DiscreteMVDistribution-class {distrEx} | R Documentation |

## 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(s)

Matthias Kohl Matthias.Kohl@stamats.de

### 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
```

[Package

*distrEx* version 2.9.2

Index]