Class: distribution.continuous.beta.class

Derived class for a beta random variable.

Super classes

R6.class.class -> distribution.abstract.class -> distribution.continuous.class -> distribution.continuous.beta.class

Active bindings

interfaces

The list of available class interfaces

mean

the mean of the distribution

sd

the standard deviation of the distribution

var

the variance of the distribution

Methods

Public methods

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

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

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

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

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

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

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distribution.continuous.beta.class$new()

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

Usage

distribution.continuous.beta.class$new(alpha, beta)

Arguments

alpha

the alpha shape parameter of a beta distribution

beta

the beta shape parameter of a beta distribution

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distribution.continuous.beta.class$d()

density function for a beta random variable with shape parameters $params$alpha and $params$beta

Usage

distribution.continuous.beta.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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distribution.continuous.beta.class$p()

Cumulative density function for a beta random variable with shape parameters $params$alpha and $params$beta

Usage

distribution.continuous.beta.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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distribution.continuous.beta.class$q()

Quantile function for a beta random variable with shape parameters $params$alpha and $params$beta

Usage

distribution.continuous.beta.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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distribution.continuous.beta.class$r()

Generates random deviates for a beta random variable with shape parameters $params$alpha and $params$beta

Usage

distribution.continuous.beta.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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distribution.continuous.beta.class$clone()

The objects of this class are cloneable with this method.

Usage

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

Arguments

deep

Whether to make a deep clone.