Distribution {rdecision}

A probability distribution

Description

An R6 class representing a (possibly multivariate) distribution.

Details

The base class for particular univariate or multivariate distributions.

Methods

Public methods

• Distribution$new()

• Distribution$order()

• Distribution$distribution()

• Distribution$mean()

• Distribution$mode()

• Distribution$SD()

• Distribution$varcov()

• Distribution$quantile()

• Distribution$sample()

• Distribution$r()

• Distribution$clone()

Method new()

Create an object of class Distribution.

Usage

Distribution$new(name, K = 1L)

Arguments

name

Name of the distribution ("Beta" etc.)

K

Order of the distribution (1 = univariate, 2 = bivariate etc.). Must be an integer; use 1L, 3L etc. to avoid an error.

Returns

An object of class Distribution.

Method order()

Order of the distribution

Usage

Distribution$order()

Returns

Order (K).

Method distribution()

Description of the uncertainty distribution.

Usage

Distribution$distribution()

Details

Includes the distribution name and its parameters.

Returns

Distribution name and parameters as character string.

Method mean()

Mean value of the distribution.

Usage

Distribution$mean()

Returns

Mean value as a numeric scalar (K = 1L) or vector of length K.

Method mode()

Return the mode of the distribution.

Usage

Distribution$mode()

Details

By default returns NA, which will be the case for most because an arbitrary distribution is not guaranteed to be unimodal.

Returns

Mode as a numeric scalar (K = 1L) or vector of length K.

Method SD()

Return the standard deviation of a univariate distribution.

Usage

Distribution$SD()

Details

Only defined for univariate (K = 1L) distributions; for multivariate distributions, function varcov returns the variance-covariance matrix.

Returns

Standard deviation as a numeric value.

Method varcov()

Variance-covariance matrix.

Usage

Distribution$varcov()

Returns

A positive definite symmetric matrix of size K by K, or a scalar for K = 1L, equal to the variance.

Method quantile()

Marginal quantiles of the distribution.

Usage

Distribution$quantile(probs)

Arguments

probs

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

Details

If they are defined, this function returns the marginal quantiles of the multivariate distribution; i.e. the quantiles of each univariate marginal distribution of the multivariate distribution. For example, the univariate marginal distributions of a multivariate normal are univariate normals, and the univariate marginal distributions of a Dirichlet distribution are Beta distributions. Note that these are not the true quantiles of a multivariate distribution, which are contours for K = 2L, surfaces for K = 3L, etc. For example, the 2.5% and 97.5% marginal quantiles of a bivariate normal distribution define a rectangle in x_1, x_2 space that will include more than 95% of the distribution, whereas the contour containing 95% of the distribution is an ellipse.

Returns

For K = 1L a numeric vector of length equal to the length of probs, with each entry labelled with the quantile. For K > 1L a matrix of numeric values with the number of rows equal to the length of probs, the number of columns equal to the order; rows are labelled with probabilities and columns with the dimension (1, 2, etc).

Method sample()

Draw and hold a random sample from the distribution.

Usage

Distribution$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

Void

Method r()

Return a random sample drawn from the distribution.

Usage

Distribution$r()

Details

Returns the sample generated at the last call to sample.

Returns

A vector of length K representing one sample.

Method clone()

The objects of this class are cloneable with this method.

Usage

Distribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Andrew J. Sims [e-mail]