DiscreteDistribution
Generating function "DiscreteDistribution"
Generates an object of class "DiscreteDistribution"
- Keywords
- distribution
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 toTRUE
. - .withSim
- normally not set by the user, but if determining the entries
supp
,prob
simulations were involved, you may set this toTRUE
. - .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
isFALSE
) - .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)
.
Details
If prob
is missing, all elements in supp
are equally weighted.
Typical usages are
DiscreteDistribution(supp)
Value
- Object of class
"DiscreteDistribution"
Note
Working with a computer, we use a finite interval as support which
carries at least mass 1-getdistrOption("TruncQuantile")
.
Also, we require that support points have distance at least
.DistrResoltion
, if this condition fails,
upon a suggestion by Jacob van Etten, .DistrCollapse
to
decide whether we use collapsing or not. If we do so, we collapse support
points if they are too close to each other, taking
the (left most) median among them as new support point which accumulates
all the mass of the collapsed points.
With .DistrCollapse==FALSE
, we at least collapse
points according to the result of unique()
, and if after this
collapsing, the minimal distance is less than .DistrResoltion
,
we throw an error. By .DistrCollapse.Unique.Warn
,
we control, whether we throw a warning upon collapsing or not.
concept
- discrete distribution
- generating function
See Also
DiscreteDistribution-class
AbscontDistribution-class
RtoDPQ.d
Examples
# 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)