdecision::Distribution -> GammaDistribution

Methods

Public methods

• GammaDistribution$new()

• GammaDistribution$distribution()

• GammaDistribution$mean()

• GammaDistribution$mode()

• GammaDistribution$SD()

• GammaDistribution$sample()

• GammaDistribution$quantile()

• GammaDistribution$clone()

Inherited methods

• rdecision::Distribution$order()
• rdecision::Distribution$r()
• rdecision::Distribution$varcov()

Method new()

Create an object of class GammaDistribution.

Usage

GammaDistribution$new(shape, scale)

Arguments

shape

shape parameter of the Gamma distribution.

scale

scale parameter of the Gamma distribution.

Returns

An object of class GammaDistribution.

Method distribution()

Accessor function for the name of the distribution.

Usage

GammaDistribution$distribution()

Returns

Distribution name as character string.

Method mean()

Return the expected value of the distribution.

Usage

GammaDistribution$mean()

Returns

Expected value as a numeric value.

Method mode()

Return the mode of the distribution (if shape >= 1)

Usage

GammaDistribution$mode()

Returns

mode as a numeric value.

Method SD()

Return the standard deviation of the distribution.

Usage

GammaDistribution$SD()

Returns

Standard deviation as a numeric value

Method sample()

Draw and hold a random sample from the distribution.

Usage

GammaDistribution$sample(expected = FALSE)

Arguments

expected

If TRUE, sets the next value retrieved by a call to r() to be the mean of the distribution.

Returns

Updated distribution.

Method quantile()

Return the quantiles of the Gamma uncertainty distribution.

Usage

GammaDistribution$quantile(probs)

Arguments

probs

Vector of probabilities, in range [0,1].

Returns

Vector of quantiles.

Method clone()

The objects of this class are cloneable with this method.

Usage

GammaDistribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Andrew J. Sims [e-mail]

References

Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. Oxford, UK: Oxford University Press; 2006.