distrEx (version 2.9.5)

DiscreteMVDistribution-class: Discrete Multivariate Distributions

Description

The class of discrete multivariate distributions.

Objects from the Class

Objects can be created by calls of the form new("DiscreteMVDistribution", ...). More frequently they are created via the generating function DiscreteMVDistribution.

Slots

img

Object of class "rSpace". Image space of the distribution. Usually an object of class "EuclideanSpace".

param

Object of class "OptionalParameter". Optional parameter of the multivariate distribution.

r

Object of class "function": generates (pseudo-)random numbers

d

Object of class "OptionalFunction": optional density function

p

Object of class "OptionalFunction": optional cumulative distribution function

q

Object of class "OptionalFunction": optional quantile function

support

numeric matrix whose rows form the support of the distribution

.finSupport

logical: (later on to be) used internally to check whether the true support is finite; the element in the 1st row and ith column indicates whether the ith marginal distribution has a finite left endpoint, and the element in the 2nd row and ith column if it is has a finite right endpoint); not yet further used.

.withArith

logical: used internally to issue warnings as to interpretation of arithmetics

.withSim

logical: used internally to issue warnings as to accuracy

.logExact

logical: used internally to flag the case where there are explicit formulae for the log version of density, cdf, and quantile function

.lowerExact

logical: used internally to flag the case where there are explicit formulae for the lower tail version of cdf and quantile function

Extends

Class "MultivariateDistribution", directly.
Class "Distribution", by class "MultivariateDistribution".

Methods

support

signature(object = "DiscreteMVDistribution"): accessor function for slot support.

Author

Matthias Kohl

See Also

Distribution-class, MultivariateDistribution-class, DiscreteMVDistribution, E-methods

Examples

(D1 <- new("MultivariateDistribution")) # Dirac measure in (0,0)
r(D1)(5)

(D2 <- DiscreteMVDistribution(supp = matrix(c(1:5, rep(3, 5)), ncol=2, byrow=TRUE)))
support(D2)
r(D2)(10)
d(D2)(support(D2))
p(D2)(lower = c(1,1), upper = c(3,3))
q(D2)
## in RStudio or Jupyter IRKernel, use q.l(.)(.) instead of q(.)(.)
param(D2)
img(D2)

e1 <- E(D2) # expectation