Class: distribution.multivariate.normal.class

Base class for multivariate distributions

Super classes

R6.class.class -> distribution.abstract.class -> distribution.multivariate.class -> distribution.multivariate.normal.class

Active bindings

interfaces

The list of available class interfaces.

mean

the means of each variable

sd

the standard deviation of each variable

var

the variance of variable

Methods

Public methods

• distribution.multivariate.normal.class$new()

• distribution.multivariate.normal.class$set_uniform_correlation()

• distribution.multivariate.normal.class$d()

• distribution.multivariate.normal.class$r()

• distribution.multivariate.normal.class$p()

• distribution.multivariate.normal.class$q()

• distribution.multivariate.normal.class$clone()

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distribution.multivariate.normal.class$new()

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

Usage

distribution.multivariate.normal.class$new(means, covariance)

Arguments

means

vector of means

covariance

the covariance matrix

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distribution.multivariate.normal.class$set_uniform_correlation()

Updates to the covariance matrix so that all off-diagonal correlations are the same

Usage

distribution.multivariate.normal.class$set_uniform_correlation(rho)

Arguments

rho

the single correlation between all variables

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distribution.multivariate.normal.class$d()

Density function for a multivariate normal

Usage

distribution.multivariate.normal.class$d(x, log = FALSE)

Arguments

x

matrix of varlues

log

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

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distribution.multivariate.normal.class$r()

Generates random deviates of a multivariate normal with rate params$rate.

Usage

distribution.multivariate.normal.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.multivariate.normal.class$p()

Univariate cumulative density functions

Usage

distribution.multivariate.normal.class$p(q, lower.tail = TRUE, log.p = FALSE)

Arguments

q

matrix of univariate 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.multivariate.normal.class$q()

Univariate quantile functions

Usage

distribution.multivariate.normal.class$q(p, lower.tail = TRUE, log.p = FALSE)

Arguments

p

matrix of univariate 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.multivariate.normal.class$clone()

The objects of this class are cloneable with this method.

Usage

distribution.multivariate.normal.class$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.