Class: distribution.discrete.poisson.class

Derived class for an Poisson-distributed random variable.

Super classes

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

Active bindings

interfaces

The list of available class interfaces

mean

The mean of a Poisson distribution with mean $params$lambda.

sd

The standard deviation of a Poisson distribution with mean $params$lambda.

var

The variance of a Poisson distribution with mean $params$lambda.

Methods

Public methods

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

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

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

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

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

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

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Method new()

Create a new object of class distribution.discrete.class

Usage

distribution.discrete.poisson.class$new(lambda)

Arguments

lambda

vector of (non-negative) means.

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Method d()

Density function for a poisson random variable with size params$size and success probability params$prob.

Usage

distribution.discrete.poisson.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 poisson random variable with size params$size and success probability params$prob.

Usage

distribution.discrete.poisson.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 poisson random variable with size params$size and success probability params$prob.

Usage

distribution.discrete.poisson.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 poisson random variable with size params$size and success probability params$prob.

Usage

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

Arguments

deep

Whether to make a deep clone.