Class: distribution.discrete.finite_set.class

Derived class for a generic discrete distribution on a finite set of points in (-Inf, Inf).

Super classes

mastiff::R6.class.class -> mastiff::distribution.abstract.class -> mastiff::distribution.discrete.class -> distribution.discrete.finite_set.class

Active bindings

interfaces

The list of available class interfaces

params

Named list of distribution parameters

support

The support of the distribution, i.e. the subset of values for which the density is positive,

mean

The mean of a point mass at $params$value.

sd

The standard deviation of a point mass at $params$value.

var

The variance of a point mass at $params$value.

Methods

Public methods

• distribution.discrete.finite_set.class$new()

• distribution.discrete.finite_set.class$d()

• distribution.discrete.finite_set.class$p()

• distribution.discrete.finite_set.class$q()

• distribution.discrete.finite_set.class$r()

• distribution.discrete.finite_set.class$clone()

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Method new()

Create a new object of class distribution.discrete.class

Usage

distribution.discrete.finite_set.class$new(support, prob)

Arguments

support

vector of values giving the points in the finite set on which the distribution has support.

prob

vector of probability weights for obtaining each element of support.

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Method d()

Density function for the probability distribution with mass $params$prob at points $support.

Usage

distribution.discrete.finite_set.class$d(x, log = FALSE)

Arguments

x

vector of quantiles.

log

logical; if TRUE, probabilities p are given as log(p).

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Method p()

Cumulative density function for the probability distribution with mass $params$prob at points $support.

Usage

distribution.discrete.finite_set.class$p(q, lower.tail = TRUE, log.p = FALSE)

Arguments

q

vector of quantiles.

lower.tail

logical; if TRUE (default), probabilities are \(P[ X \leq x ]\), otherwise, \(P[X>x]\).

log.p

logical; if TRUE, probabilities p are given as log(p).

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Method q()

Quantile function for the probability distribution with mass $params$prob at points $support.

Usage

distribution.discrete.finite_set.class$q(p, lower.tail = TRUE, log.p = FALSE)

Arguments

p

vector of probabilities.

lower.tail

logical; if TRUE (default), probabilities are \(P[ X \leq x ]\), otherwise, \(P[X>x]\).

log.p

logical; if TRUE, probabilities p are given as log(p).

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Method r()

Generates random deviates for probability distribution with mass $params$prob at points $support.

Usage

distribution.discrete.finite_set.class$r(n)

Arguments

n

number of observations. If length( n ) > 1, the length is taken to be the number required.

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Method clone()

The objects of this class are cloneable with this method.

Usage

distribution.discrete.finite_set.class$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.