DiracDistribution {rdecision}

A Dirac delta function

Description

An R6 class representing a Dirac Delta function.

Details

A distribution modelled by a Dirac delta function \delta(x-c) where c is the hyperparameter (value of the constant). It has probability 1 that the value will be equal to c and zero otherwise. The mode, mean, quantiles and random samples are all equal to c. It is acknowledged that there is debate over whether Dirac delta functions are true distributions, but the assumption makes little practical difference in this case. Inherits from class Distribution.

Super class

rdecision::Distribution -> DiracDistribution

Methods

Public methods

• DiracDistribution$new()

• DiracDistribution$distribution()

• DiracDistribution$mode()

• DiracDistribution$mean()

• DiracDistribution$SD()

• DiracDistribution$quantile()

• DiracDistribution$sample()

• DiracDistribution$clone()

Inherited methods

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

Method new()

Create a new Dirac Delta function distribution.

Usage

DiracDistribution$new(const)

Arguments

const

The value at which the distribution is centred.

Returns

A new DiracDistribution object.

Method distribution()

Accessor function for the name of the distribution.

Usage

DiracDistribution$distribution()

Returns

Distribution name as character string.

Method mode()

Return the mode of the distribution.

Usage

DiracDistribution$mode()

Returns

Numeric Value where the distribution is centered.

Method mean()

Return the expected value of the distribution.

Usage

DiracDistribution$mean()

Returns

Expected value as a numeric value.

Method SD()

Return the standard deviation of the distribution.

Usage

DiracDistribution$SD()

Returns

Standard deviation as a numeric value

Method quantile()

Quantiles of the distribution.

Usage

DiracDistribution$quantile(probs)

Arguments

probs

Numeric vector of probabilities, each in range [0,1].

Details

For a Dirac Delta Function all quantiles are returned as the value at which the distribution is centred.

Returns

Vector of numeric values of the same length as probs.

Method sample()

Draw and hold a random sample from the model variable.

Usage

DiracDistribution$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 clone()

The objects of this class are cloneable with this method.

Usage

DiracDistribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Andrew Sims [e-mail]