Class: distribution.continuous.uniform.class

Derived class for an uniformly-distributed random variable on [min, max]

Super classes

mastiff::R6.class.class -> mastiff::distribution.abstract.class -> mastiff::distribution.continuous.class -> distribution.continuous.uniform.class

Active bindings

interfaces

The list of available class interfaces

mean

The mean of a uniform random variable on [min, max].

sd

The standard deviation of a uniform random variable on [min, max].

var

The variance of a uniform random variable on [min, max].

Methods

Public methods

• distribution.continuous.uniform.class$new()

• distribution.continuous.uniform.class$d()

• distribution.continuous.uniform.class$p()

• distribution.continuous.uniform.class$q()

• distribution.continuous.uniform.class$r()

• distribution.continuous.uniform.class$clone()

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Method new()

Create a new object of class distribution.continuous.exponential.class

Usage

distribution.continuous.uniform.class$new(min = 0, max = 1)

Arguments

min

The lower bound of the uniform distribution

max

The max bound of the uniform distribution

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Method d()

Density function for a uniform random variable on [min, max].

Usage

distribution.continuous.uniform.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 a uniform random variable on [min, max].

Usage

distribution.continuous.uniform.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 a uniform random variable on [min, max]. rate params$rate.

Usage

distribution.continuous.uniform.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 a uniform random variable on [min, max].

Usage

distribution.continuous.uniform.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.continuous.uniform.class$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.