distr (version 2.2.2)

DiscreteDistribution: Generating function "DiscreteDistribution"

Description

Generates an object of class "DiscreteDistribution"

Usage

DiscreteDistribution(supp, prob, .withArith=FALSE, .withSim=FALSE, 
                       .lowerExact = TRUE, .logExact = FALSE,
             .DistrCollapse = getdistrOption("DistrCollapse"),
             .DistrCollapse.Unique.Warn = 
                  getdistrOption("DistrCollapse.Unique.Warn"),
             .DistrResolution = getdistrOption("DistrResolution"),
             Symmetry = NoSymmetry())

Arguments

supp
numeric vector which forms the support of the discrete distribution.
prob
vector of probability weights for the elements of supp.
.withArith
normally not set by the user, but if determining the entries supp, prob distributional arithmetics was involved, you may set this to TRUE.
.withSim
normally not set by the user, but if determining the entries supp, prob simulations were involved, you may set this to TRUE.
.lowerExact
normally not set by the user: whether the lower.tail=FALSE part is calculated exactly, avoing a ``1-.''.
.logExact
normally not set by the user: whether in determining slots d,p,q, we make particular use of a logarithmic representation to enhance accuracy.
.DistrCollapse
controls whether in generating a new discrete distribution, support points closer together than .DistrResolution are collapsed.
.DistrCollapse.Unique.Warn
controls whether there is a warning whenever collapsing occurs or when two points are collapsed by a call to unique() (default behaviour if .DistrCollapse is FALSE)
.DistrResolution
minimal spacing between two mass points in a discrete distribution
Symmetry
you may help Rin calculations if you tell it whether the distribution is non-symmetric (default) or symmetric with respect to a center; in this case use Symmetry=SphericalSymmetry(center).

Value

  • Object of class "DiscreteDistribution"

concept

  • discrete distribution
  • generating function

Details

If prob is missing, all elements in supp are equally weighted. Typical usages are DiscreteDistribution(supp)

See Also

DiscreteDistribution-class AbscontDistribution-class RtoDPQ.d

Examples

Run this code
# Dirac-measure at 0
D1 <- DiscreteDistribution(supp = 0)
D1
# simple discrete distribution
D2 <- DiscreteDistribution(supp = c(1:5), prob = c(0.1, 0.2, 0.3, 0.2, 0.2))
D2

plot(D2)

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